Interventions for improving medication-taking ability and adherence in older adults prescribed multiple medications

Types of studies

We will include randomised controlled trials (RCTs), cluster RCTs and quasi-RCTs as specified by the Cochrane Consumers and Communication Review Group (CCCRG 2014).

Types of participants

We will include studies in which:

• most participants (80% or more) were aged 65 years and over, or the mean age was over 65 years. If studies are identified that do not meet these criteria, but have relevant data regarding older people that can be extracted separately, these will also be included.

• participants were living in the community or were discharged from a hospital or other healthcare facility to the community (living in the community includes in a person’s own home or retirement village/independent living unit, with or without additional support; it does not include situations in which professional carers or nurses administer the person's medications, such as in nursing homes, residential care facilities or full nursing care in the home).

• participants used at least four long-term regular prescription medications, or the group mean/median was more than four (irrespective of the participants’ number of medical conditions).

Studies that involve carers of consumers who meet these criteria will also be included. Carers are defined as “people who provide unpaid care and support to family members and friends who have disability, mental illness, chronic condition, terminal illness or general frailty” (ACSQHC 2012).

Types of interventions

We will include studies that tested single interventions or combinations of interventions directed at the consumer or their carer that sought to improve medication-taking ability and/or adherence by the consumer.

Examples include the following behavioural and educational interventions:

• support for behaviour change;

• provision of medication aids (e.g. dose administration aids, medication lists);

• medication regimen simplification;

• remote monitoring of medication use with or without feedback;

• facilitation of communication and decision making about medications;

• provision of information or education; and

• acquisition of skills and competencies.

This list of interventions is not exhaustive, since the types of interventions that might be used to improve the medication-taking ability and adherence of consumers is potentially unlimited. Acknowledging that this is the case, the search strategy (see Appendix 1) to be used will focus on terms that describe the outcomes of interest to avoid missing potentially-relevant studies that tested novel interventions.

We will include the following comparisons:

• interventions to improve medication-taking ability and/or adherence versus no intervention;

• interventions to improve medication-taking ability and/or adherence versus standard or usual care; and

• one form of intervention to improve medication-taking ability and/or adherence versus another - including simple versus complex interventions.

Types of outcome measures

Electronic searches

We will search the following electronic databases using search strategies tailored to each database:

• Cochrane Central Register of Controlled Trials (CENTRAL,Cochrane Library, latest issue);

• MEDLINE (OvidSP) (1966 to present);

• Embase (OvidSP) (1973 to present);

• PsycINFO (OvidSP) (1967 to present);

• CINAHL Plus (1981 to present); and

• International Pharmaceutical Abstracts (IPA) (1971 to present).

The strategy for MEDLINE (OvidSP) is presented in Appendix 1. We will tailor strategies to other databases and report them in the review.

There will be no language restrictions (provided the title and abstract are in English).

Searching other resources

For grey literature, we will search:

• the Joanna Briggs Institute Evidence Based Practice (EBP) Database; and

• conference proceedings (e.g. Scopus).

We will check the reference lists of included studies and previously published relevant systematic reviews to locate potential studies that have not been identified via electronic searches.

We will contact experts in the field and authors of included studies for advice as to other relevant studies.

We will also search the following online trial registries for ongoing and recently-completed studies:

• World Health Organization International Clinical Trials Registry Platform (ICTRP);

• ClinicalTrials.gov;

• ClinicalTrials.com;

• TrialsCentral;

• Australian New Zealand Clinical Trials Registry (ANZCTR);

• United Kingdom Clinical Research Network (UKCRN);

• Networked Digital Library of Theses and Dissertations (NDLTD); and

• ISRCTN registry.

Non-English language studies will be translated and included if they meet the eligibility criteria.

Selection of studies

Abstracts will be screened independently by two review authors and full text of any papers identified as potentially relevant by at least one author will be retrieved. Two authors will independently screen full-text articles for inclusion or exclusion, with discrepancies resolved by discussion and by consulting a third author if necessary to reach consensus. Review authors will not be responsible for the screening of studies in which they were involved or associated. All potentially-relevant papers excluded from the review at this stage will be listed as excluded studies, with reasons provided in the ‘Characteristics of excluded studies’ table. We will also provide citation details and any available information about ongoing studies, and collate and report details of duplicate publications, so that each study (rather than each report) is the unit of interest in the review. We will report the screening and selection process in an adapted PRISMA flow cart (Liberati 2009).

Data extraction and management

Two review authors will extract data independently from included studies. Any discrepancies will be resolved by discussion until consensus is reached, or through consultation with a third author where necessary. We will develop and pilot a data extraction form using the Cochrane Consumers and Communication Review Group Data Extraction Template (cccrg.cochrane.org/author-resources). Data to be extracted will include the following study details: aim of the intervention, study design, study population, intervention details, risk factor(s) for poor medication adherence and/or medication-taking ability targeted by the intervention(s), control/comparison group(s), outcome(s), and follow-up period(s). All extracted data will be entered into Review Manager (RevMan 2014, v. 5.3) by one review author, and will be checked for accuracy against the data extraction sheets by a second review author working independently.

Assessment of risk of bias in included studies

We will assess and report on the methodological risk of bias of included studies in accordance with the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) and Cochrane Consumers and Communication guidelines (CCCRG 2014), which recommend the explicit reporting of the following individual elements for RCTs: random sequence generation; allocation sequence concealment; blinding (participants, personnel); blinding (outcome assessment); completeness of outcome data, selective outcome reporting; and other sources of bias. We will consider blinding separately for different outcomes where appropriate (for example, blinding may have the potential to differently affect subjective versus objective outcome measures). We will judge each item as being at high, low or unclear risk of bias as set out in the criteria provided by Higgins 2011, and provide a quote from the study report and a justification for our judgement for each item in the 'Risk of bias' table.

Studies will be deemed to be at the highest risk of bias if they are scored at high or unclear risk of bias for either the sequence generation or allocation concealment domains, based on growing empirical evidence that these factors are particularly important potential sources of bias (Higgins 2011).

We will assess and report quasi-RCTs as being at high risk of bias on the random sequence generation item of the 'Risk of bias' tool. For cluster-RCTs we will also assess and report risk of bias associated with an additional domain: selective recruitment of cluster participants.

In all cases, two authors will independently assess the risk of bias of included studies, with any disagreements resolved by discussion to reach consensus. We will contact study authors for additional information about the included studies, or for clarification of the study methods as required. We will incorporate the results of the 'Risk of bias' assessment into the review through standard tables, and systematic narrative description and commentary about each of the elements, leading to an overall assessment of the risk of bias of included studies and a judgement about the internal validity of the review’s results.

Measures of treatment effect

The primary outcomes will be considered as dichotomous variables where possible. That is, the person (or carer) would be assessed as able to manage medications or not, and similarly have satisfactory (80% to 120%) or not satisfactory adherence. If sufficient studies that use the same outcome measure for a primary outcome exist, or if a study does not report its outcome as dichotomous, then additional analyses using continuous variables may be performed.

For dichotomous outcomes, we will analyse data based on the number of events and the number of people assessed in the intervention and comparison groups. We will use these to calculate the risk ratio (RR) and 95% confidence interval (CI). For continuous measures, we will analyse data based on the mean, standard deviation (SD) and number of people assessed for both the intervention and comparison groups to calculate mean difference (MD) and 95% CI. If the MD is reported without individual group data, we will use this to report the study results. If more than one study measures the same outcome using different tools, we will calculate the standardised mean difference (SMD) and 95% CI using the inverse variance method in Review Manager 5.

Unit of analysis issues

If cluster-RCTs are included we will check for unit of analysis errors. If errors are found, and sufficient information is available, we will re-analyse data using the appropriate unit of analysis, by taking account of the intracluster correlation (ICC). We will obtain estimates of the ICC by contacting authors of included studies, or impute the ICC using estimates from external sources. If it is not possible to obtain sufficient information to re-analyse data we will report effect estimates and annotate "unit of analysis error".

Dealing with missing data

We will attempt to contact study authors to obtain missing data (participant, outcome, or summary data). Where possible we will conduct analysis of participant data on an intention-to-treat basis; otherwise data will be analysed as reported. We will report on levels of loss to follow-up and assess this as a potential source of bias.

For missing outcome or summary data we will impute missing data where possible and report any assumptions in the review. We will investigate, through sensitivity analyses, the effects of any imputed data on pooled effect estimates.

We will also conduct a sensitivity analysis that excludes studies presenting data with loss to follow-up greater than 20%, including total reported lost to follow-up and differential loss to follow-up between groups. This is due to potential serious threats to validity associated with high loss to follow-up (Sackett 2000).

Assessment of heterogeneity

We anticipate substantial variations in types of interventions, populations studied, study designs and settings. Where studies are considered similar enough to enable data pooling using meta-analysis, we will assess the degree of heterogeneity by visual inspection of forest plots and examining the Chi² test for heterogeneity. Heterogeneity will be quantified using the I² statistic. An I² value of 50% or more will be considered to represent substantial levels of heterogeneity, but this value will be interpreted in light of the size and direction of effects and the strength of the evidence for heterogeneity, based on the P value from the Chi² test (Higgins 2011). Where heterogeneity is present in pooled effect estimates we will explore possible reasons for variability by conducting subgroup analysis.

Where we detect substantial clinical, methodological or statistical heterogeneity across included studies we will not report pooled results from meta-analysis but will instead use a narrative approach to data synthesis.

Assessment of reporting biases

We will assess reporting bias qualitatively based on the characteristics of the included studies (e.g. if only small studies that indicate positive findings are identified for inclusion), and if information that we obtain from contacting experts and authors of studies suggests that there are relevant unpublished studies.

If we identify sufficient studies (at least 10) for inclusion in the review we will construct a funnel plot to investigate small study effects, which may indicate the presence of publication bias. We will formally test for funnel plot asymmetry, with the choice of test made based on advice in Higgins 2011, and bearing in mind that there may be several reasons for funnel plot asymmetry when interpreting the results.

Data synthesis

We will decide whether to meta-analyse data based on whether the interventions in included trials are similar enough in terms of participants, settings, intervention, comparison and outcome measures to ensure meaningful conclusions from a statistically pooled result. Due to the anticipated variability in the interventions of included studies, we will use a random-effects model for meta-analysis.

If we are unable to pool data for meta-analysis we will conduct a narrative synthesis of results. We will group data based on the category that best explores the heterogeneity of studies and makes most sense to the reader (such as by interventions, populations or outcomes). We will present data in tables and narratively summarise the results for each category.

Subgroup analysis and investigation of heterogeneity

Subgroup analyses are proposed if adequate studies exist for each variable. These include:

• duration of intervention (short versus long);

• duration of follow-up (short, medium and long term) as described in ‘Timing of outcome assessment’;

• type of outcome measure (objective versus subjective);

• person managing the medication (consumer versus carer);

• number of medications (up to 10 versus 11 or more medications);

• frailty and/or functional ability (e.g. level of home-assistance required); and

• health professional group delivering the intervention (e.g. pharmacist versus nurse versus medical professional versus automated).

Sensitivity analysis

Should meta-analysis be possible for the outcomes of interest, two sensitivity analyses will be performed that exclude studies assessed to have losses to follow-up of greater than 20% and high risk of bias (determined by conducting 'Risk of bias' assessment).