· web viewwe searched the cochrane library, embase, cinahl, ipa and pubmed (until december...
TRANSCRIPT
Hospital Admissions Associated with Medication Non-adherence: A Systematic
Review of Prospective Observational Studies
Running header: Hospital Admissions and Medication Non-adherence
Word count: 3112 words (excluding title page, abstract, references, figures and tables)
Number of references: 67 references
Number of figures: 1 figure
Number of tables: 4 tables
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Abstract
Background: Medication non-adherence in ambulatory care has received
substantial attention in the literature, but less so as it affects acute care. Accordingly,
we aimed to estimate the frequency with which non-adherence to medication
contributes to hospital admissions.
Methods: We searched the Cochrane library, EMBASE, CINAHL, IPA and PUBMED
(until December 2017) to identify prospective observational studies which examined
prevalence rates of hospital admissions associated with medication non-adherence.
A quality assessment was performed using an expanded Crombie checklist. Data
extraction covered patterns, circumstances, and patient and other key characteristics
of non-adherence. Pooled estimates were obtained using a random-effect model.
Results: Of 24 included studies, 8 were undertaken in North America, 7 from
Europe, 6 from Asia, and 3 from Australia. Most studies (79%) were rated as low
risks of bias. All but three studies used combination measures to detect non-
adherence, but approaches to assess preventability varied considerably. Across the
studies, high heterogeneity between prevalence estimates was identified (χ2=548;
d.f. 23; p<0.001; I2=95.8%). The median prevalence rate of hospital admissions
associated with non-adherence was 4.29% (interquartile range 3.22-7.49%) with
prevalence rates ranging from 0.72% to 10.79%. By definition, almost all of these
admissions were considered preventable. The underlying causes contributing to
these admissions included medication cost and side-effects, and non-adherence
most often involved cardiovascular medicines.
Conclusions: Hospital admissions associated with non-adherence to medication is
a common problem. This systematic review highlights important targets for
intervention. Greater attention could be focused on adherence to medication during
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the hospital stay as part of an enhanced medication reconciliation process.
Standardisation in study methods and definitions is needed to allow future
comparisons amongst settings; future studies should also encompass emerging
economies.
Keywords: Adverse event, epidemiology and detection; hospital medicine;
medication safety; patient safety; adherence.
INTRODUCTION
Adherence is defined as “the extent to which a person’s behavior, (taking
medication, following a diet, and/or executing lifestyle changes) corresponds with
agreed recommendations from a health care provider”.1 However, non-adherence to
medication is common among patients with long-term conditions which can
negatively affect their health outcomes. Previous studies have suggested that 25-
90% of non-adhering patients experienced treatment failure and for some leading to
hospitalization.2-4 This accounts for significant health care costs, estimated as
exceeding $100 billion annually to the US economy.5 6
Several studies have examined the prevalence and nature of hospital
admissions associated with medication-related problems.7 8 Around 5-10% of hospital
admissions are thought to arise from such problems,9 10 often due to adverse drug
reactions or adverse drug events.8 11 However, far less is known about the role of
medication non-adherence that leads to hospital admissions, and the associated risk
factors. To our knowledge, no systematic review has yet quantified the prevalence
of hospital admissions that are a consequence of non-adherence to medications.
Therefore, this systematic review and meta-analysis of prospective observational
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studies aimed to determine these aforesaid prevalence estimates and the nature of
medication non-adherence leading to hospital admissions.
METHODS
This systematic review and meta-analysis was conducted in accordance with
the PRISMA guidelines12 and registered with PROSPERO (registration number:
CRD42017083688).
Study Outcome
The prevalence of hospital admissions associated with non-adherence to
medications was defined as our primary outcome.
Data Sources and Study Selection
Inclusion criteria: The following criteria were used for including studies in our
systematic review: (i) studies were prospective and observational, and provided
sufficient data to calculate the prevalence of hospital admissions associated with
medication non-adherence; (ii) patients could be admitted to any hospital
department, including admission via emergency departments; and (iii) there were no
restrictions on the definition of (non)-adherence used in the studies.
Exclusion criteria: We excluded studies that investigated the prevalence of hospital
admissions arising from non-adherence to specific medications or for specific
diseases. Case reports, case series, editorials, and review articles were also
excluded.
Search strategy: We searched the following bibliographic databases from their
inception dates until December 2017: Cochrane library, EMBASE, CINAHL,
International Pharmaceutical Abstracts (IPA) and PUBMED. The search strategy
included the following keywords and their synonyms: (“patients” OR “human”) AND
(“drug-related problem” OR “adverse drug events” OR “non-adherence”) AND
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(“incidence” OR “prevalence”). The literature retrieval was supplemented by
manually searching the reference list of all identified articles. There were no
language restrictions.
Screening process: Eligible titles/abstracts and full-text articles were screened by
two independent investigators (P.M. and C.K.). Inter-reviewer agreement for study
selection was assessed using the Cohen’s kappa statistic. Any disagreements were
resolved through discussion.
Data extraction and methodological quality assessment
Studies meeting the eligibility criteria were extracted independently by two
investigators (P.M. and C.K.) using a pre-designed extraction form. The following
information was extracted: country of study, study setting, study year, study period,
population, participant ages, percentage of males, definitions of medication non-
adherence, method for detecting medication non-adherence, implicated medications
classified according to the British National Formulary (BNF) classification system,13
causality assessment, preventability, reasons for medication non-adherence, and
prevalence rates for hospital admission related to medication non-adherence.
Reasons for medication non-adherence that led to hospital admissions were
classified into 3 groups: patient-related factors, healthcare professional-related
factors, and healthcare system-related factors. We also contacted authors when
primary outcome data was missing. If the authors did not respond, the study was
excluded. Inter-reviewer agreement for extracting prevalence rates was assessed
using the Cohen’s kappa statistic and disagreements were resolved by discussion.
Two investigators (P.M. and C.K.) independently appraised the risk of bias for
the included studies using Crombie’s checklist which is applicable for cross-
sectional/prevalence studies.14 The operationalisation of the Crombie tool,
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specifically for prevalence studies is provided in eTable1. Each item was scored 1
point for ‘yes’, 0.5 points for ‘unclear’, and a 0 point for ‘no’. Studies were then
classified as having high risk of bias if the summary score was 0 to <4 points,
moderate risk of bias (4 to <7 points), and low risk of bias (7 to 9). Inter-reviewer
agreement for quality assessment was assessed by the Cohen’s kappa statistic and
disagreements resolved by discussion.
Data analyses
The prevalence of hospital admissions associated with non-adherence was
calculated as the number of patients who had medication non-adherence that
required hospital admission (the numerator) divided by number of patients admitted
to hospital during the study period for any medical cause (the denominator). Pooled-
effect estimates for the prevalence rate of hospital admissions associated with
medication non-adherence across the included studies with corresponding 95%
confidence intervals (95% CI) were calculated using the DerSimonian-Laird random-
effects model, assuming that the true effect size varies between studies.15 To assess
heterogeneity of prevalence rates among studies, we used standard χ2 tests, and the
I2 statistic. If high heterogeneity was indicated (I275%), the results across studies
were summarized using the median rate and interquartile range (IQR). To explore
possible sources of heterogeneity, subgroup analyses were performed by study
population (children, all-age group, and elderly), geographical region (North America,
Europe, Asia and Australia), and method of detection (combination vs single
measures), to investigate the impact on the prevalence rates of hospital admission
associated with medication non-adherence. In addition, heterogeneity was also
explored in a univariate random-effects meta-regression. The following variables
were included: publication year, study population, geographical region, and method
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of detection [(i) an interview method, (ii) a combination of medical record review and
drug level analysis, (iii) a combination of medical record review, drug level analysis
and interview methods, (iv) medical record review only, (v) a combination of medical
record review and interview methods, (vi) a combination of medical record review,
interview, and pill count methods]. A funnel plot was used to investigate any
evidence of publication bias. We also tested for funnel asymmetry using the Begg’s
test, Egger’s tests, and the trim-and-fill method (all p<0.05).16-18 Statistical tests were
2-sided and used a significance threshold of p<0.05. All analyses were performed
using STATA software version14.1 (StataCorp, College Station, TX, USA).19
RESULTS
Search results
From all sources, 17432 articles were identified. After removing duplicates,
17010 articles remained. Of these, 16978 were removed because they: (i) did not
meet the inclusion criteria after screening (16719 articles); (ii) were review articles
(225); (iii) were case reports (28); or (iv) were not related to humans (6). Seven
additional articles were identified by hand-searching. The remaining 39 full-text
articles were assessed for eligibility and 15 articles were excluded because they
focused on adverse drug reactions (7 articles); patients were not admitted to
hospitals (3 articles); had insufficient data to calculate prevalence rates of non-
adherence to medications (4 articles); and were related to specific medications (1
article). In all, 24 studies 2 20-42 were included in the systematic review and meta-
analysis (figure 1A). The inter-reviewer agreement was deemed good for full-text
screening (Cohen’s kappa value = 0.72) and very good for data extraction on
prevalence rates (Cohen’s kappa value = 1.0).
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All the included studies (n=24) were prospective and observational that
investigated hospital admissions related to medication non-adherence. They were
conducted in North America (8 articles), Europe (7), Asia (6), and Australia (3).
Three studies37 40 41 (12.5%) were multi-center. In 21 studies2 20-23 25-30 32-40 42(87.5%)
employing combination measures, four different approaches for detection of non-
adherence were identified; (i) a combined review of medical records and blood drug
concentrations, (ii) a combined review of medical records, drug concentrations, and
interview, (iii) a combined review of medical records and interview, and (iv) a
combined review of medical records, interview, and pill count. In 18 studies that
reported definitions of medication non-adherence, four studies22 33 37 39 used the
Haynes definition,43 one40 applied the WHO definition,1 one38 used Hepler and
Strand,44 and the remaining studies created their own definitions. For the type of
population, 17 studies20-22 24 25 27 29-31 34-40 42 were conducted on general populations, six
in the elderly,2 26 28 32 33 41 and one in a pediatric population23 (Table 1). Eleven studies2
25-28 32 33 36 38 41 42 reported the mean number of medications prescribed to each patient,
which ranged between 2.525 and 9.0.42 Two studies26 32 merely reported that patients
admitted to hospital had at least 2-4 medical conditions/person. Many patients (14-
45%) were living alone.2 33 41 In addition, four studies (16%) reported that more than
50% of the patients had not completed education to high school level or above.2 33 38
40
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Table 1 Characteristics of 24 included studies
Author (Year) Country
Ward admitted,
Study settingStudy period Population Mean age,
% maleDefinition of medication (non)
adherenceMethod of detection
HA rate due to NA
(%)Type of non-
adherence (%)HA rate due to NA among pt
with MRP
Medication classes involved
(%)
Quality assessment
McKenny, et al. (1976)20
United States
General medical ward, Teaching hospital
2 mo General population
56.1% of population aged 50 yr or over
Patients admit to taking less than the prescribed number of doses of a given medication during month prior to hospital admission
Interview and medical record review
23/216 (10.65)
NR 23/59(38.98)
CVS drugs (39.1), endocrine drugs
(26.1), CNS drugs (21.7), respiratory
drugs (8.7), infections(4.3)
Moderate risk of bias
Stewart, et al.(1980)21
United States
Inpatient Psychiatric Unit
6 mo General population
33.9 yr, 40% male
Hospital admission was related to patient's not following directions for prescribed medications
Interview, medical record review and drug level analysis
5/60 (8.33) NR 5/25(20.00)
CNS drugs(80), endocrine drugs
(20)
Moderate risk of bias
Bergman, et al. (1981)22
Sweden Medical ward, University hospital
3.5 mo General population
59±19 yr (range 16-97 yr), 49.5% male
Haynes definition43 as the extent to which the patient behavior coincided with clinical prescription
Interview, medical record review and drug level analysis
21/285 (7.37)
UD (11/21), OD (10/21)
21/45(46.67)
NR Low risk of bias
YosselsonSuperstine, et al. (1982)23
Israel Paediatric ward, University hospital
7 mo Children(0-16 yr)
NR NR Interview and medical record review
31/906 (3.42)
Discontinued medication (10/31), UD (11/31),OD (6/31), mixed type(4/31)
31/160(19.38)
Infection drugs (64.5), CNS drugs (22.6), CVS drugs
(3.2)
Moderate risk of bias
Bigby, et al. (1987)24
United States
Emergency admissions, Teaching hospital
12 mo General population
60.7±18.8 yr, 36% male
Patient ability to comply with prescribed therapies
Interview of patients and primary care clinicians
26/686 (3.79)
NR 26/73(35.62)
NR Low risk of bias
Davidsen, et al. (1988)25
Denmark Department of Cardiology, University Hospital
2 mo General population
non-adhering patients with (F:74.9±9.7 y M: 73.3±5.7 y), 50% male
A deviation of more than 50% between the dose actually taken and prescribed drug dose.
Interview and medical record review
16/426 (3.76)
NR 16/65(24.62)
CVS drugs (56.3), respiratory drugs (6.3), other drugs
(37.5)
Low risk of bias
Grymonpre, et al. (1988)26
Canada Department of Medicine, Tertiary hospital
4 mo Elderly(50 yr)
69.8±0.5 yr, 57.4% male
A failure to accomplish the goals of treatment because of deliberate nonadherence to a therapeutic program
Interview, medical record review and pill count
26/863 (3.01)
NR 26/162(16.05)
NR Low risk of bias
Col, et al. (1990)2
United States
Medical ward, Community teaching hospital
3 mo Elderly(65 yr)
76.6 yr (range 65-99 yr), 45.4% male
Any nontrivial deviation from the prescribed medication regimen.
Interview and medical record review
34/315 (10.79)
UD (81%), OD (17%), misuse (2%), inten-tional(54%) unintentional(46%)
34/89(38.20)
CVS drugs (63.9), respiratory drugs (30.5), endocrine
drugs (5.6)
Low risk of bias
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197
Author (Year) Country
Ward admitted,
Study settingStudy period Population Mean age,
% maleDefinition of medication (non)
adherenceMethod of detection
HA rate due to NA
(%)Type of non-
adherence (%)HA rate due to NA among pt
with MRP
Medication classes involved
(%)
Quality assessment
Stanton, et al. (1994)27
Australia Medical ward, Teaching hospital
10 weeks
General population
Median age 67 yr (range 11-97 yr), 50.8% male
Deviation from prescribed medication regimen due to non-comprehension, forgetfulness or by choice, producing an exacerbation of symptoms of the patient's condition
Interview and medical record review
10/691 (1.45)
NR 10/68(14.71)
Respiratory drugs (60), CVS drugs
(40)
Low risk of bias
Courtman, et al. (1995)28
Canada Medical ward, Tertiary teaching hospital
139 days
Elderly(65 yr)
78 yr (range 65-108 yr), 41.3% male
NR Medical record review and drug level analysis
9/150 (6.00)
NR 9/46(19.57)
NR Low risk of bias
Dartnell, et al. (1996)29
Australia Royal Melbourne hospital, Teaching hospital
1 mo General population
Aged 15-91 yr
Patient or carer described drug taking that deviated from prescribed directions; or the patient’s mental condition or home situation together with the presenting condition made non-compliance highly likely; or drug-assay determinations concurred with a doctor’s suspicion of non-compliance.
Interview and medical record review
15/965 (1.55)
UD (12/15), OD (3/15)
15/55(27.27)
CVS drugs (53.3), respiratory drugs (33.3), endocrine (6.7), CNS drugs
(6.7)
Low risk of bias
Nelson, et al. (1996)30
United States
Intensive care unit or internal medicine service, University hospital
1 mo General population
Median age in drug-related admission = 43 yr, 60% male
NR Medical record review and drug level analysis
48/450 (10.76)
NR 48/73(65.75)
NR Low risk of bias
Murad, et al. (1997)31
Bahrain Medical ward, Medical Center
1 mo General population
NR NR Medical record review
206/2167 (9.51)
NR 206/523(39.39)
NR Moderate risk of bias
Chan, et al. (2001)32
Australia Medical ward, Public acute care hospital
2 mo Elderly (≥ 75 yr)
81.8 yr (range 75-94 yr), 45% male
A deviation from a prescribed medication regimen due to non-comprehension, forgetfulness or by choice, producing exacerbation of symptoms of the patient's condition
Interview and medical record review
9/240 (3.75)
NR 9/73(12.33)
NR Low risk of bias
Malhotra, et al. (2001)33
India Medical emergency department, Tertiary hospital
7 mo Elderly(65 yr)
72.5±4.7 yr (range 65-91 yr), 47.1% male
Haynes definition43 as extent to which the patients behavior coincides with the clinical prescription
Interview and medical record review
44/578 (7.61)
UD (71% of all non-compliance), OD (17%), misuse (2%), intentional (63%), unintentional (37%)
44/83(53.01)
CVS drugs(61.4), respiratory
drugs(18.2), endocrine
drugs(11.4), and CNS drugs(9.1)
Low risk of bias
Martin, et al. (2002)34
Spain Admissions through the emergency department
9 mo General population
68.4 yr, 58.9% male
Patients did not comply with the prescribed regimen
Interview and medical record review
91/1661 (5.48)
NR 91/215(42.33)
CVS drugs (48.4), Respiratory drugs (24.2), GI drugs
(15.4), CNS drugs (4.4), Endocrine
Low risk of bias
10
Author (Year) Country
Ward admitted,
Study settingStudy period Population Mean age,
% maleDefinition of medication (non)
adherenceMethod of detection
HA rate due to NA
(%)Type of non-
adherence (%)HA rate due to NA among pt
with MRP
Medication classes involved
(%)
Quality assessment
drugs (3.3), Infection drugs (2.2), UTI (1.1),
Nutrition & blood(1.1)
Otero Lopez, et al. (2006)35
Spain Medical units, University hospital
6 mo General population
NR NR Interview and medical record review
19/2643 (0.72)
NR 42/177(23.73)
Respiratory, CVS, CNS, & endocrine
drugs
Low risk of bias
Samoy, et al. (2006)36
Canada Internal medicine,Teach-ing hospital
12 weeks
General population
69.3±18.8 yr, 49.4% male
Any noxious, unintended, or undesired effect caused by failure to receive a drug.
Interview and medical record review
22/565 (3.89)
NR 22/136(16.18)
NR Low risk of bias
Kongkaew . (2009)37
United Kingdom
Two tertiary hospitals
18 mo General population
OD group= 38.49±16.75 yr, 44%male, UD group= 58±20.8 yr, 57.1%male
Haynes definition43 as the extent to which the patient’s behaviour (in terms of taking medication, following diets or executing other life-style changes) coincides with the clinical prescription’
Interview and medical record review
190/3904 (4.87)
OD (148/190), UD (42/190)
190/604(31.46)
OD: analgesic, CNS drugs, UD:
CNS drugs, endocrine
Low risk of bias
Singh, et al. (2011)38
India Internal medicine, Tertiary hospital
6 mo General population
49.8±18.2 yr, 60% male
DRP classifications by Hepler and Strand44
Interview and medical record review
55/3560 (1.54)
NR 55/118(46.61)
NR Low risk of bias
Al-Arifi, et al. (2014)39
Saudi Arabia
Admission via emergency department, Tertiary hospital
1 mo General population
Median age 51 yr, 53.33% male
Haynes definition43 as the extent to which the patient’s drug taking behavior (in terms of taking medication) coincides with the prescription
Interview, medical record review and drug level analysis
17/251 (6.77)
NR 17/52(32.69)
NR Moderate risk of bias
Kongkaew, et al. (2015)40
Thailand Inpatient units of university hospital, general hospital and community hospitals
16 mo General population
56.7±17.4 yr, 50.1% male
WHO definition1 as the extent to which a person’s behaviour – taking medication, following a diet, and/or executing lifestyle changes, corresponds with agreed recommendations from a health care provider
Interview and medical record review
32/3755 (0.85)
OD (11/32),UD (21/32)
32/91(35.16)
NR Low risk of bias
Gustafsson M, et al. (2016)41
Sweden Acute internal medicine ward and the orthopedic ward at university hospital, and county hospital
3 yr Elderly(65 yr)
Among drug-related group: mean age 82.4 yr, 39.7% male
A deviation from the prescribed medications because of a choice, noncomprehension or forgetfulness leading to an ADR or exacerbation of symptoms
Medical record review
19/458 (4.15)
NR 20/189(10.58)
Endocrine, CVS, Respiratory, CNS,
Injection
Low risk of bias
Jolivot, et al. (2016)42
France Medical ICU, Teaching hospital
12 mo General population
Median age 65 yr, 57.5% male
NR Interview and medical record review
31/701 (4.42)
NR 31/173(17.92)
NR Low risk of bias
Abbreviations; NR: not reported, NA: Non-adherence, ICU: intensive care unit, OD: overdosage, UD: underdosage, HA: hospital admission, WHO: World Health Organization, MRP: medication-related problem; CVS: cardiovascular system, CNS: central nervous system; GI: gastrointestinal, UTI: urinary tract infection, DRP:drug related problem.
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Quality assessment and publication bias
The Crombie’s tool for quality assessment yielded scores ranging from 4 to 9.
Nineteen studies2 22 24-30 32-38 40-42 were classified as low risk of bias, five20 21 23 31 39 were
moderate risk of bias, and none was classified as high risk of bias. Here, the
agreement for each item ranged from 0.78 – 1.00. However, when making an overall
risk of bias judgement according to the range of summary scores, the agreement
between reviewers was judged as very good (Cohen’s kappa = 1.0). (Supplementary
data, eTable 2). Six studies2 26 32 34-36 met all 9 domains on critical appraisal of
prevalence studies. Most studies clearly stated the aims of the study, employed an
appropriate design to meet the objectives, and adequately described the data in
terms of method of participant selection, study location, and study duration.
Seventeen studies (70.8%) reported statistical methods used for data analysis.
Twenty-one studies (87.5%) used combination methods for measuring medication
non-adherence. Sixteen studies (66.7%) employed a method to evaluate the causal
relationship between admissions and non-adherence (Supplementary data, eTable
2).
No evidence of publication bias was detected by Begg’s test (p=0.36) and
Egger’s test (0.066). The findings were not different after calibrating publication bias
by performing the trim-and-fill method. The corresponding funnel plot is displayed in
eFigure1.
Prevalence of hospital admissions associated with medication non-adherence
The 24 studies included 26,496 patients of whom 999 experienced non-
adherence to medications. The crude prevalence rates of hospital admissions
associated with non-adherence varied from 0.7% to 10.8%. Given the high
heterogeneity (χ2=548; d.f. 23; p<0.001; I2=95.8 %), the prevalence rate of hospital
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admissions associated with non-adherence was reported as a median of 4.3% (IQR
3.2-7.5%). All 24 studies also reported the number of patients admitted to hospital
due to medication-related problems (3354 patients). Of these admissions, 29.4% (as
a median prevalence rate, IQR=17.0-39.2%) were associated with medication non-
adherence (heterogeneity: χ2=2761; d.f. 23; p <0.001; I2=99.2%).
The evaluation of causality between hospital admissions and medication non-
adherence was carried out in 16 studies2 22 24-27 29 30 32-37 40 41 based on explicit criteria
and/or the judgement of reviewers. The non-adherence was classified as causal
non-adherence if the causality was rated as definite/probable in 10 studies.2 22 24 27 30
35-37 40 41 The most common explicit criteria used to assess causality was the Hallas
criteria45 employed in 6 studies,27 28 30 32 34 37 followed by the WHO criteria46 47 (2
studies25 41), the Karch-Lasagna algorithm48 (2 studies29 35), and the Bergman and
Wiholm algorithm22 (2 studies22 26). The remaining criteria2 33 49 of included studies are
described in Table 2.
Eleven studies24 28 29 32 34 36-40 42 (45.8%) estimated the preventability of hospital
admissions associated with medication non-adherence giving an overall median
preventable rate of 100% (ranging from 29.5 to 100%). The criteria used28-30 44 45 50-52
and how preventability were judged are given in Table 2 and supplement data
eTable 3.
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Table 2. Causality and preventability of hospital admission associated with medication non-adherenceAuthor (Year) Causality criteria Causality
judgementausality Causal relationship Preventabilitycriteria
Preventability judgement
% Preventability
McKenny, et al. (1976)20 NR NR NR NR NR NR
Stewart, et al. (1980)21 NR NR NR NR NR NR
Bergman, et al. (1981)22 Algorithm of Bergman and Wiholm22 By researchers Definite or probable:
(21/21; 100%) NR NR NR
Yosselson-Superstine, et al. (1982)23 NR NR NR NR NR NR
Bigby, et al. (1987)24 No explicit criteriaBy 3 reviewers and consensus (at least two ‘yes’ judgements)
Definite (26/26; 100%) No explicit criteriaBy 3 reviewers and consensus (at least 2 ‘yes’ judgements)
19/26 (73.1)
Davidsen, et al. (1988)25 WHO criteria46 By research physician Definite, probable or possible (16/16; 100%) NR NR NR
Grymonpre, et al. (1988)26 Algorithm of Bergman and Wiholm22
By attending physicians and house staff. NR for non-adherence NR NR NR
Col, et al. (1990)2 Col2 By two senior medical residents
Definite or probable (11/34; 32.35%), possible (13/34; 38.24%), contributing factor (10/34; 29.41%),
NR NR NR
Stanton, et al. (1994)27 Hallas45By an attending medical officer and a panel of four of the authors
Definite (6/10; 60%), probable (4/10; 40%) NR NR NR
Courtman, et al. (1995)28 NR NR NR Courtman and Stallings28
By pharmacy residents 9/9 (100)
Dartnell, et al. (1996)29 Karch and Lasagna48By at least 2 authors and discrepancy was resolved by all authors.
NR Dartnell29By at least 2 authors discrepancy resolved by all.
15/15 (100)
Nelson, et al. (1996)30 Modified Hallas45 By investigators Definite or probable(48/48; 100%) Nelson andTalbert30 By investigators NR
Murad, et al. (1997)31 NR NR NR NR NR NR
Chan, et al. (2001)32 Hallas45 By investigators and training doctors NR Hallas preventability45 By investigators and
trainee doctors 9/9 (100)
Malhotra, et al. (2001)33 Malhotra33 By one of investigator NR NR NR NR
Martin, et al. (2002)34 Hallas45 By investigators Definite, probable or possible (91/91; 100%)
Schmock and Thornton50 By investigators 91/91 (100)
Otero Lopez, et al. (2006)35 Modified algorithm of Karch-Lasagna48 By investigators Definite or probable (19/19;
100%)Schmock and Thornton50 By investigators NR
Samoy, et al. (2006)36 Using explicit predefined approach
By 3 reviewers and consensus. Report only DRP result Zed51 and Forster52 By 3 reviewers and
consensus. 22/22 (100)
Kongkaew. (2009)37 Hallas45,amended Howard49
By 3 reviewers and consensus.
Definite or probable (190/190; 100%) Hepler and Strand44 By 3 reviewers and
consensus. 56/190 (29.47)
Singh, et al. (2011)38 NR NR NR Zed51 and Forster52 By investigators 55/55 (100)
Al-Arifi, et al. (2014)39 NR NR NR Nelson and Talbert30 By investigators 17/17 (100)
Kongkaew, et al. (2015)40 Hallas45, amended Howard49
By 3 reviewers and consensus.
Causal: overuse (11/32; 34%), underuse (21/32; 66%)
Hepler and Strand44 By 3 reviewers and consensus. 32/32 (100)
Gustafsson, et al. (2016)41 Using WHO criteria47 Using explicit criteriaDefinite (7/19; 36.8%), probable (5/19; 26.3%), possible (7/19; 36.8%)
NR NR NR
Jolivot, et al. (2016)42 NR NR NR Schumock and Thornton50
Judged by investigators 31/31 (100)
Abbreviations: NR=not reported
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Subgroup analyses
In subgroup analyses, the median prevalence of hospital admission for the
elderly was 5.1% (IQR 3.8-7.6%) and that for all-age patients was 4.4% (IQR 1.6-
7.4%), while the mean prevelance for pediatric patients was 3.4% (95%CI, 2.2-
4.6%).
Geographically, 8 studies originated from North America2 20 21 24 26 28 30 36 where
the median prevalence was highest (7.2%, IQR 3.8-10.7%), six were from Asia23 31 33
38-40 (median prevelance = 5.1%, IQR 1.5-7.6%), 7 from Europe22 25 34 35 37 41 42 (median
prevelance = 4.4%, IQR 3.8-5.5%), while the lowest prevalence rate was from the 3
Australian studies27 29 32 [(pooled mean 1.7%; 95% CI, 0.9-2.5%) with a moderate
degree of heterogeneity (I2=37.2%, p=0.20)] (Table 3).
Detection using combination measurements yielded a median prevalence of
4.4% (IQR; 3.0-7.4%, and a similar value when single measures were employed
(4.2%; 95% CI, 3.8-9.5%). Fourteen different definitions of non-adherence were
identified. Four studies22 33 39 found prevalence estimates of 6.4% (95% CI; 4.6-8.1%,
p=0.035, I2=65) (Figure 1B) by using the Haynes definition43 which was the first to be
introduced into medicine.53
Meta-regression
Geographic regions and method of detection were related to admission rates
while age-group and year of publication were unrelated (Supplementary data, eTable
4).
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Table 3. Subgroup analysis according to population, continent and method of detection
No. of studies
Prevalence estimate, %(95% CI)
Heterogeneity testΧ2 d.f. p-value I2
PopulationElderly 6 Median 5.08 (IQR; 3.75–7.61) 28.55 5 <0.001 82.5%General population 17 Median 4.42 (IQR; 1.55–7.37) 468.83 16 <0.001 96.6%Pediatric population 1 3.42 (2.24–4.60) NA NA NA NA
ContinentNorth America 8 Median 7.17 (IQR; 3.84–10.66) 49.3 7 <0.001 85.8%Asia 6 Median 5.10 (IQR; 1.54–7.61) 231.98 5 <0.001 97.8%Europe 7 Median 4.42 (IQR; 3.76–5.48) 199.67 6 <0.001 97.0%Australia 3 1.72 (0.93-2.52) 3.19 2 0.203 37.2%
Method of detectionCombination methods 21 Median 4.42 (IQR; 3.01-7.37) 383.70 20 <0.001 94.8%
A combination of medical record review and drug level analyses
2 8.51 (3.95-13.07) 3.71 1 0.054 73%
A combination of medical record review, drug level analysis and interview
3 7.19 (5.11-9.26) 0.19 2 0.911 0%
A combination of medical record review and interview
15 Median 3.76 (IQR; 1.54-5.48) 306.99 14 <0.001 95.4%
A combination of medical record review, interview and pill count
1 3.01 (1.87-4.15) NA NA NA NA
Single method 3 Median 4.15 (IQR; 3.79-9.51) 42.99 2 <0.001 95.3% Interview only 1 3.79 (2.36-5.22) NA NA NA NA Medical record review only 2 Median 6.83 (IQR; 4.15-9.51) 22.71 1 <0.001 95.8%
Abbreviations: CI=confident interval, IQR=interquartile range, NA=not applicable
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Reasons and risk factors for medication non-adherence
Five studies2 23 25 32 33 identified the causes of medication non-adherence by
conducting an interview. The reported reasons for the medication non-adherence
were classified into 3 categories i) patient-related, ii) healthcare professional-related,
and iii) health care system-related (shown in Table 4).
Two studies2 33 identified risk factors for hospital admissions associated with
non-adherence were: poor recall of the medication regimen,2 33 multiple consulting
physicians,2 33 female gender,2 33 medium income ($10,000 - $15,000 per year)
compared with those on public assistance (Medicaid),2 and a greater number of
medications prescribed.2 33
Medication classes involving non-adherence
Twelve (48%) studies2 20 21 23 25 27 29 33-35 37 41 reported the medication class
associated with hospital admissions due to medication non-adherence. Those most
commonly involved targeted the cardiovascular system [50.9% (IQR; 39.6-58.9%)]
(n=8),2 20 23 25 27 29 33 34 respiratory system [24.2% (IQR; 8.7-33.3%)] (n=7),2 20 25 27 29 33 34
central nervous system [15.4% (IQR; 6.7-22.6%)] (n=6),20 21 23 29 33 34 endocrine system
[9.1% (IQR; 5.6-20)] (n=6),2 20 21 29 33 34 and medication used to treat infections [4.3%
(IQR; 2.2-64.5%)] (n=3).20 23 34 Other medication classes reported were analgesics,
gastrointestinal drugs, haematology drugs, and nutrition preparations, but their rates
for non-adherence were not stated (Table 1).
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Table 4 Reasons for medication non-adherence that led to hospital admissions
Reasons for non-adherence to medication Number of studies
Patient-related Experienced adverse events or side effects 42 23 25 33
Perceived as not necessary 42 23 25 33
Forgetfulness 32 23 33
Cognitive impairment or because of senility 225 32
Disliked taking medication 22 33
Visits to the clinic for continued medication administration considered a burden 123
Misunderstood the instruction from their general practitioner 125
Poor social circumstance 132
Healthcare professional-related
Confusing directions for use 22 23
Inadequate instruction 22 33
Switched to an non-conventional prescription 133
Healthcare system-related Cost of medication 22 33
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DISCUSSION
This systematic review and meta-analysis found that medication non-
adherence accounted for 4% of all hospital admissions or 29% of all medication-
related problems. These findings are similar to admission rates due to adverse drug
reactions which are considered a major health care burden,8 11 equally reflecting the
importance of medication non-adherence to patient safety.
It is noteworthy that hospital admissions due to non-adherence were judged
as almost always preventable largely by definition. In practice, this is an area that
requires high priority attention. Our findings indicate potential at-risk-groups, such as,
(i) patients having poor recall of their medication regimen, (ii) those who consult
multiple physicians, (iii) those receiving polypharmacy, and (iv) as in previous
reports,54-56 patients whose medication treats cardiovascular, respiratory, and
infectious diseases remain a problem. Appropriate and effective interventions are
needed, but so far, no single intervention strategy, or package of strateges has led to
large improvements of adherence across all patients, conditions, and settings.1 57
Nevertheless, a multiplicity of approaches is likely to have worthwhile gains.
Pharmacist-led interventions in England develivered by telephone has recently
demonstrated useful improvements in adherence.58-60 Considerable evidence has
accumulated suggesting that interventions tailored to individual patients together with
support from family, community, patient organisations, or healthcare professionals
trained in adherence management are required for improving medication non-
adherence.61 62 In addition, since medication reconciliation has showed promising
benefits in reduction of the rate of all-cause readmissions or all-cause ED visits,63
actions to improve medication adherence during hospital stay as part of an
enhanced medication reconcilication process should be explored further. However,
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evidence from robust cost-benefit analyses demonstrating improvements in patients
quality of life is needed to demonstrate both clinical and cost-effectiveness of such
interventions in routine clinical practice.
The observed prevalence rates of hospital admissions associated with
medication non-adherence were influenced by several factors which influenced
themarked heterogeneity between studies, including the geographical region and
method of detection. Another known factor is the non-uniformity in the terminology
and definitions of non-adherence which is in line with another systematic review
where a taxonomy for describing and defining adherence to medications was
proposed.64 According to this taxonomy, adherence to medications further divided
into three quantifiable phases: ‘initiation’, ‘implementation’, and ‘discontinuation’. To
our knowledge, no study has yet explicitly reported the risk of hospital admissions
using these phases. Therefore, we suggest that future studies should look into this
issue, especially the risk of hospital admissions due to poor implementation versus
non-persistence. Other possible patient-level sources of heterogeneity that were
identified previously are likely to include the complexity of the medication regimen,
level of education, and underlying medical conditions.65
This study has some limitations. Firstly, the included primary studies differed
in methodology. This could affect the estimation of prevalence rates. Secondly, few
studies reported the number of medications, number of comorbidities, family/society
support, and reasons for non-adherence, although these factors were cited as
important factors affecting non-adherence.66 Finally, we observed (a) variations in
how non-adherence was measured, and (b) small sample sizes in some sub-group
analyses. Such variation in methods, and rather small sub-groups may compromise
their interpretation. We suggest that further studies aiming to investigate the
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prevalence and nature of hospital admissions related to medication non-adherence
should include the following minimum characteristics; (i) clearly defined terms
describing medication non-adherence,53 (ii) clearly provide the number of patients
who were non-adherent and total number of admissions, (iii) apply explicit criteria for
assessing causality by experts, and (iv) use a validated tool for measuring non-
adherence to medication.
Our study had several important strengths. It is the first systematic review and
meta-analysis estimating the prevalence and nature of hospital admissions
associated with medication non-adherence. We untook an extensive search to
ascertain that the included studies were representative: this involved searching a
wide range of international bibliographic databases; hand searching for unpublished
articles; and without language restrictions. In the absence of information in individual
studies, we also contacted the study authors for additional data. The agreements
between reviewers was rated as ‘good’ for full-text screening and ‘excellent’ for
extracting prevalence rates and overall quality assessment. The results presented no
evidence of publication bias. In addition, we used an explicit criterion (ie., the
modified Crombie scale) to critique study quality.14 Finally, our study adheres to the
standard methodology of systematic review and meta-analysis as required by the
Cochrane and PRISMA checklists.12 67
Conclusions:
Hospital admissions associated with medication non-adherence were a
common problem. Almost all were preventable by definition and surpasses estimates
of preventable admission rates due to adverse drug reactions. Medications
commonly involved included those used to treat cardiovascular and respiratory
disorders, and infections. Future research and implementation should: (i) determine
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the most effective strategies to minimise unnecessary hospital admissionsdue to
non-adherence to medication, thereby improving patient safety, (ii) have robust study
designs that fulfill our quality checklist, and (iii) encompass emerging economies.
Conflict of Interest
The funders have played no part in the research project nor the preparation of
the manuscript. The authors have no conflicts of interest to declare.
Funding
Financial support from the Thailand Research Fund through the Royal Golden
Jubilee Ph.D. Program (Grant No. PHD/0197/2557) and Naresuan University
Research Fund (R2559C244) are gratefully acknowledged.
Author contributions
DMA and CK conceptualized the study; PM, CK performed the searches,
screened all the titles and abstracts for compliance with the inclusion criteria,
reviewed full-text articles of the potential studies and completed data extraction. All
included studies were assessed for methodological quality by PM and cross-checked
by CK. PM, CK drafted the manuscript. CNS, DMA, CK extensively revised the
manuscript. All authors have read and approved the final manuscript.
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Figure legends
Figure 1 A) PRISMA Diagram.12 B) Pooled prevalence estimate of hospital
admission-related to medication non-adherence when using the Haynes definition.43
30
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566
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571