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Electronically monitored adherence to medications by newly diagnosed patients with juvenile rheumatoid arthritis.

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Arthritis & Rheumatism (Arthritis Care & Research)
Vol. 53, No. 6, December 15, 2005, pp 905–910
DOI 10.1002/art.21603
© 2005, American College of Rheumatology
ORIGINAL ARTICLE
Electronically Monitored Adherence to
Medications By Newly Diagnosed Patients With
Juvenile Rheumatoid Arthritis
MICHAEL A. RAPOFF, JOHN M. BELMONT, CAROL B. LINDSLEY,
AND
NANCY Y. OLSON
Objective. To describe patterns of adherence to nonsteroidal antiinflammatory drugs (NSAIDs) in newly diagnosed
patients with juvenile rheumatoid arthritis (JRA), and to examine demographic and disease-related variables as potential
predictors of adherence.
Methods. Adherence to NSAIDs was monitored in 48 children with JRA (mean age 8.6 years) over 28 consecutive days
using an electronic monitoring device. Measures of disease activity (active joint counts, morning stiffness) and demographics (age, sex, ethnicity, socioeconomic status) were also obtained.
Results. Using an 80% adherence cut point, 25 (52%) patients were classified as adherent and 23 (48%) as nonadherent.
There was considerable variability across patients, with full adherence (taking all doses on time) ranging from 0 to 100%
of the monitored days. Logistic regression showed that active joint count and socioeconomic status were the only
significant predictors. Both were positively related to adherence. The model correctly classified 70.5% of patients as either
adherent or nonadherent (Cox and Snell R2 ⴝ 0.295, P ⴝ 0.0005).
Conclusion. Children newly diagnosed with JRA are more likely to adhere to an NSAID regimen if they have a greater
number of active joints or their families have higher socioeconomic status. The former finding suggests that children’s
adherence is symptom-driven, while the latter suggests that families of lower socioeconomic status deserve special
attention to address adherence issues.
KEY WORDS. Adherence; Juvenile rheumatoid arthritis; Nonsteroidal antiinflammatory drugs; Electronic monitoring.
INTRODUCTION
Juvenile rheumatoid arthritis (JRA) is the most common
pediatric rheumatic disease and a major cause of shortand long-term disability. Medical treatment objectives for
JRA include reduction of pain and inflammation, preservation of function, and prevention of joint deformities and
destruction. Comprehensive treatment of JRA includes
pharmacotherapy (frequently nonsteroidal antiinflammatory drugs [NSAIDs]), physical and occupational therapy,
and psychosocial support (1). Children with JRA and their
parents are required to adhere consistently and over a
Dr. Rapoff is supported by a grant from Maternal and
Child Health (MCJ-200617).
Michael A. Rapoff, PhD, John M. Belmont, PhD, Carol B.
Lindsley, MD, Nancy Y. Olson, MD: University of Kansas
Medical Center, Kansas City.
Address correspondence to Michael A. Rapoff, PhD, University of Kansas Medical Center, Department of Pediatrics,
3901 Rainbow Boulevard, Kansas City, KS 66160-7330. Email: mrapoff@kumc.edu.
Submitted for publication May 25, 2005; accepted in revised form July 21, 2005.
long period of time to medication regimens, such as daily
NSAIDs, whose benefits may be delayed even though
adverse side effects such as gastrointestinal irritation
are immediate. This constellation of factors associated
with JRA and its treatment is predictive of greater adherence problems to medical regimens in pediatric chronic
disease (2).
There are few studies that have specifically addressed
adherence to treatment regimens for JRA. Two retrospective studies by Litt and colleagues found that 55% of
children and adolescents with JRA were adherent to salicylate medications as determined by serum assays (3,4). In
3 separate within-subject design studies involving 5 patients with JRA (ages 3 to 14 years), baseline adherence
with medications was assessed by parental observations or
pill counts and ranged from 38 –59% (5–7). Therefore,
adherence to medications for JRA can vary widely across
different patient samples and across methods of assessing
adherence, but appears to be similar to the average adherence levels (50 –55%) found with medications for other
chronic pediatric diseases (2). Although optimal levels of
adherence for most regimens have not been established,
these average adherence levels could compromise the ther905
906
apeutic benefits of medical regimens for JRA and increase
unnecessary health care costs (8).
Because not all patients have problems with adherence,
discovering factors that predict poor adherence would be
useful for identifying those patients at risk who might
benefit from special attention to adherence issues. Moreover, factors that predict adherence could be used to improve the internal validity of clinical studies by stratifying
patients on relevant dimensions (e.g., age, sex, socioeconomic status) before randomly assigning them to intervention or control groups. Few studies have examined predictors of adherence to medication regimens for JRA. Litt and
Cuskey (3) found that lower adherence to salicylate medications among 38 adolescents with JRA was associated
with younger age at disease onset, longer disease duration,
shorter duration of subspecialty care, greater delay between disease onset and first subspecialty clinic visit, and
fewer clinic visits. Litt et al (4) found that higher selfesteem and autonomy (assuming more responsibility for
health care in the context of a positive relationship with
parents) were predictive of higher adherence to salicylate
medications among a separate group of 38 adolescents
with JRA. Chaney and Peterson (9) discovered that family
relationship and stress variables predicted adherence to
medications among 18 children and adolescents with JRA.
A recent Finnish study found that self-reported adherence
of adolescents with JRA was predicted by motivation, fear
of acute problems, support from nurses, energy and willpower, and threat to social well being (10). These studies
provide useful data on potential predictors of adherence to
medication regimens for JRA, but they are limited by having assessed adherence using periodic drug assays (which
measure adherence over short time periods) or self-report
(which tends to overestimate adherence) (2).
The most important advance in adherence assessment is
the electronic monitor that detects actions necessary to
administer medications, such as removing the cap from a
pill bottle. These monitors store downloadable time- and
date-stamped information in real time for up to several
months. They can provide precise information on the frequency and timing of presumptive dosing and reveal daily
patterns of adherence, such as full adherence (taking all
daily doses), partial adherence (taking some but not all
daily doses), and drug holidays (2 or more consecutive
days without dosing). However, electronic monitors can
only indicate that a maneuver consistent with dosing has
been executed. It is assumed that the patient actually ingests the medication close to the time the monitored action
is performed and that the monitored container is the only
one in use by the patient (11). In spite of these potential
limitations, electronic monitors have been proposed as the
new “gold standard” in adherence measurement (12). To
our knowledge, only 2 studies on adherence to NSAIDs
have used electronic monitors. One involved patients with
ankylosing spondylitis (13), and the other targeted a subset
of patients in the current study who participated in an
adherence intervention trial (14). The latter involved a
28-day preintervention baseline period, which is the focus
of the current study. In addition to the use of electronic
monitoring, the current study is unique in its focus on
newly diagnosed patients with JRA.
Rapoff et al
The aims of this study were to describe patterns of
adherence to NSAIDs (full, partial, or no adherence and
drug holidays) among newly diagnosed patients with JRA
during a 28-day period using an electronic monitoring
device, and to examine demographic and disease-related
variables as potential predictors of adherence.
PATIENTS AND METHODS
Patients. Patients included children and adolescents
who were diagnosed with JRA within the past year according to established standards (15) and had been on an
unvarying NSAID regimen for at least 1 month. Recruitment occurred over a 42-month period. The parents of 90
patients were sent recruitment letters. Thirty-six (40%)
declined to participate because of time constraints. The
remaining 54 agreed to participate, but 6 had incomplete
adherence data (3 because of device malfunctions, 1 from
failure of the patient to bring in the device for downloading, and 2 from dropping out during baseline). The final
sample consisted of 48 participants. The age range of the
participants was 2.3–16.7 years (mean ⫾ SD 8.6 ⫾ 4.0),
71% were female, and 90% were white.
Measures. Adherence. Adherence was assessed using
the Medication Event Monitoring System (MEMS; Aprex
Corporation, Fremont, CA). This electronic medication
bottle cap records the date and time of each bottle opening.
It can record 1,800 openings and has an 18-month battery
life. MEMS data are downloaded to a portable computer
and analyzed using the manufacturer’s software, which
provides daily and continuous data on adherence. Patients
were asked to use the MEMS for their primary NSAID,
which we targeted because the regimen for most patients
(75%) involved only a single NSAID, and over 80% of
those were instructed to take it twice daily. Patients and
their parents were informed about the purpose of the
MEMS cap.
The daily MEMS adherence score was the percent of
prescribed doses taken within the recommended dosing
interval, with a ⫾2-hour forgiveness interval (e.g., NSAID
twice daily, could be taken 10 to 14 hours between doses).
Extra bottle-cap openings were not considered in calculating adherence, because they were infrequent and reportedly resulted from filling the pill vial. Each participant
yielded 28 contiguous days of data.
Demographics. The parents’ demographic questionnaire yielded the child’s age (dates converted to decimal
years), sex (male/female), ethnicity (white/nonwhite),
mother’s marital status (married/not married), mother’s
and father’s education levels (high school/some college/
college graduate/graduate school), and the parents’ occupations. The Hollingshead Four Factor Index of Social
Status (16) was computed as the measure of socioeconomic status (SES), including the whole score (continuously variable from 8 – 66) and the 5 social strata (ranging
from unskilled to major professional).
Disease-related variables. The participant’s JRA subtype (pauciarticular, polyarticular, or systemic onset) and
regimen complexity (1 NSAID alone versus at least 2 med-
Adherence to Medications By Patients With JRA
907
ications) were recorded from the medical record. During
routine clinical examination the rheumatologist recorded
standard clinical indices of disease activity, including
number of active joints (those with pain, swelling, and
limitation of motion) and the number of minutes of morning stiffness.
Procedure. Patients were given the MEMS cap to fit over
the vial used to dispense their primary NSAID. They were
asked to dispense medications only from this vial and not
to use any other vials or pill reminder containers to dispense their primary NSAID. Demographic data were obtained at the beginning of the 28-day MEMS monitoring
period; disease activity measures were obtained from the
medical record of the participant’s last clinic visit prior to
the first day of MEMS use (median delay ⫽ 5.5 weeks,
mean ⫾ SD 7.5 ⫾ 6.7 weeks). Informed permission/assent
forms were approved by the Institutional Review Board.
Statistical analysis. For each participant, each day was
defined as full adherence (all prescribed doses taken on
time ⫾ 2-hour forgiveness interval), partial adherence
(some but not all taken on time), and no adherence (no
doses taken at all). A drug holiday was defined as 2 or
more consecutive days with no doses taken preceded and
followed by at least 1 day with at least partial adherence.
The means, medians, and dispersions of the number of
days fitting these classifications were calculated and plotted to provide descriptive data on the patterns of adherence.
To examine demographic and disease-related variables
as potential predictors of adherence, patients were labeled
adherent if they demonstrated full adherence on at least
80% of the 28 monitored days. This 80% adherence cut
point is now a convention, which originated in early studies on adherence to antihypertensive medications (2). The
analysis aimed to develop a model for prediction of adherence. Potential demographic and disease-related predictors were screened using univariate tests of the group
difference (adherent versus nonadherent) at a liberal ␣ ⫽
0.2 for entry. Continuous variables were tested using t-test,
or Mann-Whitney U test as appropriate; categorical variables were tested using Fisher’s exact test, chi-square test,
or linear-by-linear chi-square test as appropriate. All variables that met the screening criterion were entered into a
preliminary forward stepwise logistic regression model
with the adherence group as the dependent variable. Using
␣ ⫽ 0.05 for entry, variables identified as significant predictors were then entered into a final logistic regression
model. All statistics were computed using SPSS software,
version 11.5 (SPSS, Chicago, IL).
RESULTS
Descriptive statistics and univariate analyses. Using
median levels, patients showed full adherence on 70% of
the monitored days, partial adherence on 14%, and no
adherence on 7% of the monitored days (Figure 1). There
was considerable variability across patients, with full adherence ranging from 0 to 100% of the days. Seventy-nine
Figure 1. For each of 48 patients with newly diagnosed juvenile
rheumatoid arthritis, each of the 28 monitored days was classified
as having full, partial, or no adherence to their nonsteroidal antiinflammatory drug regimens. Ranges, quartiles, means, and medians (mdn) for the percentage of monitored days falling into each
classification are shown. max ⫽ maximum; min ⫽ minimum.
percent of patients took no drug holidays, 13% took 1, and
the remaining 8% of the patients took between 2 and 4.
The 80% adherence cut point yielded 25 (52%) adherent
and 23 (48%) nonadherent patients. Table 1 shows the
results for the demographic and disease activity variables
for the entire sample and for the 2 adherence groups. The
initial screen for candidate predictors showed that the
adherence groups did not differ (P ⬎ 0.20) on age, sex,
mother’s marital status, father’s education, participant’s
JRA subtype, duration of morning stiffness, or complexity
of NSAID regimen. The groups did differ, however, on 4
variables: the adherent group had a higher concentration
of white patients (P ⫽ 0.180), their mothers had more
education (P ⫽ 0.052) and had a higher Hollingshead
Index (SES) (P ⫽ 0.001). The adherent group also had a
higher active joint count (P ⫽ 0.016).
Multivariate model. Of the 48 patients, 4 did not have
joint count recorded in their medical record on the visit
prior to the electronic monitoring period. The remaining
44 had complete data sets, and these were included in the
multivariate model. The preliminary stepwise logistic regression analysis admitted 2 of the 4 candidate predictors:
the active joint count and SES. Entering joint count first,
65.9% of the patients were correctly classified as adherent/
nonadherent (Cox and Snell R2 ⫽ 0.158, P ⫽ 0.006). Adding SES to the prediction equation, the final logistic model
correctly classified 70.5% of the patients (Cox and Snell
R2 ⫽ 0.295, P ⫽ 0.0005). This model had 74% sensitivity,
67% specificity, 71% positive predictive value, and 70%
negative predictive value.
908
Rapoff et al
Table 1. Demographic and disease-related variables for 48 newly diagnosed children with juvenile rheumatoid arthritis (JRA)
and for subsamples*
Variable
Age, mean ⫾ SD years
Sex, % female
Ethnicity, % white
Mother’s marital status, % married
Mother’s education, %
High school
Some college
College graduate
Graduate school
Father’s education, %
High school
Some college
College graduate
Graduate school
Hollingshead Index, no. ⫾ SD
JRA subtype, %
Pauciarticular
Polyarticular
Systemic onset
Active joint count, no ⫾ SD
AM stiffness, minutes ⫾ SD
Regimen of 1 NSAID only, %
Total sample
(n ⴝ 48)
Nonadherent
(n ⴝ 23)
Adherent
(n ⴝ 25)
8.6 ⫾ 4.0
71
90
77
8.4 ⫾ 4.4
78
83
70
8.8 ⫾ 3.7
64
96
84
35
21
35
8
48
22
26
4
24
20
44
12
41
20
22
17
45 ⫾ 13
52
14
19
14
39 ⫾ 13
32
24
24
20
51 ⫾ 9
33
52
15
1.3 ⫾ 2.6
43 ⫾ 47
75
35
57
9
0.4 ⫾ 0.9
40 ⫾ 50
83
32
48
20
2.2 ⫾ 3.2
46 ⫾ 44
68
Statistical
test
P†
t-test
␹2
Exact
␹2
L-b-L ␹2
0.717
0.278
0.180‡
0.235
0.052‡
L-b-L ␹2
0.282
t-test
␹2
0.001‡
0.537
Mann-Whitney U
t-test
␹2
0.016‡
0.701
0.243
* Nonadherent ⫽ ⬍80% of nonsteroidal antiinflammatory drug (NSAID) doses taken on time; adherent ⫽ 80% of NSAID doses taken on time; L-b-L
␹2 ⫽ linear x linear ␹2 for ordered categories; exact ⫽ Fisher’s test (used because expected frequencies ⬍5).
† Differences between the subsamples.
‡ Variables that were P ⱕ 0.20 and were then entered into the regression analysis.
DISCUSSION
The overall adherence rate in this study (25 [52%] of 48
patients) is consistent with previous studies in the adult
and pediatric literatures, including a wide range of disease
regimens and methods of measuring adherence (2). This
adherence rate is based on classifying as adherent all patients who take ⱖ80% of their prescribed doses. This
cutoff score is a convention in the adherence literature (2),
which apparently had its origin in early studies on adherence to antihypertensive medications. It was reported that
patients who took ⱖ80% of their medications had better
blood pressure control than those who took ⬍80% (17). A
recent study of children with human immunodeficiency
virus (HIV) (18) also supports the 80% cutoff score. In this
study children with detectable viral loads (a measure of
active disease) were ⬍80% adherent to highly active antiretroviral therapy (18).
Our descriptive information can also be compared with
previous studies using electronic monitoring. In our study,
full adherence occurred on a median of 70% of the monitored days, which is close to the 78% full adherence
found in patients with ankylosing spondylitis (the only
other study using MEMS to assess NSAID adherence) (13).
In contrast, full adherence as assessed by electronic monitoring has been much lower in other pediatric studies. For
example, full adherence to inhaled steroids or bronchodilator medications for children with asthma has ranged
from 5 to 50% of monitored days (19,20). Days with no
detectable adherence were quite low in our study, occur-
ring on only 7% of the days, compared with 19% of the
monitored days in the other NSAID study using the MEMS
(13). In contrast, studies on children with asthma have
reported a higher percentage of days with no adherence to
inhaled medications (range 20 – 48%) (19,21,22). One
might expect better adherence to NSAIDs than to inhaled
medications for asthma because it may be easier to swallow a pill than to use a metered dose inhaler. Moreover,
NSAID therapy may provide more immediate symptom
relief than inhaled medications for asthma, particularly
inhaled steroids, which are preventive and therefore do
not provide immediate symptom relief. Another possible
reason for more days with full adherence and fewer with
no adherence in our study is that patients were newly
diagnosed with JRA. Shorter disease duration has been
associated with better adherence to chronic disease regimens (2).
The multivariate model in our study showed that a
higher active joint count prior to monitoring of adherence
and higher SES were significantly associated with being
classified as adherent to NSAID. The active joint count
finding contradicts pediatric studies that reported greater
disease activity to be associated with lower adherence to
medications for HIV (18), renal disease (23), and seizures
(24). However, these studies were cross-sectional, and it is
possible that lower adherence produced increased disease
activity. In our study, active joint counts were obtained
prior to the adherence-monitoring period. It appears that
children with more active joints, including pain as a major
Adherence to Medications By Patients With JRA
symptom, were more likely to take their NSAID to relieve
their symptoms. An applied behavior analytic perspective
would suggest that taking NSAIDs would be negatively
reinforced; that is, taking medications more consistently
would provide relief or escape from aversive symptoms
such as pain (2).
The most robust predictor of adherence to NSAIDs in
this study was SES, with patients from lower SES strata
being more likely to be classified as nonadherent. This is
in agreement with a number of pediatric studies showing
that lower SES predicts lower adherence to regimens for
asthma, cystic fibrosis, diabetes, and renal disease (23,25–
27). Lower SES has also been found to be a significant risk
factor for poorer health, mental health, and cognitive/
academic outcomes for children (28,29). Most studies utilize income, education, and occupation to represent SES
(28). We used the Hollingshead Index, which combines
caregivers’ educational level and type of occupation. The
connection between SES and adherence to medical regimens may be explained by a number of potential mechanisms. Lower education levels among lower SES caregivers may make it more difficult for them to understand and
accommodate prescribed regimens (30). Part of the difficulty in accommodating or integrating regimens into their
daily lives may originate in lower SES families’ experiencing increased stress and reduced social support (28). Also,
they may have fewer financial resources, which may make
it difficult for them to fill or renew prescriptions in a
timely fashion. Given all of these potential factors, one
might expect that lower SES families in particular would
have a more difficult time adhering to NSAIDs at times
when their children are asymptomatic, as there are other
stressors and demands that would take precedence. In our
study we found that no low-SES patient was adherent
when joint count was zero, whereas 61% of high-SES
patients were adherent when joint count was zero (␹2 ⫽
7.67, with Yates’ correction, P ⫽ 0.006); when joint count
was ⬎0, the respective figures were 50% and 83% (␹2 ⫽
0.444, P ⫽ 0.505).
There are several limitations to this study. It was cross
sectional and correlational, and it utilized a convenience
sample of newly diagnosed children with JRA who were
participating in a randomized trial to prevent an anticipated drop in adherence over time (14). The sample was
rather small, and there were other potentially important
predictors, such as self-efficacy or barriers to adherence,
that were not considered. In addition, we could only speculate about potential mechanisms that link SES with adherence, for we did not assess such factors as stressors in
the family and financial resources.
In spite of these limitations, this is one of only 2 studies
that monitored adherence to NSAIDs among children with
JRA using electronic monitoring, which is considered the
gold standard in adherence assessment (12). Unlike all
other assessment methods, electronic monitors can capture highly specific daily medication events such as full,
partial, and no dosing, as well as drug holidays. Future
studies would benefit from periodic assays to confirm that
medications removed from the MEMS container are ingested. Further research is also needed using biologic criteria for defining optimal cutoff points for adherence/non-
909
adherence, the target being the level of adherence
necessary to achieve an acceptable therapeutic response
(31). Clearly, this would need to be done on a disease-bydisease, regimen-by-regimen basis. Longitudinal studies
are also needed to predict adherence over time, with repeated measures of adherence, disease activity, and psychosocial predictors that might vary over time (such as
family stressors, self-efficacy, and barriers to adherence).
The results of this study suggest that lower SES families
deserve special attention to address nonadherence to
NSAIDs in the treatment of JRA. Educational and behavioral interventions to improve adherence must address the
unique challenges and lack of practical and psychological
resources among these families.
REFERENCES
1. Cassidy JT, Petty RE. Textbook of pediatric rheumatology. 5th
ed. New York: Elsevier; 2005.
2. Rapoff MA. Adherence to pediatric medical regimens. New
York: Kluwer/Plenum; 1999.
3. Litt IF, Cuskey WR. Compliance with salicylate therapy in
adolescents with juvenile rheumatoid arthritis. Am J Dis
Child 1981;135:434 – 6.
4. Litt IF, Cuskey WR, Rosenberg A. Role of self-esteem and
autonomy in determining medication compliance among adolescents with juvenile rheumatoid arthritis. Pediatrics 1982;
69:15–7.
5. Rapoff MA, Lindsley CB, Christophersen ER. Improving compliance with medical regimens: case study with juvenile rheumatoid arthritis. Arch Phys Med Rehabil 1984;65:267–9.
6. Rapoff MA, Purviance MR, Lindsley CB. Improving medication compliance for juvenile rheumatoid arthritis and its effect on clinical outcome: a single-subject analysis. Arthritis
Care Res 1988;1:12– 6.
7. Rapoff MA, Purviance MR, Lindsley CB. Educational and
behavioral strategies for improving medication compliance in
juvenile rheumatoid arthritis. Arch Phys Med Rehabil 1988;
69:439 – 41.
8. Rapoff MA. Compliance with treatment regimens for pediatric
rheumatic diseases. Arthritis Care Res 1989;2:S40 –7.
9. Chaney JM, Peterson L. Family variables and disease management in juvenile rheumatoid arthritis. J Pediatr Psychol 1989;
14:389 – 403.
10. Kyngas H. Motivation as a crucial predictor of good compliance in adolescents with rheumatoid arthritis. Int J Nurs Pract
2002;8:336 – 41.
11. Urquhart J, de Klerk E. Contending paradigms for the interpretation of data on patient compliance with therapeutic drug
regimens. Stat Med 1998;17:251– 67.
12. Cramer JA. Microelectronic systems for monitoring and enhancing patient compliance with medication regimens. Drugs
1995;49:321–7.
13. De Klerk E, van der Linden SJ. Compliance monitoring of
NSAID drug therapy in ankylosing spondylitis, experiences
with an electronic monitoring device. Br J Rheumatol 1996;
35:60 –5.
14. Rapoff MA, Belmont J, Lindsley C, Olson N, Morris J, Padur J.
Prevention of nonadherence to nonsteroidal anti-inflammatory medications for newly diagnosed patients with juvenile
rheumatoid arthritis. Health Psychol 2002;21:620 –3.
15. Brewer EJ Jr, Bass J, Baum J, Cassidy JT, Fink C, Jacobs J, et al.
Current proposed revision of JRA criteria. Arthritis Rheum
1997;20 Suppl 2:195–9.
16. Hollingshead AB. Four factor index of social status. New
Haven (CT): Yale University; 1975.
17. Sackett DL, Snow JC. The magnitude of compliance and noncompliance. In: Haynes RB, Taylor DW, Sackett DL, editors.
Compliance in health care. Baltimore: The Johns Hopkins
University Press; 1979. p. 11–22.
910
18. Farley J, Hines S, Musk A, Ferrus S, Tepper V. Assessment of
adherence to antiviral therapy in HIV-infected children using
the Medication Event Monitoring System, pharmacy refill,
provider assessment, caregiver self-report, and appointment
keeping. J Acquir Immune Defic Syndr 2003;33:211– 8.
19. Bender B, Milgrom H, Rand C, Ackerson L. Psychological
factors associated with medication nonadherence in asthmatic children. J Asthma 1998;35:347–53.
20. Gibson NA, Ferguson AE, Aitchison TC, Paton JY. Compliance with inhaled asthma medication in preschool children.
Thorax 1995;50:1274 –9.
21. Bender B, Wamboldt FS, O’Connor SL, Rand C, Szefler S,
Milgrom H, et al. Measurement of children’s asthma medication adherence by self report, mother report, canister weight,
and Doser CT. Ann Allergy Asthma Immunol 2000;85:416 –
21.
22. Milgrom H, Bender B, Ackerson L, Bowry P, Smith B, Rand C.
Noncompliance and treatment failure in children with
asthma. J Allergy Clin Immunol 1996;98:1051–7.
23. Brownbridge G, Fielding DM. Psychosocial adjustment and
adherence to dialysis treatment regimes. Pediatr Nephrol
1994;8:744 –9.
24. Hazzard A, Hutchinson SJ, Krawiecki N. Factors related to
Rapoff et al
25.
26.
27.
28.
29.
30.
31.
adherence to medication regimens in pediatric seizure patients. J Pediatr Psychol 1990;15:543–55.
Bobrow ES, AvRuskin TW, Siller J. Mother-daughter interaction and adherence to diabetes regimens. Diabetes Care 1985;
8:146 –51.
Patterson JM. Critical factors affecting family compliance
with home treatment for children with cystic fibrosis. Fam
Relations 1985;34:79 – 89.
Radius SM, Becker MH, Rosenstock IM, Drachman RH, Schuberth KC, Teets KC. Factors influencing mothers’ compliance
with a medication regimen for asthmatic children. J Asthma
Res 1978;15:133– 49.
Bradley RH, Corwyn RF. Socioeconomic status and child
development. Annu Rev Psychol 2002;53:371–99.
Chen E, Matthews KA, Boyce WT. Socioeconomic differences
in children’s health: how and why do these relationships
change with age? Psychol Bull 2002;128:295–329.
Goldman DP, Smith JP. Can patient self-management help
explain the SES health gradient? Proc Natl Acad Sci U S A
2002;99:10929 –34.
Gordis L. Conceptual and methodologic problems in measuring patient compliance. In: Haynes RB, Taylor DW, Sackett
DL, editors. Compliance in health care. Baltimore: The Johns
Hopkins University Press; 1979. p. 23– 45.
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