· web viewwe searched the cochrane library, embase, cinahl, ipa and pubmed (until december...

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

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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


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)


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


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)


Bigby, et al. (1987)24

United States

Emergency admissions, Teaching hospital


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


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.


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.


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


Ward admitted,




HA rate due to NA

(%)Type of non-


with MRP


(%)

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


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


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.


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


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


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


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


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


68.4 yr, 58.9% male

Patients did not comply with the prescribed regimen


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


Ward admitted,




HA rate due to NA

(%)Type of non-


with MRP


(%)

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


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.


22/565 (3.89)

NR 22/136(16.18)

NR Low risk of bias

Kongkaew . (2009)37

United Kingdom

Two tertiary hospitals


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’


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


49.8±18.2 yr, 60% male

DRP classifications by Hepler and Strand44


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


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


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


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


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


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

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