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Interventions for improving outcomes in patients with multimorbidity in primary care and community settings

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Abstract

Background

Many people with chronic disease have more than one chronic condition, which is referred to as multimorbidity. The term comorbidity is also used but this is now taken to mean that there is a defined index condition with other linked conditions, for example diabetes and cardiovascular disease. It is also used when there are combinations of defined conditions that commonly co‐exist, for example diabetes and depression. While this is not a new phenomenon, there is greater recognition of its impact and the importance of improving outcomes for individuals affected. Research in the area to date has focused mainly on descriptive epidemiology and impact assessment. There has been limited exploration of the effectiveness of interventions to improve outcomes for people with multimorbidity.

Objectives

To determine the effectiveness of health‐service or patient‐oriented interventions designed to improve outcomes in people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual.

Search methods

We searched MEDLINE, EMBASE, CINAHL and seven other databases to 28 September 2015. We also searched grey literature and consulted experts in the field for completed or ongoing studies.

Selection criteria

Two review authors independently screened and selected studies for inclusion. We considered randomised controlled trials (RCTs), non‐randomised clinical trials (NRCTs), controlled before‐after studies (CBAs), and interrupted time series analyses (ITS) evaluating interventions to improve outcomes for people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual. This includes studies where participants can have combinations of any condition or have combinations of pre‐specified common conditions (comorbidity), for example, hypertension and cardiovascular disease. The comparison was usual care as delivered in that setting.

Data collection and analysis

Two review authors independently extracted data from the included studies, evaluated study quality, and judged the certainty of the evidence using the GRADE approach. We conducted a meta‐analysis of the results where possible and carried out a narrative synthesis for the remainder of the results. We present the results in a 'Summary of findings' table and tabular format to show effect sizes across all outcome types.

Main results

We identified 17 RCTs examining a range of complex interventions for people with multimorbidity. Nine studies focused on defined comorbid conditions with an emphasis on depression, diabetes and cardiovascular disease. The remaining studies focused on multimorbidity, generally in older people. In 11 studies, the predominant intervention element was a change to the organisation of care delivery, usually through case management or enhanced multidisciplinary team work. In six studies, the interventions were predominantly patient‐oriented, for example, educational or self‐management support‐type interventions delivered directly to participants. Overall our confidence in the results regarding the effectiveness of interventions ranged from low to high certainty. There was little or no difference in clinical outcomes (based on moderate certainty evidence). Mental health outcomes improved (based on high certainty evidence) and there were modest reductions in mean depression scores for the comorbidity studies that targeted participants with depression (standardized mean difference (SMD) −0.41, 95% confidence interval (CI) −0.63 to −0.2). There was probably a small improvement in patient‐reported outcomes (moderate certainty evidence). The intervention may make little or no difference to health service use (low certainty evidence), may slightly improve medication adherence (low certainty evidence), probably slightly improves patient‐related health behaviours (moderate certainty evidence), and probably improves provider behaviour in terms of prescribing behaviour and quality of care (moderate certainty evidence). Cost data were limited.

Authors' conclusions

This review identifies the emerging evidence to support policy for the management of people with multimorbidity and common comorbidities in primary care and community settings. There are remaining uncertainties about the effectiveness of interventions for people with multimorbidity in general due to the relatively small number of RCTs conducted in this area to date, with mixed findings overall. It is possible that the findings may change with the inclusion of large ongoing well‐organised trials in future updates. The results suggest an improvement in health outcomes if interventions can be targeted at risk factors such as depression in people with co‐morbidity.

PICOs

Population
Intervention
Comparison
Outcome

The PICO model is widely used and taught in evidence-based health care as a strategy for formulating questions and search strategies and for characterizing clinical studies or meta-analyses. PICO stands for four different potential components of a clinical question: Patient, Population or Problem; Intervention; Comparison; Outcome.

See more on using PICO in the Cochrane Handbook.

Plain language summary

Improving outcomes for people with multiple chronic conditions

Background

The World Health Organization defines chronic conditions as "health problems that require ongoing management over a period of years or decades". Many people with a chronic health problem or condition, have more than one chronic health condition, which is referred to as multimorbidity. This generally means that people could have any possible combination of health conditions but in some studies the combinations of conditions are pre‐specified to target common combinations such as diabetes and heart disease. We refer to these types of studies as comorbidity studies. Little is known about the effectiveness of interventions to improve outcomes for people with multimorbidity. This is an update of a previously published review.

Review question

This review aimed to identify and summarise the existing evidence on the effectiveness of interventions to improve clinical and mental health outcomes and patient‐reported outcomes including health‐related quality of life for people with multimorbidity in primary care and community settings.

Description of study characteristics

We searched the literature up to September 2015 and identified 17 generally well‐designed randomised controlled trials meeting the eligibility criteria. Nine of these studies focused on specific combinations of health conditions (comorbidity studies), for example diabetes and heart disease. The other eight studies included people with a broad range of conditions (multimorbidity studies) although they tended to focus on elderly people. The majority of studies examined interventions that involved changes to the organisation of care delivery although some studies had more patient‐focused interventions. All studies had governmental or charitable sources of funding.

Key results

Overall the results regarding the effectiveness of interventions were mixed. There were no clear positive improvements in clinical outcomes, health service use, medication adherence, patient‐related health behaviours, health professional behaviours or costs. There were modest improvements in mental health outcomes from seven studies that targeted people with depression. Results indicated that interventions may possibly improve   functional outcomes in the studies that reported these outcomes. . Overall the results indicate that it is difficult to improve outcomes for people with multiple conditions. The review suggests that interventions that are designed to target specific risk factors (for example treatment for depression) may be more effective. There is a need for further studies on this topic, particularly involving people with multimorbidity in general across the age ranges.

Quality/certainty of the evidence

All of the included studies were randomised controlled trials. The overall quality of these studies was good though many studies did not fully report on all potential sources of bias. As definitions of multimorbidity vary among studies, the potential to reasonably combine study results and draw overall conclusions is limited. Overall, we judged that the certainty or confidence we can have in the results from this review is moderate but due to small numbers of studies and mixed results we acknowledge the uncertainty remaining and the potential that future studies could change our conclusions.

Authors' conclusions

Implications for practice

Multimorbidity is common in clinical practice and is an important problem in most healthcare systems. While the evidence supporting specific intervention types is limited, it does suggest that clinicians and policy makers should prioritise interventions that target specific problems experienced by people with multimorbidity or should target common comorbid conditions. However, we can only be moderately certain that this is the case and new services and interventions should be evaluated robustly to contribute to the much‐needed evidence to support clinical practice. The epidemiological data on the impact of multimorbidity highlights the specific challenges for people who are socioeconomically disadvantaged (Barnett 2012); and interventions targeting this population have the potential to address health inequalities. One of the ongoing studies specifically targets multimorbidity in areas of deprivation (Mercer ongoing).

The sub‐group analysis from the Guided Care study discussed above suggests that multimorbidity interventions need to be integrated into existing healthcare systems to support implementation and sustainability (Boult 2011). Independent interventions that do not integrate with existing healthcare systems will be difficult to sustain. Many of the included studies focused on integration of care between practitioners, but we also need to consider how interventions can be integrated into healthcare systems. It is likely that local adaptations will need to be made even for interventions that are effective. For example, we are confident in the review findings that interventions targeting comorbid depression are effective but these interventions require training and support for primary care clinicians which may not be available in all settings.

The literature on multimorbidity indicates that it is generally associated with poorer outcomes for patients. However, health planners and policy makers need to consider which outcomes they want to target in an intervention. This should be considered in the early stages of the development of a potential new intervention. People with multimorbidity are not only at higher risk of many adverse outcomes, but they are also more likely to experience 'treatment burden', that is that the effort needed to engage in the multiple treatments offered to them actually make their lives more difficult (May 2009). Having the individual participate in priority setting based on his/her values and preferences becomes both the rational and the ethical thing to do.

Implications for research

Definitions

There is a need for a clear conceptual definition of multimorbidity and its differentiation from other related concepts such as comorbidity, complexity, frailty, and vulnerability. The variation in definitions in the studies included in this review highlight the need for clear reporting of participant characteristics to allow consideration of external validity and generalisability. This will be particularly important given the need to account for the heterogeneity of multimorbidity; interventions could have differential effects depending on the definition or degree of multimorbidity and the socioeconomic status of participants.

Without these definitions and consideration of related concepts, the generalisability or applicability of studies for people with multimorbidity (with a broader definition than only two or three specific diseases) will be uncertain, as is the case for many of the studies in this review, particularly those with the specific comorbidity focus (Fortin 2013).

We would also advocate for including multimorbidity as a MeSH term as the search strategy for this review and for ongoing work on multimorbidity is particularly complex and time consuming, given the growing concern and interest in the issue.

Study design

While the risk of bias was generally low in this review and all studies were RCTs, we acknowledge the challenge of conducting organisational type interventions using optimal RCT designs, so pragmatic trials or quasi‐experimental studies may also be appropriate while still maintaining rigour. This could include the use of stepped wedge cluster RCTs that would involve regional introduction of organisational or health system delivery change while still allowing for robust evaluation.

Future studies need to carefully consider the comparison or control group, particularly in relation to contamination of control participants. Cluster randomised designs are likely to be optimal if interventions are delivered through care providers. This needs to be taken into account both in terms of power calculations and in the analysis of results.

Interventions

This review indicates that interventions that are targeted at either specific combinations of common conditions such as comorbid depression, or at specific problems for people with multiple conditions, may be more effective. When designing interventions researchers should be clear about the theoretical assumptions underlying the intervention, consider its individual components and the evidence base behind each and then link these to outcomes as outlined below. They should also consider interventions that are likely to be reproducible and applicable within the context of primary care. The Medical Research Council Framework for the Design and Evaluation of complex interventions designed to improve health, provides useful guidance in designing and undertaking these trials (MRC Framework 2008).

A group of researchers active in the area of multimorbidity has developed a specific framework for the development of interventions for multimorbidity which is based on a series of workshops undertaken over a two‐year period combined with the experience of this expert group (Smith 2013). This framework highlights the potential for other study designs such as stepped wedge designs that may be more suited to multimorbidity intervention initiatives and that can be undertaken within service/ research partnerships. The framework also stresses the importance of clearly describing all intervention components to allow replicability and generalisability to other settings.

Within this review, inter professional collaboration was embedded in all interventions. This is worth building on for future intervention development. Most of the included studies focused on changing professional care provision; it may also be worthwhile incorporating the participants' perspective. This could be achieved by adopting a participatory approach to intervention development. People with multimorbidity, their family members, and a range of professionals involved should be consulted during the modelling and exploratory phases of service and intervention planning.

The majority of the evidence for effective chronic disease management has been based on a single disease paradigm. However, it is likely that participants in these trials had some degree of multimorbidity, though sicker individuals may have been excluded. This is also the case for trials examining interventions for frail older people or for interventions seeking to improve care transitions as many participants in these studies also have multimorbidity though this is not usually clearly reported or addressed as a potential confounding variable. We should therefore seek to build on and apply the evidence regarding effective interventions for single conditions or related interventions to people with multimorbidity, rather than designing interventions with no consideration of the existing evidence base for single conditions.

In its broadest sense, multimorbidity encompasses a large variety of individuals which must be considered as it is not pragmatic to design interventions that change systems completely. For this reason, parallel economic analyses that link outcomes to costs and benefits are better than providing simple cost data alone, which make comparison across studies difficult.

Outcomes

The challenge with multimorbidity is to define a set of outcomes that can be used for different combination of diseases, so there is a need for generic outcomes measures that incorporate physical functioning, quality of life and measure of treatment burden that are responsive to change over time. Other outcomes to consider include goal attainment, self care, self efficacy, health related quality of life, distress, adherence to treatment, behavioural changes regarding health habits, individuals' knowledge about care plans, shared decision making, and participation in care. However, unless validated measures are used, many of these outcomes will not be comparable across studies. The recent work of PROMIS (PROMIS 2011) provides validated and useful patient‐reported outcomes that will be particularly relevant for those researching interventions to improve outcomes for people with multimorbidity. Work to develop a core outcome set for multimorbidity using methodology recommended by the COMET initiative (http://www.comet‐initiative.org/) is ongoing.

Most of the interventions in this review used a conceptual model, particularly the Chronic Care Model. In general there needs to be clearer reporting of intervention development and outcomes chosen to reflect the theoretical underpinning as to how and why an intervention might work. It would also be helpful if authors clearly identified intervention elements and matched outcomes to these elements in an effort to clarify which components of multifaceted interventions are more effective than others.

Conclusion

This review highlights the relatively limited but growing evidence underpinning interventions to improve outcomes for people with multimorbidity with the focus to date being on comorbid conditions or multimorbidity in older individuals. The results suggest that interventions to date have had mixed effects but have shown a tendency to improve outcomes if organisational interventions can be targeted at risk factors in common comorbidities such as depression or multidisciplinary team interventions focused on specific functional difficulties in people with multimorbidity. Due to the number of studies and their low risk of bias, we can be confident that there is an effect on depression outcomes in the comorbidity studies that included treatment for depression but there are fewer studies supporting the conclusions for targeting functional difficulties in multimorbidity generally and these findings may change as new evidence becomes available. However, further research is needed and future interventions should be developed in ways that allow rigorous evaluations to be performed that will add to the evidence. There is a need for clear and broader definitions of participants, consideration of appropriate outcomes, and further pragmatic studies based in primary care settings.

Summary of findings

Open in table viewer
Summary of findings 1. Summary of findings

Interventions aimed at improving outcomes for people with multimorbidity compared with usual care

Participant or population: Adults with multimorbidity (two or more chronic conditions)

Settings: Primary care and community settings

Intervention: Any intervention designed to improve outcomes for people with multimorbidity including professional‐, organisational‐ and patient‐oriented interventions

Comparison: Usual care

Outcomes

Impacts

Number of studies

Quality of the evidence
(GRADE)

Clinical outcomes

There is no clear effect on clinical outcomes with a range of standardised effect sizes from 0.01 to 0.78 with a minority having effect sizes > 0.5; interventions aimed at improving management of risk factors in comorbid conditions were more likely to have higher effect sizes.

10

⊕⊕⊕⊖

Moderate

Mental health outcomes

There are improved depression‐related outcomes in studies targeting comorbid conditions that include depression with a range of standardised effect sizes from 0.09 to 1.18 with 3 of 7 studies having moderate to large effect sizes (> 0.5) . Standardised mean difference of −0.41 (95% CI, −0.63 to −0.20) was calculated from combining data from 6 studies.

9

⊕⊕⊕⊕

High

Patient‐reported outcome measures (PROMs)

There are mixed effects on PROMs with only half of studies that reported these outcomes showing any benefit with a range of standardised effect sizes from 0.03 to 0.84. Only 1 of 5 studies with data available data on self‐efficacy had a moderate effect size ​(>0.5), 1 of 6 had a moderate effect size for HRQoL, and effect sizes for other psychosocial outcomes were generally low.

13

⊕⊕⊕⊖

Moderate

Health Service Utilisation

There were no effects on health service utilisation and changes in visits were difficult to interpret as some interventions could lead to higher numbers of visits if previous unmet need was being addressed. There was no difference in admission‐related outcomes, though numbers of admissions in most of these studies were very small.

5

⊕⊕⊖⊖

Low

Medication use and adherence

There are mixed effects on medication use and adherence with half the studies reporting this outcome showing benefit. Proportions adherent to medication were higher in intervention participants with ranges in absolute difference of 10% to 40% but all studies with available data had small effect sizes.

4

⊕⊕⊖⊖

Low

Health‐related patient behaviours

Studies measuring this outcome reported a range of effects varying from an additional 18 minutes spent walking per week to an absolute difference in kcals expenditure per week of 2516 (no studies presented data that could be used to calculate effect sizes).

7

⊕⊕⊕⊖

Moderate

Provider behaviour

The majority of studies reporting provider behaviour indicated improved provider behaviour relating to care delivery; three studies reported a range of 15% to 40% in proportions of intervention providers improving behaviours such as appropriate referral.

5

⊕⊕⊕⊖

Moderate

GRADE Working Group grades of evidence
High quality: Further research is very unlikely to change our confidence in the estimate of effect.
Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate.
Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate.
Very low quality: We are very uncertain about the estimate.

We downgraded the evidence for effects on clinical and psychosocial outcomes to moderate due to lack of consistency of effect across studies and small effect sizes. We downgraded the evidence for effects on provider behaviour to moderate due to limited available data for calculation of standardised effect sizes (SES) and lack of clarity regarding the clinical importance of the results. We downgraded the evidence for effects on health service utilisation and medication use and adherence to low due to variation across studies and small effect sizes.

Background

Many people with chronic disease have more than one chronic condition, which is referred to as multimorbidity. While this is not a new phenomenon, there is greater recognition of its impact and the importance of improving outcomes for individuals affected. Research in the area to date has focused mainly on descriptive epidemiology and impact assessment (Fortin 2007). There has been limited exploration of the effectiveness of interventions to improve outcomes for people with multimorbidity.

Description of the condition

There has been increasing focus on the enormous personal and societal burden of ill‐health caused by chronic disease. The World Health Organization (WHO) has emphasised the importance of organising healthcare delivery systems to improve health outcomes and has stressed the importance of building integrated healthcare systems that can address chronic disease management (WHO 2002). This can be done by focusing on generic chronic care models, as has happened mainly in the United States of America (USA), or by developing national systems focusing on single chronic conditions as has happened with the National Service Frameworks in the UK (Lewis 2004; Satariano 2013). However, many people with chronic disease have more than one chronic condition, which is referred to as multimorbidity and formally defined as the co‐existence of two or more chronic conditions (Fortin 2005). While this is not a new phenomenon, there is greater recognition of its impact and the importance of improving outcomes for individuals affected (Fortin 2007; Smith 2007).

While the accepted term for people with multiple chronic conditions is now multimorbidity, the term comorbidity has been used interchangeably in the past. It is now accepted that comorbidity should be used when there is a specified index condition or where there are defined combinations of conditions (for example hypertension and cardiovascular disease) as opposed to multimorbidity where any condition could be included (Valderas 2011). Multimorbidity is the more general term and individuals with comorbidity also have multimorbidity but the reverse does not necessarily apply. For the purposes of this review when analysing the included studies, we looked at studies based on the intervention elements but we also considered differences between studies that specifically target comorbid conditions as opposed to those targeting general multimorbidity. This is because interventions in the comorbidity studies are designed to target the specific included conditions. These distinctions are important in the context of developing and evaluating effective interventions and considering their generalisability (Fortin 2013; Smith 2013).

Individuals with multimorbidity are more likely to die prematurely (Deeg 2002; Menotti 2001; Rochon 1996), be admitted to hospital (Bähler 2015; Condelius 2008; Payne 2013), and have longer hospital stays (Bähler 2015; Librero 1999). They have poorer quality of life (Brettschneider 2013), loss of physical functioning (Bayliss 2004; Fortin 2004; Fortin 2006b), and are more likely to suffer from psychological stress (Fortin 2006a; Gunn 2012). Medicines management is often complex, resulting in polypharmacy with its attendant risks of drug interactions and adverse drug events (Duerden 2013; Gandhi 2003; Guthrie 2011). For patients, in addition to understanding and managing their conditions and drug regimes, they must also attend multiple appointments with different healthcare providers and adhere to lifestyle recommendations (Gallacher 2011; Townsend 2006).

Prevalence studies of multimorbidity have been carried out in different countries indicating that, particularly in those over 60 years, the majority of people attending family primary care services had more than one chronic condition (Fortin 2005; Fortin 2006c; van den Akker 1998; Wolff 2002). A subgroup of these service users have a debilitating combination of conditions that have a high impact on their own lives but also on their utilisation of health services and related costs (Hoffman 1996; Marengoni 2011; Parmelee 1995; Smith 2008). This emerging concept may be referred to as 'complex multimorbidity' and has been defined as people with three or more chronic conditions involving three or more body systems (Harrison 2014). These individuals can pose management difficulties, resulting in frequent health care visits, frequent emergency hospital admissions, and repeated investigations with enormous cost both for the individuals and the healthcare system involved. A UK report has examined the costs associated with this group of people who are described as 'high impact users' on the basis of their frequent emergency admissions (Rowell 2006). Fragmentation of care is a significant problem for this group, resulting from the involvement of both primary care and multiple specialists who may not be communicating with each other effectively (Wallace 2015). Starfield found that people with a greater morbidity burden have a higher use of specialists even for conditions that are normally managed in primary care, and concludes that there is a need for a better understanding of the roles of generalists and specialists in managing these individuals (Starfield 2005)

Description of the intervention

Given the complexity of managing people with multiple chronic conditions, potential interventions are likely to be complex and multifaceted if they are to address the varied needs of these individuals. We anticipated that a variety of intervention types could work to improve outcomes for people with multimorbidity and could be included within the scope of this review. Cochrane Effective Practice and Organisation of Care (EPOC) has developed a taxonomy that defines intervention types (EPOC 2002). We have used this taxonomy to define health service and patient‐oriented interventions that have been designed to improve outcomes of people or populations with more than one chronic condition.

1. Professional interventions: for example, education designed to change the behaviour of clinicians. Such interventions may work by altering professionals' awareness of multimorbidity or providing training or education designed to equip clinicians with skills in managing these individuals, thus improving their healthcare delivery.

2. Financial interventions: for example, financial incentives to providers to reach treatment targets. These interventions might work by incentivising health service delivery and providing resources to extend consultation length for people with multimorbidity.

3. Organisational interventions: these can be further divided into organisational changes delivered through practitioners or directly to patients. For example, any changes to care delivery such as case management or the addition of different healthcare workers such as a pharmacist to the healthcare team. These interventions may work by changing care delivery to match the needs of people with multimorbidity across a range of areas such as coordination of care, medicines management, or use of other health professionals such as physiotherapists and occupational therapists to address needs relating to physical and social functioning.

4. Patient‐oriented interventions: this would include any intervention directed primarily at individuals, for example, education or support for self management. These interventions might work by improving self management, thus enabling people to manage their conditions more effectively and to seek appropriate health care.

5. Regulatory interventions: for example, changes to local or national regulations designed to alter care delivery in order to improve outcomes. Such interventions might work by introducing regulatory changes that facilitate and enable the funding of care that is directed towards those with complex health needs. An example could be the introduction of free primary care for people with multimorbidity on the basis that preventive care might prevent subsequent more costly hospital admissions. While we did not find these types of interventions, we believe they could exist and would fall within the scope of this review for future updates.

How the intervention might work

We anticipated that organisational‐type interventions might predominate. We were aware that there has been a focus on case management, based mainly in health maintenance organisations in the USA (Zwarenstein 2000). Case management is defined as the explicit allocation of co‐ordination of tasks to an appointed individual or group and it is postulated that the function of co‐ordination is so important and specialised that responsibility for carrying it out needs to be explicitly allocated (Zwarenstein 2000). Our review included studies where case management was employed but only if it was specifically directed towards individuals identified as having multimorbidity.

The implementation of the Family Medicine Groups in the province of Québec, Canada, is another example of an organisational intervention as it involved new forms of shared responsibilities between physicians and nurses (MSSS 2001). Another example in the United Kingdom (UK) is the community matrons programme, which is being delivered through primary care trusts and is based on nurse‐provided case management for people with complex care needs including those with multimorbidity (London DOH 2005). It is similar to previous programmes delivered through social services in the 1990s and there have been concerns expressed as to the feasibility of achieving the programme targets without real integration of primary and specialist services (Murphy 2004).

The differences outlined earlier between multimorbidity in general and comorbidity where there are defined combinations of conditions also influences how interventions are designed. Interventions targeting specified comorbid conditions can be designed to address the specific challenges for people with those conditions. For example, an intervention that targets people with diabetes and depression will combine elements of diabetes‐focused care with psychotherapy or escalation of antidepressant medication, or both interventions, so as to address both conditions. Interventions for people with multimorbidity in general cannot have a disease focus as there are no pre‐specified conditions so the interventions might address improved coordination of care, improved medicines management or specific functional difficulties experienced by patients.

Since this review was originally planned in 2007, there has been widespread recognition of the need to address the challenge of multimorbidity across health systems with a series of articles in international medical journals highlighting the challenges involved. Two very useful resources highlighting the challenges of multimorbidity and collating research in the area are: i) the BMJ multimorbidity special collections (BMJ Multimorbidity collection) and ii) the International Research Community on Multimorbidity archive IRCMO at the University of Sherbrooke, Quèbec, Canada (IRCMO). The BMJ series includes a series of editorials, original research studies and a clinical review with a multimorbidity focus. IRCMO provides a platform for any researcher interested in multimorbidity to contribute to a regularly updated blog and also compiles a list of multimorbidity related publications.

Why it is important to do this review

This review was originally undertaken based on the clear recognition of the need for integrated care for people with multiple conditions who have complex care needs (Stange 2005). The evidence base for managing chronic conditions is based largely on trials of interventions for single conditions and individuals with multimorbidity are often excluded from such studies (Fortin 2006c; Starfield 2001; Wyatt 2014Zulman 2011). The inadequacy of existing clinical guidelines to support clinicians in managing people with multimorbidity has been highlighted as a significant issue in delivering care (Dumbreck 2015; Wyatt 2014). Clinical guideline developers have attempted to address this issue with the consideration of certain combinations of commonly co‐occurring conditions, for example, diabetes and depression (NICE 2009). However good quality evidence is essential to inform this clinical area and in recent years focus has shifted to intervention development and the need to reorientate clinical practice and healthcare systems for the people who use them most (Satariano 2013).

Objectives

To determine the effectiveness of health‐service or patient‐oriented interventions designed to improve outcomes in people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual.

Methods

Criteria for considering studies for this review

Types of studies

We considered randomised controlled trials (RCTs), non‐randomised clinical trials (NRCTs), controlled before‐after studies (CBAs), and interrupted time series analyses (ITS), meeting EPOC quality criteria (EPOC 2013). We included NRCTs in the original protocol (Smith 2007b) as we anticipated that, given the challenges in undertaking multimorbidity research (Fortin 2007) and the likelihood that complex interventions would be tested, there would be relatively few RCTs and that non‐randomised designs might be used instead.

Types of participants

We included any people or populations with multimorbidity receiving care in a primary or community care setting. We adopted the most widely used definition of multimorbidity, that is, the co‐existence of multiple chronic diseases and medical conditions in the same individual, usually defined as two or more conditions (Fortin 2004; van den Akker 1998). We used the WHO definition of chronic disease, which is "health problems that require ongoing management over a period of years or decades" (WHO 2002). We included all studies that identified participants or sub‐groups of participants on the basis of multimorbidity, as defined by the study authors. In some studies, additional eligibility criteria were applied (for example, history of high service utilisation) in an effort to identify more vulnerable people who might benefit more from the intervention being studied.

We excluded studies where multimorbidity was assumed to be the norm on the basis of individuals' age as the interventions were not being targeted specifically at multimorbidity and its recognised challenges. This included studies where interventions were directed at communities of people based on location or age of participants in which participants could be presumed to have multimorbidity on the basis of their age or residence in a nursing home but interventions were not designed to specifically target multimorbidity.

Types of interventions

We included any type of intervention that was specifically directed towards a group of people defined as having multimorbidity. Only interventions based in primary care and community settings were included. Interventions included care delivered by family doctors, nurses, or other primary care professionals. Primary health care was defined as providing "integrated, easy to access, health care services by clinicians who are accountable for addressing a large majority of personal health care needs, developing a sustained and continuous relationship with patients, and practicing in the context of family and community" (Vaneslow 1995). However, not all countries have clearly‐defined primary care systems (Starfield 1998), so we included care delivered in community settings by individuals fulfilling the basic criteria for primary care, i.e. if they are available to treat all common conditions in all age groups and have an ongoing relationship with their patients. While some specialists may deliver components of primary care to their patients, practitioners were not included unless they fulfilled the definition of being available to treat all conditions and have an ongoing relationship with their patients.

Interventions were classified as 'simple' if they used one identifiable component or 'multifaceted' if they incorporated more than one feature.

We categorised interventions using the EPOC taxonomy presented in the Background section. Where interventions had multiple elements, we defined each element within the taxonomy and highlighted the predominant element of the intervention (see Table 1).

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Table 1. Multimorbidity intervention components

Author Year

Professional

Participant

Organisational

Effect of intervention on primary outcome

Case management or coordination of care

Reorganisation of care/team working

New team member

Predominantly organisational

Barley 2014

Nurse training

Participant information

Prioritisation to create

goals and health plan

Case manager provided personalised care

Regular planned participant visits

Weekly team meetings

Nurse case manager

Pilot study and primary outcome was feasibility and deemed successful

Bogner 2008

Individualised programme

Case manager

Regular planned participant visits

Improved blood pressure control and depression scores

Boult 2011

Nurse training

Individual management plans

Support for self‐management

Guided care nurses coordinated care

Guided care 'pods' consisting of nurse and PCP

Monthly monitoring of participants

No impact on healthcare utilisation

Coventry 2015

Practice team training

Personalised goals and participant workbooks

Collaborative care using stepped care protocols

Joint consultation between participant, psychologist and practice nurse

Psychologist

Supervision and input from team psychiatrist

Modest reduction in depression scores

Hogg 2009

Individualised care plans

Multidisciplinary team‐based management with home based assessment

Medication review

Pharmacist

Modest improvements in quality of chronic care delivery

Katon 2010

Individualised management plans and targets

Support for self‐management

Team‐based care

Stepped care treatment protocols

Weekly team meeting

Psychologist and psychiatrist supported depression care

Improvements in composite outcome of glycaemic control, blood pressure, lipids and depression scores

Kennedy 2013

Practice training

Support for self‐management

Participant guidebooks

Systems‐based approach to self‐management support with practice supports and links made with related local services

No intervention effect noted

Krska 2001

Individualised pharmaceutical care plans

Practice team‐implemented care plans

Pharmacist undertook medication review and devised pharmaceutical care plans

Reduction in pharmaceutical care issues

Martin 2013

Training for community psychologists

Cognitive behavioural therapy sessions

Psychological care programme designed for headache and depression

Community psychologists

Reduced headaches and improved depression scores

Morgan 2013

Practice nurse training

Support for self‐management

Goal setting

Individualised care plans

Nurse case manager

Quarterly reviews with practice nurse with GP stepping up care as needed

Improved depression scores

Sommers 2000

Risk reduction plan

Team based care with home assessment followed by team discussion, treatment plan and targets

Social worker

Reduced hospitalisation

Wakefield 2012

Participation in home telehealth monitoring

Nurse case manager using telehealth monitoring and treatment algorithms

Improved blood pressure, no effect on glycaemic control

Predominantly Patient‐oriented interventions

Eakin 2007

Support for self‐management with focus on diet and physical activity

Regular visits and follow‐up telephone calls

Health educator

Improvements in diet but not in physical activity

Garvey 2015

Occupational therapist (OT) training

OT‐led, group‐based support for self‐management programme (6 weeks)

Goal setting and peer support

GP and primary care team referral

OT with input from physiotherapist and pharmacist

Improvements in activity participation

 

 

 

 

 

 

 

Hochhalter 2010

Training for coaches running intervention

Patient Engagement workshop (x1)

Two follow‐up phone calls

Coach who delivered workshop

No effect on outcomes

Lorig 1999

Training for volunteer lay group leaders

Chronic Disease Self Management Support Programme (six sessions)

Peer support

Volunteer lay group leaders supported by study team

No primary outcome specified. Multiple outcomes reported with mixed effects

Lynch 2014

Diabetes self management support groups (18 sessions)

Peer support

Goal setting and behaviour skills training

Dietician led groups

No effect on primary outcome of weight reduction

The predominant intervention component is highlighted in bold text for each study

No study contained a financial‐type intervention element

We excluded the following interventions:

  • Professional educational interventions or research initiatives where there was no specified structured clinical care delivered to an identified group of people with multimorbidity.

  • Interventions including people with comorbid conditions where the intervention was targeted solely at one condition and did not address the full extent of the multimorbidity. This commonly arises in relation to chronic disease and comorbid depression, so called 'depression plus one studies'. These are increasingly common as the link between depression and most chronic conditions has now been well established (Simon 2001). They include interventions designed to address depression in participants rather than targeting all conditions identified. We therefore excluded such studies if the intervention was only targeted at the depression and did not address the full extent of the multimorbidity.

The comparison was usual care.

Types of outcome measures

We included studies if they reported any objective, validated measure of:

  • Patient clinical or mental health outcomes (e.g. blood pressure, symptom scores, depression scores).

  • Patient‐reported outcome measures (e.g. quality of life, well‐being, measures of disability or functional status).

  • Utilisation of health services (e.g. hospital admissions).

  • Patient behaviour (e.g. measures of medication use and adherence, and other objective measures such as goal attainment (Cox 2002; Gordon 1999; Kiresuk 1968), if measured with a validated scale.

  • Provider behaviour (e.g. chronic disease management scores).

  • Acceptability of the service to recipients and providers, and treatment satisfaction were included if it was reported in a study that reported objective outcome measures behaviour.

  • Economic outcomes (e.g. full economic analyses incorporating measures of efficiency or effectiveness in relation to costs or direct costs depending on what was reported in included studies). Where direct costs were reported alone, we indicated whether these costs related to society, the health service, or the recipients. We also reported, where possible, costs in relation to the specific year and currency presented; whether costs related to total costs or simple fees charged; what was included in the cost calculations; and over what time period costs were calculated.

We excluded attitude and knowledge outcomes.

Search methods for identification of studies

Electronic searches

We searched the following electronic databases without language restrictions up to 28 September 2015:

  • Cochrane Central Register of Controlled Trials (CENTRAL), The Cochrane Library, 2015, Issue 10, Wiley

  • Database of Abstracts of Reviews of Effects (DARE), The Cochrane Library, 2015, Issue 3, Wiley

  • MEDLINE, 1990 to September 2015, In‐Process and other non‐indexed citations, OvidSP

  • EMBASE, 1980 to September 2015, OvidSP

  • Cochrane Effective Practice and Organisation of Care (EPOC) Group Specialised Register, Reference Manager

  • Cumulative Index to Nursing and Allied Health Literature (CINAHL), 1980 to September 2015, EBSCOHost

  • Allied and Complementary Medicine Database (AMED), 1985 to September 2015, OvidSP

  • CAB Abstracts, 1973 to September 2015, EBSCOHost

  • HealthSTAR, 1999 to September 2015, OvidSP

We also searched the following trials registries:

We searched the IRCMO repository for unpublished/grey literature (IRCMO), and invited experts to inform us of other completed or ongoing studies

The search strategy was particularly challenging given the lack of a MeSH terms for multimorbidity. In addition, we were aware from existing epidemiological literature that the recognition of multimorbidity as a concept is relatively recent. Multimorbidity is sometimes used synonymously with the term comorbidity, though this tends to be used in relation to diseases that coexist with an index disease under study (de Groot 2004). However, comorbidity is a MeSH term, whereas multimorbidity is not, so we included both terms in our search. For pragmatic reasons we limited the MEDLINE search to articles indexed from 1990 onwards.

The search strategy published in the protocol (Smith 2007b) was not used; and the search strategy recorded for the 2007 search of MEDLINE was revised in 2009 to better capture the concept of multimorbidity. Results of the search were limited by filters for study design and an extensive list of intervention terms. Search strategies are provided in Appendix 1; Appendix 2; Appendix 3; Appendix 4; Appendix 5. The MEDLINE search strategy was used in HealthSTAR and AMED; the Cochrane search strategy was used in DARE.

Searching other resources

We also:

(a) Searched the reference lists of included papers
(b) Contacted authors of relevant papers regarding any further published or unpublished work where indicated

Data collection and analysis

Selection of studies

All citations identified by the electronic searches were downloaded to reference manager software (EndNote 2013) and duplicates were removed. Potentially relevant studies were identified by review of the titles and abstracts of search results by the lead author (SS). We retrieved full text copies of all articles identified as potentially relevant. Two review authors (SS, HS, or EW) ) independently screened all citations found by the electronic searches and assessed each retrieved article for inclusion. We resolved any disagreement by discussion and consensus.

Data extraction and management

Two review authors (SS, HS or EW ) undertook data abstraction and cross checked data abstraction forms using a modified version of the EPOC data collection checklist (EPOC 2013a). Disagreements about data abstraction and quality were resolved by consensus between the review authors or through adjudication by the Cochrane contact editor.

We extracted the following information from the included studies:
(1) Details of the intervention: a full description of the intervention was extracted as were details regarding aims; clinical protocols; use of case workers; remuneration/payment systems; providers involved; and theoretical framework on which the intervention was based; (2) Participants: patients, the nature of multimorbidity and how it was determined; providers, i.e. specialist and primary care providers, family members; (3) Clinical setting; (4) Study design; (5) Outcomes; (6) Results. Results were organised into: (i) Clinical outcomes; (ii) Mental health outcomes; (iii) Patient‐reported outcomes; (iv) Health service use (v) Recipient and provider behaviours; and (vi) Recipient and provider acceptability/satisfaction.

Assessment of risk of bias in included studies

Two review authors independently assessed the risk of bias in all included studies using standard EPOC criteria (EPOC 2015) and included the following domains: allocation (sequence generation and concealment); baseline characteristics; incomplete outcome data; contamination; blinding; selective outcome reporting; and other potential sources of bias.

Measures of treatment effect

We reported data in natural units for each study. For RCTs, we reported results as (1) Absolute difference (mean or proportion of outcome in intervention group minus control at study completion); (2) Relative percentage difference (absolute difference divided by post‐intervention score in the control group). We undertook meta‐analysis where appropriate in terms of participants, interventions and outcomes using random‐effects models. Analyses were undertaken for clinical outcomes (glycaemic control and blood pressure) and depression scores in the comorbidity studies. We also undertook meta‐analyses for HRQoL and self‐efficacy in all studies in which these were reported. All meta‐analyses apart from self efficacy had significant statistical heterogeneity so we present the figures for these analyses without the pooled estimates of effect.

Standardised effect sizes (SES) are presented in tables where possible, i.e. where studies reported relevant data for their calculation. We have reported the range of effects using SES in the text of the results and used the generally accepted convention that an SES of more than 0.2 indicates a small intervention effect, an SES of more than 0.5 indicates a moderate intervention effect and an SES of more than 0.8 is a large effect size (Cohen 1988).

For ITS we had planned to report two effect sizes:
(1) The change in the outcome immediately after the introduction of the intervention.
(2) The change in the slope of the regression lines.

However, no ITS studies were identified.

Unit of analysis issues

None of the included studies had unit of analysis errors.

Dealing with missing data

If data on multimorbidity sub‐groups were missing from potentially eligible studies, we contacted authors to obtain the information. Two studies provided additional data on sub‐groups with multimorbidity (Coventry 2015; Eakin 2007). We did not include any studies with more than 20% missing data in meta‐analyses and did not make any assumptions regarding missing data.

Assessment of heterogeneity

We assessed included studies in terms of clinical and statistical heterogeneity. Statistical heterogeneity was assessed by examining forest plots and considering the I² statistic (Cochrane Handbook). We planned to prepare tables and funnel plots comparing effect sizes of studies grouped according to potential effect modifiers (for example, simple versus multifaceted interventions) if sufficient studies had been identified but this was not possible.

If there had been enough studies, we had planned to use meta‐regression to see whether the effect sizes could be predicted by study characteristics. These could, for example, include duration of the intervention, age groups, and simple versus multifaceted interventions (Cooper 1994). We also considered formal tests of homogeneity (Petitti 1994). None of these quantitative methods were possible for this version of the review but will be considered for future review updates.

Assessment of reporting biases

We assessed incomplete reporting of outcomes, where possible, within the 'Risk of bias' tables. This was only possible for studies that had published protocols or specifically reported different results than the outcomes mentioned in the methods sections of included papers.

Data synthesis

We expected that included studies would measure similar outcomes using different methods. These included either continuous variables (such as different depression scales) or dichotomous process measures (such as proportion of people with recovery from depression). For continuous outcomes, we reported means and standard deviations at study completion with the absolute difference and relative percentage difference. We calculated standardised effect sizes for continuous measures by dividing the difference in mean scores between the intervention and comparison group in each study, by an estimate of the pooled standard deviation. For categorical outcomes, we reported the proportions in the intervention and control groups with the absolute difference and relative percentage difference.

We undertook meta‐analysis of studies that were similar in terms of settings, participants, interventions, outcome assessment and study methods. If there was a high I² indicating statistical heterogeneity, we used graphs to illustrate the results but did not present the combined effects as the heterogeneity indicates that combining the studies in a meta‐analysis is inappropriate. Where meta‐analysis was not possible we carried out a narrative synthesis of the results and presented the results based on outcome groupings. See Additional tables.

We assessed the certainty of the evidence for the main outcomes using the following GRADE (Grading of Recommendations Assessment, Development and Evaluation) criteria (Guyatt 2008); and present the main findings with our judgments in a 'Summary of findings' table

1. Study limitations (i.e. risk of bias).
2. Consistency of effect.
3. Imprecision.
4. Indirectness.
5. Publication bias.

Subgroup analysis and investigation of heterogeneity

We had planned to consider subgroup analyses based on the degree of multimorbidity of participants estimated by the number of conditions per person. These analyses were not possible due to the variation in definitions of multimorbidity and characteristics of participants across studies.

Sensitivity analysis

We planned to undertake sensitivity analyses based on intervention type or clear distinctions in studies with different risk of bias but this was not possible due to the limited number of meta‐analyses undertaken with each containing relatively few studies.

Summary of findings and assessment of the certainty of the evidence

We assessed the certainty of the evidence for the main outcomes using the following GRADE (Grading of Recommendations Assessment, Development and Evaluation) criteria (Guyatt 2008) including risk of bias, consistency of effect, imprecision, indirectness and other potential criteria such as publication bias. We presented the main findings with our judgments in a 'Summary of findings' table. We used the EPOC guidance for preparing a Summary of Findings (SoF) table using GRADE (EPOC 2017a). We included the following outcomes in the Summary of Findings Table: HRQoL, mental health outcomes, clinical outcomes, other patient reported outcomes, health behaviours, healthcare utilisation, medicines outcomes and provider behaviour.

We have included worksheet that outline the decision making process when applying GRADE for the three comparisons (Appendix 2; Appendix 3; Appendix 4).

Results

Description of studies

Results of the search

The electronic searches yielded 30,206 original citations after duplicates were removed (Figure 1). Of these, 30,075 citations were irrelevant and directly excluded. Full texts were retrieved for 131 studies. Of these, 60 studies from 93 papers were excluded with reasons Characteristics of excluded studies. Fifteen studies are on‐going (Characteristics of ongoing studies) ,. Seventeen studies from 19 papers were eligible for inclusion in this review and four other studies are awaiting classification.(Characteristics of studies awaiting classification).


Flow Diagram

Flow Diagram

Included studies

See Characteristics of included studies table

Study design

We identified 17 RCTs eligible for inclusion in the review, 9 from the original review (Bogner 2008; Boult 2011; Eakin 2007; Hochhalter 2010; Hogg 2009; Katon 2010; Krska 2001; Lorig 1999; Sommers 2000;) and 8 identified in this update (Barley 2014; Coventry 2015; Garvey 2015; Kennedy 2013; Lynch 2014; Martin 2013; Morgan 2013; Wakefield 2012). No other study designs with eligible interventions were identified.

Population/participants

There were a total of 8408 participants across all studies. The interventions varied in duration from eight weeks to two years, with the majority lasting 6 to 12 months. There was also variation in post intervention follow‐up, varying from immediate follow‐up to follow‐up 12 months post intervention cessation.

Eight of the 17 studies recruited participants with a broad range of conditions (Boult 2011; Eakin 2007; Garvey 2015; Hochhalter 2010; Hogg 2009; Krska 2001; Lorig 1999; Sommers 2000), whereas the remaining nine focused on the following comorbidities: depression and hypertension (Bogner 2008); depression and diabetes and/or heart disease (Barley 2014; Coventry 2015; Morgan 2013; Katon 2010); depression and headache (Martin 2013); diabetes and hypertension (Lynch 2014; Wakefield 2012); and a sub‐group of people with at least two of diabetes, chronic obstructive pulmonary disease and irritable bowel syndrome (Kennedy 2013).

Settings

All studies were set in primary care or community settings in the USA, apart from Krska 2001 which was set in the UK National Health Service and Hogg 2009 which was set in Canada. Seven were funded by a government or university grant (Coventry 2015; Garvey 2015; Hogg 2009; Katon 2010; Kennedy 2013; Krska 2001; Lorig 1999); and the remaining studies were funded by charitable foundations. None were funded directly by the pharmaceutical industry.

Comparison intervention

In the majority of included studies, the comparator was usual medical care which in some studies was supplemented by a newsletter or leaflet (Eakin 2007;), or involved a baseline assessment but no follow‐on intervention (Bogner 2008; Garvey 2015; Katon 2010; Krska 2001). These minimal additions to usual care could be considered as being within the variation of usual care provided in different settings. One study invited those allocated to a control group to attend a group session based on an unrelated topic (Hochhalter 2010). This was an attempt to ensure that the intervention effect did not relate to the group setting but related to the intervention content.

Description of interventions

The interventions were all multifaceted and brief descriptions for each study are provided in the Characteristics of included studies. No study specifically reported consumer involvement in the intervention design.

As outlined in the methods, we used the EPOC taxonomy of interventions to describe and categorise the interventions tested in these studies (EPOC 2002). While the interventions identified all involved multiple components they could be divided broadly into two main groups. In 12 of 17 studies, the interventions were primarily organisational, for example case management or addition of a pharmacist to the clinical care team (Barley 2014; Bogner 2008; Boult 2011; Coventry 2015; Hogg 2009; Katon 2010; Kennedy 2013; Krska 2001; Martin 2013; Morgan 2013; Sommers 2000; Wakefield 2012). In the remaining five studies, the interventions were primarily patient‐oriented, for example self‐management support groups (Eakin 2007; Garvey 2015; Hochhalter 2010; Lorig 1999; Lynch 2014). However, there were overlapping elements with some organisational‐type studies including patient‐oriented elements such as education provided by a case manager and vice versa. No study involving financial or regulatory type interventions were identified. We have included an additional table which outlines intervention elements and indicates which elements featured in each of the included studies (Table 1)

Excluded studies

We excluded 75 studies in total, see Characteristics of excluded studies. Thirty‐five studies were excluded on the basis of ineligible participants. In some of these studies there was a potential multimorbidity sub‐group but these data were not reported or not available from authors when requested. Twenty‐six studies were excluded on the basis of an ineligible intervention. This was usually because it was conducted in a specialist setting or had a single‐condition focus despite participants having multiple conditions. The remaining studies were excluded on the basis of study design, largely due to absence of control groups.

Risk of bias in included studies

See Characteristics of included studies table, Figure 2 and Figure 3 for a summary assessment of the risk of bias of the included studies. Overall three of the 17 studies reported all elements for the risk of bias domains. Two studies reported domains with a high risk of bias and in 13 studies there were domains classified as unclear due to lack of reporting.


Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.


Risk of bias summary: review authors' judgements about each risk of bias item for each included study.

Risk of bias summary: review authors' judgements about each risk of bias item for each included study.

Allocation concealment was assessed as adequate in eight of the 17 studies (Barley 2014; Boult 2011; Coventry 2015; Garvey 2015; ; Hogg 2009; Katon 2010; Krska 2001; Sommers 2000), but was assessed as unclear in the remainder. Baseline measurement of outcomes was carried out in all studies. All reported adequate follow‐up of participants except Lorig 1999 and Wakefield 2012 where the risk of bias was assessed as unclear. Lorig 1999 did not provide specific details pertaining to follow‐up for the multimorbidity subgroup, although follow‐up for the overall study was assessed as adequate. There was high risk of bias in Martin 2013 with poorer follow‐up in the intervention group (57%) compared to the control group (80%) at study completion. Objective outcomes were used in all but two studies, Krska 2001 and Hogg 2009, where this dimension was assessed as unclear. Krska 2001 used a measure detailing pharmaceutical care issues (PCIs) which was a previously developed classification system modified for the study. Hogg 2009 collected data on chronic and preventive care delivery from individuals' records but the accuracy of this process was not described. Blinding of outcome assessment was assessed as done in six studies (Boult 2011; Coventry 2015;; Hochhalter 2010; Katon 2010; Lorig 1999; Sommers 2000). It was assessed as unclear in nine studies (Barley 2014; Bogner 2008; Eakin 2007; Hogg 2009; Lynch 2014; Martin 2013; Morgan 2013; Wakefield 2012); and assessed as not done in Garvey 2015 and Krska 2001.

Five of the 17 studies had a cluster design that ensured no contamination of control participants (Boult 2011; Coventry 2015; Kennedy 2013; Morgan 2013; Sommers 2000). Contamination of participants allocated to the control group was unlikely in a further seven studies where the intervention was directed at recipients rather than providers (Barley 2014; Bogner 2008; Garvey 2015; Eakin 2007; Lorig 1999; Lynch 2014; Hochhalter 2010), but was possible in the remaining studies four studies (Hogg 2009; Katon 2010; Martin 2013; Wakefield 2012). However, Katon 2010 provided an appendix outlining potential contamination and indicated that it was minimal and, if it had occurred, it would have diluted rather than increased the significant effect size of their intervention. Krska 2001 stated that contamination of control participants who attended the same general practitioners (GPs) as the intervention participants could have occurred but that a cluster design would have been more problematic due to differential prescribing patterns between practices. All studies had low risk of selective outcome reporting and had no apparent other biases.

The five cluster randomised controlled trials accounted for clustering effects in their analysis so there were no unit of analysis errors (Boult 2011; Coventry 2015; Kennedy 2013; Morgan 2013; Sommers 2000).

Certainty of the evidence

See summary of findings Table 1. In general, while all the included studies were RCTs the main limitation related to a lack of consistency of effect for most outcomes. Only the mental health outcomes, largely relating to depression in the comorbidity studies, were regarded as having a high GRADE ranking. We downgraded the evidence for effects on clinical and patient‐reported outcomes to moderate due to lack of consistency of effect across studies and small effect sizes. We downgraded the evidence for effects on provider behaviour to moderate, due to limited available data for calculation of standardised effect sizes (SES) and lack of clarity regarding the clinical importance of the results. We downgraded the evidence for effects on health service utilisation and medication use and adherence to low, due to variation across studies and small effect sizes. We did not include economic outcomes in the summary of findings Table 1 due to the lack of robust economic analyses, rather we summarised this outcome in Table 2.

Open in table viewer
Table 2. Costs

Study

Study type

Outcome

Result

Notes

Barley

RCT

Cost‐effectiveness

The intervention demonstrated marginal cost effectiveness up to a QALY threshold of GBP 3035

Boult

RCT

Total healthcare cost

Saving of USD 75,000 per GCN and USD 1364 per participant

USD in 2007

Initial result only

ns

Katon

RCT

Cost‐effectiveness

Mean reduction of 114 days in depression free days and an estimated difference of 0.335 QALYs (95% CI −0.18 to 0.85). The intervention was associated with lower OPD costs with a reduction of USD 594 per participant (95% CI USD −3241 to USD 2053).

Non‐significant but 99.7% probability that the intervention met the threshold of < USD 20,000 per QALY

Krska

RCT

Mean cost of medicines

Int: 38.83

Con: 42.61

Absol diff 3.78

Rel %diff 9%

GBP in 2000

ns

SES = 0.13

Lorig

RCT

Intervention cost per completed participant

USD 70

USD in 1998

See text for assumptions made

Lorig

RCT

Cost savings per individual

USD 750

USD in 1998

See text for assumptions made

Sommers

RCT

Savings per individual

USD 90

USD in 1994

See text for assumptions made

* refers to whether original study reported statistically significant improvement in this outcome

Effects of interventions

See: Summary of findings 1 Summary of findings

Effects by type of interventions

We have presented an overview of intervention components for each study, highlighting the main intervention component in bold text and have included a brief summary of the intervention effect on the study primary outcomes in Table 1. The description of intervention components is based on reporting of intervention components in each paper and this is not consistent across studies. For example, most studies were likely to have included training of practitioners involved in interventions but not all studies reported this as an intervention component. We have also presented an overview of results based on whether the studies addressed general multimorbidity or comorbidity in Table 3.

Open in table viewer
Table 3. Overview of outcomes

Outcome category

Outcome

No. studies with this outcome

No. studies with p< 0.05 for this outcome

 

Physical Health

Hb1Ac

5

2

 

BP

6

2

 

Cholesterol

2

1

 

Other symptom score

 

4

1

Mental Health

Depression scores

8

6

 

% improved depression

1

1

 

Anxiety scores

4

3

 

Cognitive symptom management

1

0

 

Psychosocial

QoL/general health

10

4

 

Functional impairment & disability

5

2

 

Social (activity/support)

4

1

 

Self efficacy

7

3

 

Home hazards

1

0

 

Health service use

Visits/use service

5

0

 

Hospital admission related

6

2

 

Patient health related behaviours

Exercise/diet

6

2

 

Medication adherence

5

2

 

Provider behaviour

Prescribing

3

2

 

Disease management

3

3

 

Costs

Direct costs or cost effectiveness

6

Not applicable

 

* Multimorbidity is defined as two or more independent conditions within the same individual whereas comorbidity refers to linked conditions. In this review comorbidity studies included depression and diabetes or depression and hypertension

** The scales or measurements used in each study for the outcomes are described in the Table of included studies

Organisational interventions

Twelve of the 17 included studies had organisational‐type interventions (Barley 2014; Bogner 2008; Boult 2011; Coventry 2015; Hogg 2009; Katon 2010; Kennedy 2013; Krska 2001; Martin 2013; Morgan 2013; Sommers 2000; Wakefield 2012). These predominantly involved case management and coordination of care or the enhancement of skill mix in multidisciplinary teams in addition to delivery of patient care.

1. Clinical outcomes

Eight of the 12 organisational type studies reported clinical outcomes. These studies had a range of standardised effect sizes (SES) varying from 0.01 to 1.6. Interventions aimed at improving management of risk factors in comorbid conditions were more likely to have larger effect sizes (e.g. Bogner 2008; Katon 2010; Morgan 2013).

Five studies reported six measures of glycaemic control (five mean HbA1c and one study reported percentage achieving at least 0.5% reduction in HbA1c). Katon 2010 and Morgan 2013 reported improvements in mean HBA1c; however, Morgan 2013 had a substantial proportion of missing HbA1c data at study completion so these data were not included in the meta‐analysis of HbA1c. Hogg 2009, Lynch 2014 and Wakefield 2012 found little or no difference in HbA1c.Lynch 2014 The SES ranged from 0.05 to 0.38 and none of these studies had an SES greater than 0.5. The mean difference (MD) was ‐0.193 (95% CI −0.47 to 0.10) as outlined in Figure 4.


Forest plot of comparison: 1 Glycaemic control (HbA1c) Diabetes outcome: 1.1 HbA1c at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 1 Glycaemic control (HbA1c) Diabetes outcome: 1.1 HbA1c at immediate to 6 months post‐intervention follow up.

Five studies reported on systolic blood pressure (SBP). Bogner 2008 and Katon 2010 reported improvements in blood pressure, although this was of minimal clinical significance in Katon 2010. Morgan 2013 and Wakefield 2012 reported little difference. The standardised effect sizes (SES) ranged from 0.01 to 0.78 but only one of these four studies had an SES greater than 0.5. The MD was −3.10 (95% CI −7.26 to 1.06) as illustrated in Figure 5.


Forest plot of comparison: 2 Systolic Blood Pressure: outcome: 2.1 Systolic blood pressure at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 2 Systolic Blood Pressure: outcome: 2.1 Systolic blood pressure at immediate to 6 months post‐intervention follow up.

Two studies reported on cholesterol. Katon 2010 found a reduction in LDL cholesterol, whereas Morgan 2013 found no meaningful difference (SES ranges 0.22 to 0.26). Katon 2010 reported a composite primary outcome that combined three risk factors, which showed an improvement in intervention participants compared to control (see Table 4).

Open in table viewer
Table 4. Clinical Outcomes

Study

Study type

Outcomes

Results

Notes

Barley

RCT

% with angina (Rose Angina score)

Int 22/31 Con 30/37

Absol diff 8, Rel % diff 27%

ns

Bognor

RCT

Systolic BP

Int 127.3 (SD 17.7) Con 141.3 (SD 18.8)

Absol diff 14, Rel % diff 10%

*

SES = 0.78

Bognor

RCT

Diastolic BP

Int 83 (SD 10.7) Con 81.4 (SD 11.1)

Absol diff 9.2, Rel % diff 11%

*

SES = 0.15

Hogg

RCT

Systolic BP

Int 124.3 Con 124.2

Absol diff 0.1, Rel % diff < 0.1%

ns

(No SDs

reported)

Hogg

RCT

HbA1c

Int 7.01 Con 6.78

Absol diff 0.23, Rel % diff 3%

ns

Katon

RCT

Systolic BP

Int 131 (SD 18.4) Con 132.3 (SD 17.2)

Absol diff 1.3, Rel % diff 1%

*

SES = 0.07

Katon

RCT

HbA1c

Int 7.33 (SD 1.21) Con 7.81 (SD 1.9)

Absol diff 0.48, Rel % diff 6%

*

SES = 0.32

Katon

RCT

Cholesterol

Int 91.9 (SD 36.7) Con 101.4 (SD 36.6)

Absol diff 9.5, Rel % diff 9%

*

SES = 0.26

Katon

RCT

Composite: all three risk factors

(BP, HbA1c and cholesterol) below guidelines

Int 36/97 (0.37) Con: 19/87 (0.22)

Absol diff 15, Rel % diff 68%

*

Lorig

RCT

Pain/ physical discomfort

Int 59.8 (SD 20.1) Con 60.6 (SD 17.1)

Absol diff 0.8, Rel % diff 1%

SES = 0.04

ns

Lorig

RCT

Energy/fatigue

Int 2.18 (SD 0.73) Con 2.02 (SD 0.75)

Absol diff 0.16, Rel % diff 8%

ns

Lorig

RCT

Shortness of breath

Int 1.34 (SD 0.91) Con 1.58 (SD 0.83)

Absol diff 0.24, Rel % diff 15%

ns

Lynch

RCT

HbA1C

Int 7.4 (SD 1.6) Con 7.5 (SD 1.6)

Absol diff 0.1, Rel % diff 6.7%

ns

SES = 0.06

Lynch

RCT

% with at least 0.5 absolute reduction in HbA1c

Int 15/30 (0.05) 7/31 Con (0.21)

Absol diff 29, Rel % diff 138%

*

Lynch

RCT

Mean SBP

Int 135.8 (SD 21.4) Con 136.7 (SD 23)

Absol diff 0.9, Rel % diff 0.6%

ns

SES = 0.04

Morgan

RCT

HbA1C

Int 6.9 (SD 1.21) Con 7.4 (SD 1.42)

Absol diff 0.5, Rel % diff 6.7%

*

SES = 0.38

Morgan

RCT

Systolic BP

Int 132.4 (SD 19) Con 131.2(SD 19.6)

Absol diff 1.2, Rel % diff 0.9%

ns

SES = 0.02

Morgan

RCT

Cholesterol

Int 4.22 (SD 0.94) Con 4.44 (SD 1.06)

Absol diff 0.22, Rel % diff 5%

ns

SES = 0.22

Morgan

RCT

Mean BMI

Int 31.2 (SD 6) Con 31.0 (SD 6)

Absol diff 0.2, Rel % diff 0.6%

ns

SES = 0.03

Sommers

RCT

Symptom scores

Int 17.2 Con 18.9

Absol diff 1.7, Rel % diff 9%

ns

Wakefield

RCT

HbA1c

Int 6.9 (1.1) Con 6.95 (1.1)

Absol diff 0.05, Rel % diff 0.7%

ns

SES = 0.05

Wakefield

RCT

Systolic BP

Int 133 (16.6) Con 137 (17.3)

Absol diff 4, Rel % diff 3%

ns

SES = 0.24

Martin

RCT

Mean headache rating

Int 0.63 (SD 0.5) Con 1.01 (SD 0.83)

Absol diff 0.38, Rel % diff 38%

*

SES = 0.58

* refers to whether original study reported statistically significant improvement in this outcome

** Total number with final data collected was 384. No final numbers of intervention and control participants presented.

Four studies reported symptom scores relating to clinical outcomes. Barley 2014, Lorig 1999 and Sommers 2000 found little or no difference whereas Martin 2013 reported improvements in mean headache rating (see Table 4).

2. Mental health outcomes

Seven studies presented data on mental health outcomes (Barley 2014; Bogner 2008; Coventry 2015; Katon 2010; Martin 2013; Morgan 2013; Sommers 2000). Five of the seven studies reported improvements in a range of depression measures whereas two showed no improvements in depression outcomes (Barley 2014; Sommers 2000). We undertook two meta‐analyses: a meta‐analysis of Patient Health Questionnaire, version 9 (HQ9) depression scores; and a meta‐analysis of standardised mean difference (SMD) in depression scores for the studies with available data where depression was a targeted condition. This suggests a modest intervention effect. The meta‐analysis for PHQ9 scores had high heterogeneity so we do not report the pooled effect (Figure 6). The SMD for other depression scores was −0.41 (95% CI −0.63 to −0.20) (Figure 7). The range in SESs for depression outcomes across these studies was from 0.09 to 1.18 with four of the nine outcomes indicating moderate to large effect sizes (i.e. SES > 0.5). These higher effect sizes were all reported in the studies in which depression was a focus of the intervention.


Forest plot of comparison: 3 Depression scores: 3.1 PHQ9 Depression scores at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 3 Depression scores: 3.1 PHQ9 Depression scores at immediate to 6 months post‐intervention follow up.


Forest plot of comparison: 4 Depression scores: 4.1 Depression scores at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 4 Depression scores: 4.1 Depression scores at immediate to 6 months post‐intervention follow up.

Three studies reported on anxiety measures, two showed improvements (Coventry 2015 and Martin 2013) whereas Barley 2014 reported little difference (see Table 5). There were small effect sizes in all studies (SES range 0.08 to 0.26).

Open in table viewer
Table 5. Mental Health Outcomes

Study

Study type

Outcome

Result

Notes

Barley

RCT

PHQ9 depression score

Int 12.6 (SD 7.1) Con 12 (SD 6.9)

Absol diff 0.6, Rel % diff 8%

ns

SES = 0.09

Barley

RCT

HADS depression score

Int 9.5 (SD 4.6) Con 8.8 (SD 4.8)

Absol diff 0.7, Rel % diff 8%

ns

SES = 0.15

Barley

RCT

HADS anxiety score

Int 9.9 (SD 7.1) Con 9.5 (SD 5.4)

Absol diff 0.4, Rel % diff 4%

ns

SES = 0.08

Bognor

RCT

CES depression score

Int 9.9 (SD 10.7) Con 19.3 (SD 15.2)

Absol diff 9.4, Rel % diff 49%

*

SES = 0.75

Coventry

RCT

SCL‐D13 depression score

Int 1.76 (SD 0.9) Con 2.02 (SD 0.9)

Absol diff 2.6, Rel % diff 13%

*

SES = 0.28

Coventry

RCT

PHQ9 depression score

Int 11.3 (SD 6.5) Con 13.1 (SD 6.5)

Absol diff 1.8, Rel % diff 14%

*

SES = 0.28

Coventry

RCT

GAD‐7 anxiety score

Int 8.2 (SD 5.8) Con 9.7 (SD 5.9)

Absol diff 1.5, Rel % diff 15%

*

SES = 0.26

Garvey

RCT

HADS total score

Int 15.6 (SD 8.3) Con 16.7 (SD 8.2)

Absol diff 1.1, Rel % diff 6.5%

ns

SES = 0.13

Katon

RCT

SCL 20 depression score

Int 0.83 (SD 0.66) Con 1.14 (SD 0.68)

Absol diff 0.31, Rel % diff 27%

*

SES = 0.46

Katon

RCT

Patient global improvement in depression

Int 41/92 Con 16/91

Absol diff 27, Rel % diff 150%

*

Lorig

RCT

Cognitive symptom management score

Int 1.75 Con 0.98

Absol diff 0.77, Rel % diff 79%

ns

Martin

RCT

PHQ9 depression score

Int 6.7 (SD 4.6) Con 12.6 (SD 5.3)

Absol diff 5.9, Rel % diff 47%

*

SES = 1.18

Martin

RCT

BDI ‐Depression score

Int 13.1 (SD 8.6) Con 28.7 (SD 9.5)

Absol diff 15.6, Rel % diff 54%

*

SES = 1.73

Martin

RCT

BAI Anxiety score

Int 10.5 (SD 10.8) Con 16.4 (SD 9.3)

Absol diff 5.9, Rel % diff 36%

*

SES = 0.1

Morgan

RCT

PHQ9 depression score

Int 7.1 (SD 4.7) Con 9.0 (SD 5.5)

Absol diff 1.9, Rel % diff 21%

*

SES = 0.37

Sommers

RCT

GDS score (depression)

Int 4.1 Con 4.1

Absol diff 0, Rel % diff 0%

ns

* refers to whether original study reported statistically significant improvement in this outcome

3. Patient‐reported outcome measures

Nine of the organisational‐type studies presented patient‐reported outcome measures (PROMs).

Nine of these reported a variety of HRQoL measures with a range of effects from SES of 0.03 to 0.84. Coventry 2015 Three studies reported small effect sizes (Coventry 2015;Katon 2010; Martin 2013). The remaining six studies reported little or no effect (Barley 2014; Hogg 2009; Kennedy 2013; Krska 2001; Morgan 2013; Sommers 2000). Krska 2001 and Morgan 2013 reported that SF36 scores had been analysed across eight domains at study completion and reported little or no difference between groups, but did not present actual data. The mixed evidence regarding HRQoL is illustrated in Figure 8 which includes studies with available data but the pooled effect is not reported due to high statistical heterogeneity (I² = 73%).


Forest plot of comparison: 5 Health related quality of life, outcome: 5.1 HRQoL at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 5 Health related quality of life, outcome: 5.1 HRQoL at immediate to 6 months post‐intervention follow up.

Five organisational studies reported on self‐efficacy with a range in SES of 0.03 to 0.11, suggesting minimal effect. (Barley 2014; Hochhalter 2010; Kennedy 2013; Wakefield 2012; Coventry 2015). We undertook a meta‐analysis of standardised mean self‐efficacy scores in comorbidity studies and found no effect, SMD −0.05 (95% CI −0.12 to 0.22) (Figure 9).


Forest plot of comparison: 6 Self‐Efficacy, outcome: 6.1 Self‐efficacy score at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 6 Self‐Efficacy, outcome: 6.1 Self‐efficacy score at immediate to 6 months post‐intervention follow up.

Two of the organisational studies reported measures relating to disability or impaired activities of daily living (IADL). Hogg 2009 reported no effect of interventions on IADL, whereas Coventry 2015 reported an improvement in the Sheehan Disability score in intervention participants.

Two of the organisational studies reported measures relating to Illness perceptions and both reported no effect (Barley 2014; Coventry 2015).

A range of other PROMs were also reported with mixed effects and none had an SES greater than 0.3. These are presented in Table 6.

Open in table viewer
Table 6. Patient‐reported outcome measures

Study

Study type

Outcome

Result

Notes

Health Related Quality of Life

Barley

RCT

SF12 PCS

Int 32.4 (SD 10.7) Con 33.3 (SD 9.2)

Absol diff 0.7, Rel % diff 2%

ns

SES = 0.07

Barley

RCT

SF12 MCS

Int 34.5 (SD 11.6 ) Con 33.6 (SD 12.5 )

Absol diff 0.9, Rel % diff 3%

ns

SES = 0.08

Barley

RCT

HRQoL (WEMWBS)

Int 40.6 (SD 11.2) Con 39.6(SD 12.3)

Absol diff 1, Rel % diff 2.5%

ns

SES = 0.08

Coventry

RCT

HRQoL (WHOQOL)

Int 2.99 (SD 0.6) Con 2.91 (SD 0.6)

Absol diff 0.08, Rel % diff 3%

*

SES = 0.13

Garvey

RCT

HRQoL (EQ5D VAS)

Int 65.7 (SD 20.2) Con 50.5 (SD 16.3)

Absol diff 15.2, Rel % diff 30%

*

SES = 0.84

Hogg

RCT

SF 36 Mental Health

Int 52.4 Con 52.2

Absol diff 0.2, Rel % diff 0.3%

ns

Hogg

RCT

SF 36 Physical Health

Int 44.3 Con 41.5

Absol diff 2.8, Rel % diff 6.7%

ns

Katon

RCT

QoL score

Int 6.0 (SD 2.2) Con 5.2 (SD 1.9)

Absol diff 0.8, Rel % diff 15%

*

SES = 0.44

Kennedy

RCT

HRQoL (EQ5D)

Int 0.56 (SD 0.34) Con 0.57 (SD 0.32)

Absol diff 0.01, Rel % diff 1%

ns

SES = 0.03

Lorig

RCT

Psychological well‐being

Int 3.47 Con 3.33

Absol diff 0.04, Rel % diff 4%

ns

SES = 0.21

Martin

RCT

HRQol (AQOL)

Int 26.3 (SD 4.76) Con 28.4 (SD 4.97)

Absol diff 2.1, Rel % diff 7 %

*

SES = 0.4

Sommers

RCT

SF36 score

Int 2.2 Con 3.3

Absol diff 1.1, Rel % diff 33%

ns

Self‐efficacy

Barley

RCT

Self‐efficacy score

Int 28.6 (SD 6.7) Con 27.9 (SD 8.1)

Absol diff 0.11, Rel % diff 2.5%

ns

SES = 0.09

Coventry

RCT

Self‐efficacy score

Int 5.72 (SD 1.9) Con 5.53 (SD 1.9)

Absol diff 0.18, Rel % diff 3.2%

ns *

SES = 0.09

Garvey

RCT

Self efficacy score

Int 6.8 (SD 1.5) Con 5.3 (SD 1.9)

Absol diff 1.47, Rel % diff 28%

*

SES = 0.86

Hochhalter

RCT

Self‐efficacy

Int 7.4 Con 8.0

Absol diff 0.6, Rel % diff 7.5%

ns

Kennedy

RCT

Self‐efficacy

Int 68 (SD 23.4) Con 68.7 (SD 23.1)

Absol diff 0.7, Rel % diff 1%

ns

SES = 0.03

Wakefield

RCT

Self‐efficacy

Int 8.1 (SD 1.9) Con 8.3 (SD 1.9)

Absol diff 0.2, Rel % diff 2.4%

ns

SES = 0.11

Daily function and disability

Coventry

RCT

Sheehan Disability Score

Int 5.73 (SD 2.8) Con 5.83 (SD 2.8)

Absol diff 0.1, Rel % diff 2%

*

SES = 0.04

Garvey

RCT

Frenchay Activities Index

Int 21.3 (SD 7.9) Con 18.9 (SD 7.2)

Absol diff 2.4, Rel % diff 13%

*

SES = 0.32

Garvey

RCT

Activities daily living: NEADL (total)

Int 47.2 (SD 11.9) Con 40.7 (SD 10.7)

Absol diff 6.5, Rel % diff 16%

*

SES = 0.58

Hogg

RCT

IADL

Int 10.6 Con 10.9

Absol diff 0.3, Rel % diff 2.7%

ns

Lorig

RCT

Disability

Int 0.86 Con 0.96

Absol diff 0.1, Rel % diff 10%

ns

Lorig

RCT

Social role/activity limitation

Int 1.91, Con 1.98

Absol diff 0.07, Rel % diff 4%

ns

Illness perceptions

Coventry

RCT

Multimorbidity illness perception scale

Int 2.1 (SD 0.9) Con 2.28 (SD 0.9)

Absol diff 0.18, Rel % diff 8%

ns

SES = 0.2

Barley

RCT

Illness perceptions (BIPQ)

Int 40 (SD 14.8) Con 43(SD 31.1)

Absol diff 3, Rel % diff 7%

ns

SES = 0.22

Social support

Coventry

RCT

Social support (ESSI)

Int 3.29 (SD 1.1) Con 3.4 (SD 1.0)

Absol diff 0.11, Rel % diff 3%

ns

SES = 0.11

Eakin

RCT

Multilevel support for healthy lifestyle

Int 2.7 Con 2.59

Absol diff 0.11, Rel % diff 4%

ns

Other PROMs

Barley

RCT

Patient‐reported needs (PSYCHLOPS)

Int 13.6 (SD 5.1) Con 13.4 (SD 5.4)

Absol diff 0.2, Rel % diff 1.5%

ns

SES = 0.04

Hochhalter

RCT

Total unhealthy days

Int 15.3 Con 14.1

Absol diff 1.2, Rel % diff 9%

ns

Hogg

RCT

Total unhealthy days

Int 7.6 Con 9.9

Absol diff 2.3, Rel % diff 23%

ns

Kennedy

RCT

Shared decision making (HCCQ)

Int 67.7 (SD 28) Con 69.3 (SD 26.1)

Absol diff 1.6, Rel % diff 2%

ns

SES = 0.06

Lorig

RCT

Self‐rated health

Int 3.42 Con 3.44

Absol diff 0.02, Rel % diff 0.6%

ns

Lorig

RCT

Health distress

Int 1.97 Con: 2.13

Absol diff 0.16, Rel % diff 7.5%

ns

SES = 0.16

Sommers

RCT

Social activities count

Int 8.7 Con:8.6

Absol diff 0.1, Rel % diff 1%

* (when adjusted

for baseline diff)

Sommers

RCT

HAQ score

Int 0.44 Con 0.5

Absol diff 0.06, Rel % diff 12%

ns

* refers to whether original study reported statistically significant improvement in this outcome

4. Utilisation of health services

Five organisational studies reported outcomes on health services utilisation (Boult 2011; Hogg 2009; Katon 2010; Krska 2001; Sommers 2000). Sommers 2000 reported improvements for intervention group participants across a variety of measures relating to hospital admissions, whereas Boult 2011, Hogg 2009, Katon 2010 and Krska 2001 found no difference in admission‐related outcomes, although numbers of admissions in most of these studies were very small.

Three studies reported data in relation to health service visits with a range of providers none of which showed clear improvements in appropriate health service use (Boult 2011; Hogg 2009; Sommers 2000) (see Table 7). No studies that included health service utilisation reported data that could be used to calculate SESs.

Open in table viewer
Table 7. Health service use

Study

Study type

Outcome

Result

Notes

Boult

RCT

No. hospital admissions

Int 0.7 Con 0.72

Absol diff 0.02, Rel % diff 3%

ns

Boult

RCT

No. days in hospital

Int 4.26 Con 4.49

Absol diff 0.23, Rel % diff 5%

ns

Boult

RCT

No. ED visits

Int 0.44 Con 0.44

Absol diff 0, Rel % diff 0

ns

Boult

RCT

No. PC visits

Int 9.98 Con 9.88

Absol diff 0.1, Rel % diff 1%

ns

Boult

RCT

No. specialist visits

Int 9.04 Con 8.49

Absol diff 0.55, Rel % diff 6%

ns

Boult

RCT

No. home healthcare episodes

Int 0.99 Con 1.3

Absol diff 0.31, Rel % diff 24%

*

Hogg

RCT

No. hospital admissions

Int 0.4 Con 0.46

Absol diff 0.06, Rel % diff 13%

ns

Hogg

RCT

Proportion hospitalised

Int 0.26, Con 0.26

Absol diff 0, Rel % diff 0%

ns

Hogg

RCT

No. ED visits

Int 0.63 Con 0.73

Absol diff 0.01, Rel % diff 14%

ns

Hogg

RCT

Proportion with ED visit

Int 0.38 Con 0.42

Absol diff 0.04, Rel % diff 9%

ns

Katon

RCT

Proportion hospitalised

Int 0.26 Con 0.22

Absol diff 0.04, Rel % diff 18%

ns

Lorig

RCT

No. doctor and ED visits

Int 6.51 Con 7.08

Absol diff 0.57, Rel % diff 8%

ns

Lorig

RCT

No. hospital stays in past 6 months

Int 0.26 Con 0.31

Absol diff 0.05, Rel % diff 6%

*

Lorig

RCT

No. nights in hospital in last 6 months

Int 1.3 Con 1

Absol diff 0.3 Rel % diff 30%

*

Sommers

RCT

No. hospital admissions per individual per year

Int 0.36 Con 0.52

Absol diff 0.16, Rel % diff 31%

*

Sommers

RCT

≥1 60 day readmission

Int 3.6 Con 9.4

Absol diff 5.8, Rel % diff 62%

*

Sommers

RCT

≥ 1 hospital admission

Int 8.8 Con 7.7

Absol diff 1.1, Rel % diff 14%

*

Sommers

RCT

No. PCP visits

Int 6.0 Con 6.1

Absol diff 0.1, Rel % diff 2%

ns

Sommers

RCT

No. office visits

Int 11 Con 12.5

Absol diff 1.5, Rel % diff 12%

*

Sommers

RCT

≥ 1 home care visit

Int 19.5 Con 18.8

Absol diff 0.7, Rel % diff 4%

ns

Sommers

RCT

No. medical specialist visits

Int 1.4 Con 1.7

Absol diff 0.3, Rel % diff 18%

ns

Sommers

RCT

No. other visits

Int 3.9 Con 4.3

Absol diff 0.4, Rel % diff 9%

*

Sommers

RCT

≥ 1 ED visit

Int 21.4 Con 16.7

Absol diff 4.7, Rel % diff

ns

* refers to whether original study reported statistically significant improvement in this outcome

5. Patient behaviour
5.1 Medication use and adherence

Four organisational studies reported measures relating to medication use and adherence. Two of these studies found an effect whereas two did not; and there was a range in SESs from 0.2 to 0.28 indicating minimal intervention effects. Bogner 2008 reported improvements in proportions of intervention participants adhering to both antidepressant and antihypertensive medication as measured using automated counting systems in the caps of medicine bottles (MEMS caps). Morgan 2013 reported a lower proportion of intervention participants were taking anti‐depressant medication. Martin 2013 reported on mean daily medication use which was not significantly different between intervention and control participants. Wakefield 2012 reported two measures of medication‐taking adherence both of which showed no significant difference; (see data in Table 8).

Open in table viewer
Table 8. Medication use and adherence and prescribing

Study

Study type

Outcome

Results

Notes

Bognor

RCT

≥80% adherence to antidepressant medication (MEMS caps)

Int 23/32 Con 10/32

Absol diff 0.41, Rel % diff 132%

*

Bognor

RCT

≥80% adherence to antihypertensive medication (MEMS caps)

Int 25/32 Con 10/32

Absol diff 0.47, Rel % diff 152%

*

Martin

RCT

Mean daily medication use

Int 2.4 (SD 3.2) Con 3.0 (SD 2.8)

Absol diff 0.6, Rel % diff 20%

ns

SES = 0.2

Morgan

RCT

% taking antidepressant medication

Int 34/62 Con 36/113

Absol diff 0.11, Rel % diff 34%

*

Wakefield

RCT

Adherence (Edward's scale)

Int 3.4 (SD 0.5) Con 3.3 (SD 0.5)

Absol diff 0.1, Rel % diff 3%

ns

SES = 0.2

Wakefield

RCT

Medication Taking Adherence Score

Int 100 (SD 1.4) Con 98.9 (SD 6.0)

Absol diff 1.1, Rel % diff 1%

ns

SES = 0.28

* refers to whether original study reported statistically significant improvement in this outcome

5.2 Health related behaviours

Three organisational studies provided data on a variety of outcomes relating to health behaviours by participants (Katon 2010; Morgan 2013; Sommers 2000). Katon 2010 found no difference in relation to adherence to diet and exercise. Morgan 2013 presented self‐report data on three patient‐behaviour outcomes with improvements in proportions of individuals exercising (Int 60% vs Con 29%) and in the proportions smoking (Int 8% vs Con 12%) and consuming alcohol (49% vs 64%). Sommers 2000 found no changes in a nutrition checklist score. No studies reporting health‐related behaviours reported data that could be used to calculate SESs; (see data in Table 9).

Open in table viewer
Table 9. Health‐related participant behaviours

Study

Study type

Outcome

Results

Notes

Hochhalter

RCT

PAM (patient activation measure)

Int 66.8 Con 66.2

Absol diff 0.6, Rel % diff 1%

ns

Eakin

RCT

Diet behaviour scores

Int 2.2 Con 2.41

Absol diff 0.21, Rel % diff 9%

*

Eakin

RCT

Change minutes of walking/week

Int +8 Con −10

Absol diff 18, Rel % diff 180%

*

Katon

RCT

General adherence to diet score

Int 0.86 Con 0.81

Absol diff 0.05, Rel % diff 6%

ns

Katon

RCT

General adherence to exercise score

Int 0.54 Con 0.44

Absol diff 0.1, Rel % diff 23%

ns

Lorig

RCT

Exercise: stretching and strengthening (mins/week)

Int 53.1 Con 40.4

Absol diff 12.7,Rel % diff 31%

ns

Lorig

RCT

Exercise: aerobic (mins/week)

Int 101.8 Con 88

Absol diff 13.8, Rel % diff 157%

ns

Lorig

RCT

Communication with doctor

(score 1‐5)

Int 3.34 Con 3.2

Absol diff 0.14, Rel % diff 4%

ns

Lynch

RCT

Physical activity (kcal expenditure per week, CHAMPS)

Int 1913.6 Con −603

Absol diff 2516, Rel % diff 417%

*

Morgan

RCT

Smoking

Int 13/162 Con 13/110

Absol diff 0.04, Rel % diff 33%

ns

Morgan

RCT

Alcohol

Int 51/104 Con 27/42

Absol diff 0.15, Rel % diff 23%

ns

Morgan

RCT

Exercise (30 minutes/day for 5 days/ week)

Int 97/162 Con 22/75

Absol diff 0.31, Rel % diff 106%

*

Sommers

RCT

Nutrition checklist score

Int 2.0 Con1.9

Absol diff 0.1, Rel % diff 5%

ns

* refers to whether original study reported statistically significant improvement in this outcome

6. Provider behaviour
6.1 Prescribing

Two organisational studies reported measures relating to practitioner prescribing or medicines management, both of which indicated significant benefits for intervention participants. Katon 2010 reported a measure examining one or more medication adjustments for five classes of drugs related to the comorbid conditions being studied and reported differences for four of these five groups. Krska 2001 reported a reduction in pharmaceutical care issues in intervention participants; (see data in Table 8)

6.2 Other provider behaviours

Provider behaviours relating to chronic disease management or preventive care were reported in four organisational studies. Boult 2011 and Coventry 2015 both presented a validated measure called the Patient Assessment of Chronic Illness Care (PACIC) score, which is a patient assessment of the quality of care received. This score includes five elements and the aggregate quality score derived was improved in the Boult 2011 Guided Care study, and a small effect reported by Coventry 2015 study (SES 0.39). Hogg 2009 reported measures relating to chronic disease management and preventive care based on chart data and both were improved in the intervention group. Morgan 2013 reported on the proportions of particpants referred to exercise and mental health programmes which was higher in intervention than control group participants; (see data in Table 10).

Open in table viewer
Table 10. Provider behaviour

Study

Study type

Outcome

Result

Notes

Boult

RCT

PACIC score

(patient measure of quality of care received)

Int 3.14 Con 2.85

Absol diff 0.29, Rel % diff 10%

*

Coventry

RCT

PACIC score

Int 2.37 (SD 1.1) Con 1.98 (SD 1.0)

Absol diff 0.39, Rel % diff 20%

ns

SES = 0.39

Hogg

RCT

Chronic Disease Mangement Score

Int 0.84 Con 0.77

Absol diff 0.07, Rel % diff 9%

*

Hogg

RCT

Preventive Care Score

Int 0.89 Con 0.7

Absol diff 0.19, Rel % diff 27%

*

Krska

RCT

% Pharmaceutical care issues resolved from baseline

Int 950/1206 Con 542/1380

Absol diff 0.4, Rel % diff 102%

*

Morgan

RCT

% Referred to mental health

Int 58/162 Con 10/111

Absol diff 0.27, Rel % diff 300%

*

Morgan

RCT

% Referred to exercise programme

Int 58/162 Con 24/114

Absol diff 0.15, Rel % diff 71%

*

* refers to whether original study reported statistically significant improvement in this outcome

7. Acceptability of services

Three organisational studies reported treatment satisfaction. Katon 2010 reported the proportion of participants moderately to very satisfied with treatment for depression and diabetes and heart disease at study completion. More intervention participants were satisfied with their care at study completion compared to those experiencing usual care. Boult 2011 reported on the changes in satisfaction for providers as part of an overall examination of the effect of the intervention on providers. The measure incorporated changes in 11 domains of satisfaction with service provision; five domains relating to time spent with participants; six domains relating to provider knowledge of the participant; and four domains relating to care coordination. The only changes reported in the study were improvements in 5 of the 11 domains relating to satisfaction with service provision. Coventry 2015 reported mean Client Satisfaction Scores and reported no difference between intervention and control group participants.

8. Costs

Five organisational studies provided data on costs (Barley 2014; Boult 2011; Katon 2010; Krska 2001; Sommers 2000).

Barley 2014 undertook a parallel economic analysis of the UPBEAT intervention and found that the intervention demonstrated marginal cost effectiveness up to a quality‐adjusted life‐year (QALY) threshold of GBP 3035.

Leff 2009 et al provided initial cost data from Boult 2011 and indicated a saving related to Guided Care of USD 75,000 per guided care nurse (95% CI USD −244,000 to USD 150,900) or USD 1364 per individual. However, these initial results were based on small changes in outcomes with wide confidence intervals. In addition, the final study results were subsequently published and indicated no intervention effect.

Katon 2010 reported the direct mean medical costs relating to the TeamCare intervention over a 12 month period as USD 1224 per individual. A subsequent economic analysis reported that the intervention led to an additional 114 days in depression‐free days and an estimated difference of 0.335 QALYs (95% CI −0.18 to 0.85) (Katon 2012). The intervention was associated with lower OPD costs with a reduction of USD 594 per person (95% CI USD −3241 to USD 2053). There was a 99.7% probability that the intervention met the threshold of less than USD 20,000 per QALY. The authors interpreted this as a high value intervention but this must be interpreted with caution given the wide confidence intervals in the estimates with lack of statistical significance.

Krska 2001 reported the mean medicine cost for the intervention and control groups at study completion and found a marginal benefit for the intervention.

Sommers 2000 reported a net saving per intervention participant of USD 90 though this did not include additional savings from fewer physician visits. It also excluded the costs of implementing the intervention, stated to be USD 118,950, mainly relating to salary costs; (see Table 2).

Patient‐oriented interventions

Five of the 17 included studies had predominantly patient‐oriented interventions, for example education or group‐based self‐management support courses (Eakin 2007; Garvey 2015; ; Hochhalter 2010; Lorig 1999; Lynch 2014). All five aimed to address participant health‐related behaviour and did not engage or involve individuals' current health‐care providers directly. The results from these five studies were mixed and do not suggest that patient‐oriented interventions are generally effective. However, there was an indication that a focus on functional capacity and activity participation may possibly be effective (Garvey 2015)Gitlin 2009Gitlin 2006.

1. Clinical outcomes

Two of the five patient‐oriented studies reported clinical outcomes with mixed results. Gitlin 2009Gitlin 2009). Lorig 1999 reported three measures relating to clinical outcomes all of which showed little or no difference between intervention and control. Lynch 2014 reported on glycaemic and blood pressure control in people with diabetes and hypertension. Mean HbA1c was no different but there was an increase in the proportion of intervention participants who achieved at least an absolute reduction in HbA1c of 0.5%. There was no or little difference in systolic blood pressure; (see Table 4). SESs for clinical outcomes in these studies ranged from 0.01 to 0.31 indicating minimal intervention effects.

2. Mental health outcomes

Two studies presented data on mental health outcomes (Garvey 2015 and Lorig 1999). Garvey 2015 reported Hospital Anxiety and Depression Scores (HADS) and found no overall difference in total HADS scores but modest improvements in the depression and anxiety scores. Lorig 1999 reported a mean difference of 0.77 points on a scale of 0 to 5, suggesting no difference in cognitive symptom management between groups at study completion; (see Table 5).

3. Patient‐reported outcome measures

Four studies reported PROMs (Eakin 2007; Garvey 2015; Hochhalter 2010; Lorig 1999). Garvey 2015's primary and secondary outcomes reflected the occupational therapy basis of the intervention. The intervention was associated with improvements in all three reported occupational participation/functional ability‐type measures. Garvey 2015 also found improvements in HRQol and self‐efficacy but no improvements in the Health Education Impact questionnaire overall. The results relating to HRQol and self‐efficacy are included in the related meta‐analyses (Figure 8; Figure 9). Results of this study have to be interpreted with caution as it is reported as an exploratory trial with immediate post‐intervention follow‐up. A definitive RCT is planned (Garvey 2015). Gitlin 2009Garvey 2015 (see Table 6).

4. Utilisation of health services

Two studies reported outcomes on health services utilisation (Garvey 2015 and Lorig 1999). Garvey 2015 found no difference in primary care visits and hospital admissions although only examined an eight week time frame in a small sample. Lorig 1999 reported improvements for intervention group participants across a variety of measures relating to hospital admissions. Lorig 1999 also reported on primary care and emergency department visits but found no improvements (no data available to calculate SESs); (see Table 7).

5. Patient behaviour
5.1 Medication use and adherence

No study with a patient‐oriented intervention reported on medication use and adherence.

5.2 Health related behaviours

Three studies provided data on a variety of outcomes relating to health behaviours by participants (Eakin 2007, Lorig 1999 and Lynch 2014). Eakin 2007 reported improvements in diet behaviour scores and in changes in minutes of walking per week. Lorig 1999 reported three measures relating to exercise and communication with doctors and while there was moderate differences in favour of the intervention groups these were unlikely to be of clinical significance; (see Table 9). Lynch 2014 reported increased exercise measured by caloric expenditure in the intervention group. There were no data presented to calculate SESs.

6. Provider behaviour
Prescribing and other provider behaviours

No study with a patient‐oriented intervention reported on provider behaviour.

7. Acceptability of services

No study with a patient‐oriented intervention reported on acceptability of services.

8. Costs

One of these studies provided data on costs (, Lorig 1999).

Gitlin 2009

Lorig 1999 reported the mean direct cost of running the course for participants who completed it, although costs did not include the cost of accommodation as this was donated to the study. The significant reduction in hospital admissions shown by the intervention translated to a saving in healthcare costs per participant of USD 750 which the authors point out is ten times the cost of the intervention. This calculation was based on a presumed cost of USD 1000 per day if admitted to hospital (see Table 2).

Discussion

Summary of main results

We have identified 17 studies eligible for inclusion in the review, 9 from the original review and 8 added in the current update. All 17 were randomised controlled trials with a generally low risk of bias. Even within this small number of studies, there was significant variation in participants and interventions. In nine of the 17 studies, the focus was on comorbid conditions, which were eligible for inclusion as their interventions had a multimorbidity focus in that they were directed at the pre‐specified comorbid conditions. The commonest combinations of conditions included depression, diabetes and cardiovascular disease. In the other studies, which included people with general multimorbidity, the focus tended to be on older individuals.

The results suggest that interventions that are targeted at specific risk factor management (for example management of vascular risk factors and depression in people with comorbid vascular disease and depression) or focused on areas where people have difficulties, such as with functional ability or medicines management, may possibly be more likely to be effective. Given the importance of developing interventions for people with multimorbidity, the review provides interesting insights into the types of intervention components that are being examined. However, the majority of interventions in included studies had multiple components incorporating different elements, making comparison of intervention effects difficult. We categorised the intervention components using the EPOC taxonomy and identified the predominant intervention element for each study and then grouped studies depending on whether they had a predominantly organisational or patient focus. When examining the effectiveness of interventions by intervention type, we concluded that organisational type interventions, for example, the introduction of clinical nurse specialists to support treatment of depression or a focus on specific risk factor management in commonly co‐occurring conditions such as diabetes and hypertension may be more effective. Interventions that target areas where people have particular difficulties, such as functional ability, are also more likely to be effective. The current evidence suggests that organisational interventions that have a broader focus, such as case management or changes in care delivery for all individuals with multimorbidity, seem less effective. Patient‐oriented interventions that are not linked to healthcare delivery also seem less effective. Garvey 2015

We have presented results by outcomes pre‐specified in the protocol. In general these results were mixed and inconclusive, though there was a tendency for improvements in the studies that targeted common comorbid conditions that included depression. There was not a strong focus on clinical outcomes, particularly for the multimorbidity studies and this may reflect the challenge in research in multimorbidity when disease‐specific measures cannot be used.

There was limited effect on patient‐reported health outcomes such as HRQoL and on outcomes relating to health service utilisation and mixed effects on hospital admission rates and outcomes relating to medication use, and adherence. Five studies reported patient health behaviour outcomes with a tendency for these to be improved in the studies targeting comorbid conditions. There has been ongoing interest in the potential for improved patient self efficacy to lead to better self management and improved health outcomes. Self efficacy represents an outcome that is not disease or condition focused and was examined in many of the included studies. However, the majority of studies including this outcome showed no effect.

Costs were presented in six studies but only two studies conducted cost‐effectiveness analyses and it was not possible to compare outcomes across studies. The results relating to improved prescribing and risk factor management, in some of the comorbidity trials, indicate a potential for these interventions to reduce health service costs over longer periods of time.

Overall completeness and applicability of evidence

Most of the studies in this review are relatively recent reflecting the fact that this is a new area conceptually and that research to date has focused on description and impact rather than the evaluation of the effectives of interventions. The majority of newer studies included in this update and those studies identified as ongoing, focus on common comorbidities rather than on multimorbidity in general. In the original review (Smith 2012), only two of the ten included studies had interventions that focused on comorbid conditions whereas in this updated review, this has increased to nine of the 18 included studies. The tendency towards significant improvements in mental health outcomes in the comorbidity studies is likely related to the strong focus in these interventions on targeting the specific conditions involved, particularly depression. It is more challenging to design interventions for people with a broad range of conditions. The studies that seem more effective in the general multimorbidity group are those which had interventions targeted at specific areas of concern for participants, such as improving functional ability, which is not disease specific. One of the larger multimorbidity studies included involved a large well‐designed and executed RCT, the Guided Care study, which tested a broad organisational‐type intervention targeted at high risk individuals with multimorbidity, but which found no overall effect (Boult 2011). However, a pre‐planned sub‐group analysis indicated improvements in the use of some health services in the participants enrolled in one of the participating care plans (Kaiser‐Permanente, n = 365, 43% of full sample). Boult 2011 postulated that this result may have been related to the fact that care was already more organised and structured in this system, so that the Guided Care intervention may simply have extended the existing approaches used in that setting whereas its implementation was more challenging in less organised systems. However the results of sub‐group analysis, even when pre‐planned, need to be interpreted with caution given the relatively small samples sizes involved. Nonetheless, the differences in these sub‐groups highlight the importance of the healthcare delivery setting into which new interventions are added. Indeed, some of the patient‐oriented interventions seemed to run independently of people's healthcare delivery, particularly those that recruited participants from the community rather than through healthcare providers. Most of these studies had limited effectiveness, highlighting the importance of considering the overall recipient experience and integrating interventions into the healthcare system. The results of the patient‐oriented intervention studies are consistent with the Foster 2007 Cochrane review on lay‐led self‐management support programme, which concluded that there is no evidence that these interventions improve psychological health, symptoms or health‐related quality of life, or that they significantly alter healthcare use.

The evidence from this review partially addresses the review question, i.e. what interventions can effectively improve outcomes in people with multimorbidity. It suggests that interventions such as the addition of clinical care protocols need to be targeted at populations with defined combinations of common conditions such as diabetes and depression or heart disease; or need to focus on specific problems experienced by people in multimorbidity populations, for example a multidisciplinary team intervention that addresses functional difficulties. However, even when interventions are targeted they may not be effective for appropriate use of medications. The Haynes 2008 Cochrane review of Interventions for enhancing medication adherence concludes that "current methods of improving adherence for chronic health problems are mostly complex and not very effective" and suggests further research is needed. People with multimorbidity may have more specific problems with medicines use that relate to polypharmacy and managing complex drug treatment regimens, so medicines management interventions targeting these specific difficulties may be more effective.

Most of the multimorbidity studies in this review focused on older people; however, it is important to address the needs of younger individuals as there are issues relating to employability and absenteeism. Research in Scotland has highlighted that individuals in the poorest socioeconomic groups are more likely to develop multimorbidity at a younger age and more likely to die prematurely as a result (Barnett 2012). Acting upstream for younger people with multimorbidity is preventive and has potential to bring about significant quality of life benefits for individuals as well as cost savings for healthcare systems. However, even in ageing populations, multimorbidity worsens outcomes so there is still likely to be room for improvement, at least in ambulatory care patients.

The evidence to guide intervention development for individuals with multimorbidity is increasing and evolving rapidly. A number of ongoing studies have been identified and we anticipate that future updates of the review will improve the available evidence to inform policy makers and those planning services for individuals with multimorbidity.

Quality of the evidence

All of the included studies were randomised controlled trials. Overall they were of reasonable quality with minimal risk of bias, although blinding of participants and clinicians involved in the types of interventions included in this review is often impossible. Multimorbidity is a complex area because the characteristics of participants can vary depending on definitions used. This limits the potential to reasonably combine study results for meta‐analysis which is reflected in the high heterogeneity in the meta‐analyses undertaken for the review update, and potentially limits the internal validity of the results of the review.

Potential biases in the review process

The review was carried out in accordance with EPOC guidelines and using the updated Cochrane Handbook for Systematic Reviews of Interventions (Cochrane Handbook). Potential limitations in the search process relate to the lack of a MeSH term for multimorbidity. This meant that we had to use broad search terms which led to a high yield of citations to be searched. However, the authors are active researchers in the field of multimorbidity and are unaware of any potentially eligible studies that were missed by the search. We were also unable to retrieve some missing data from authors. However, as limited meta‐analyses were undertaken this did not lead to any appreciable measurement bias.

In addition, it must be noted that when we address complex interventions in primary care, there is always a context in which those interventions take place. A systematic review does not address the context that could have influenced the results in individual studies as there was limited reporting of external validity or generalisability in individual trials. The usual limitations relating to publication bias apply but we have searched the grey literature and contacted experts in the field to try and identify published and ongoing trials in this area.

Agreements and disagreements with other studies or reviews

We were unable to group sufficient numbers of studies with similar interventions in order to comment on which elements of interventions (e.g. the use of community pharmacists) seemed most effective and compare our review to other reviews of these interventions. The most consistent intervention element across all included studies was the use of case managers, but even these varied in that some were clinical case managers and others were administrative managers. The Cochrane review of the effect of case management on health care outcomes is ongoing but does plan to address differences in effectiveness between different types of case management (Zwarenstein 2000). Systematic reviews of community‐based case management in general have indicated mixed effects with improvements in client and professional satisfaction with care and reductions in caregiver strain but no impact on healthcare utilisation (Challis 2014).

Flow Diagram

Figures and Tables -
Figure 1

Flow Diagram

Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

Figures and Tables -
Figure 2

Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

Risk of bias summary: review authors' judgements about each risk of bias item for each included study.

Figures and Tables -
Figure 3

Risk of bias summary: review authors' judgements about each risk of bias item for each included study.

Forest plot of comparison: 1 Glycaemic control (HbA1c) Diabetes outcome: 1.1 HbA1c at immediate to 6 months post‐intervention follow up.

Figures and Tables -
Figure 4

Forest plot of comparison: 1 Glycaemic control (HbA1c) Diabetes outcome: 1.1 HbA1c at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 2 Systolic Blood Pressure: outcome: 2.1 Systolic blood pressure at immediate to 6 months post‐intervention follow up.

Figures and Tables -
Figure 5

Forest plot of comparison: 2 Systolic Blood Pressure: outcome: 2.1 Systolic blood pressure at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 3 Depression scores: 3.1 PHQ9 Depression scores at immediate to 6 months post‐intervention follow up.

Figures and Tables -
Figure 6

Forest plot of comparison: 3 Depression scores: 3.1 PHQ9 Depression scores at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 4 Depression scores: 4.1 Depression scores at immediate to 6 months post‐intervention follow up.

Figures and Tables -
Figure 7

Forest plot of comparison: 4 Depression scores: 4.1 Depression scores at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 5 Health related quality of life, outcome: 5.1 HRQoL at immediate to 6 months post‐intervention follow up.

Figures and Tables -
Figure 8

Forest plot of comparison: 5 Health related quality of life, outcome: 5.1 HRQoL at immediate to 6 months post‐intervention follow up.

Forest plot of comparison: 6 Self‐Efficacy, outcome: 6.1 Self‐efficacy score at immediate to 6 months post‐intervention follow up.

Figures and Tables -
Figure 9

Forest plot of comparison: 6 Self‐Efficacy, outcome: 6.1 Self‐efficacy score at immediate to 6 months post‐intervention follow up.

Comparison 1: Glycaemic control (HbA1c) studies targeting diabetes, Outcome 1: HBA1c

Figures and Tables -
Analysis 1.1

Comparison 1: Glycaemic control (HbA1c) studies targeting diabetes, Outcome 1: HBA1c

Comparison 2: Systolic Blood Pressure: studies targeting hypertension, Outcome 1: Systolic blood pressure

Figures and Tables -
Analysis 2.1

Comparison 2: Systolic Blood Pressure: studies targeting hypertension, Outcome 1: Systolic blood pressure

Comparison 3: PHQ9 depression scores: studies targeting depression, Outcome 1: PHQ9 Depression scores

Figures and Tables -
Analysis 3.1

Comparison 3: PHQ9 depression scores: studies targeting depression, Outcome 1: PHQ9 Depression scores

Comparison 4: Depression scores: studies targeting depression, Outcome 1: Depression scores

Figures and Tables -
Analysis 4.1

Comparison 4: Depression scores: studies targeting depression, Outcome 1: Depression scores

Comparison 5: Health related quality of life, Outcome 1: HRQoL

Figures and Tables -
Analysis 5.1

Comparison 5: Health related quality of life, Outcome 1: HRQoL

Comparison 6: Self‐Efficacy, Outcome 1: Self‐efficacy score

Figures and Tables -
Analysis 6.1

Comparison 6: Self‐Efficacy, Outcome 1: Self‐efficacy score

Summary of findings 1. Summary of findings

Interventions aimed at improving outcomes for people with multimorbidity compared with usual care

Participant or population: Adults with multimorbidity (two or more chronic conditions)

Settings: Primary care and community settings

Intervention: Any intervention designed to improve outcomes for people with multimorbidity including professional‐, organisational‐ and patient‐oriented interventions

Comparison: Usual care

Outcomes

Impacts

Number of studies

Quality of the evidence
(GRADE)

Clinical outcomes

There is no clear effect on clinical outcomes with a range of standardised effect sizes from 0.01 to 0.78 with a minority having effect sizes > 0.5; interventions aimed at improving management of risk factors in comorbid conditions were more likely to have higher effect sizes.

10

⊕⊕⊕⊖

Moderate

Mental health outcomes

There are improved depression‐related outcomes in studies targeting comorbid conditions that include depression with a range of standardised effect sizes from 0.09 to 1.18 with 3 of 7 studies having moderate to large effect sizes (> 0.5) . Standardised mean difference of −0.41 (95% CI, −0.63 to −0.20) was calculated from combining data from 6 studies.

9

⊕⊕⊕⊕

High

Patient‐reported outcome measures (PROMs)

There are mixed effects on PROMs with only half of studies that reported these outcomes showing any benefit with a range of standardised effect sizes from 0.03 to 0.84. Only 1 of 5 studies with data available data on self‐efficacy had a moderate effect size ​(>0.5), 1 of 6 had a moderate effect size for HRQoL, and effect sizes for other psychosocial outcomes were generally low.

13

⊕⊕⊕⊖

Moderate

Health Service Utilisation

There were no effects on health service utilisation and changes in visits were difficult to interpret as some interventions could lead to higher numbers of visits if previous unmet need was being addressed. There was no difference in admission‐related outcomes, though numbers of admissions in most of these studies were very small.

5

⊕⊕⊖⊖

Low

Medication use and adherence

There are mixed effects on medication use and adherence with half the studies reporting this outcome showing benefit. Proportions adherent to medication were higher in intervention participants with ranges in absolute difference of 10% to 40% but all studies with available data had small effect sizes.

4

⊕⊕⊖⊖

Low

Health‐related patient behaviours

Studies measuring this outcome reported a range of effects varying from an additional 18 minutes spent walking per week to an absolute difference in kcals expenditure per week of 2516 (no studies presented data that could be used to calculate effect sizes).

7

⊕⊕⊕⊖

Moderate

Provider behaviour

The majority of studies reporting provider behaviour indicated improved provider behaviour relating to care delivery; three studies reported a range of 15% to 40% in proportions of intervention providers improving behaviours such as appropriate referral.

5

⊕⊕⊕⊖

Moderate

GRADE Working Group grades of evidence
High quality: Further research is very unlikely to change our confidence in the estimate of effect.
Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate.
Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate.
Very low quality: We are very uncertain about the estimate.

We downgraded the evidence for effects on clinical and psychosocial outcomes to moderate due to lack of consistency of effect across studies and small effect sizes. We downgraded the evidence for effects on provider behaviour to moderate due to limited available data for calculation of standardised effect sizes (SES) and lack of clarity regarding the clinical importance of the results. We downgraded the evidence for effects on health service utilisation and medication use and adherence to low due to variation across studies and small effect sizes.

Figures and Tables -
Summary of findings 1. Summary of findings
Table 1. Multimorbidity intervention components

Author Year

Professional

Participant

Organisational

Effect of intervention on primary outcome

Case management or coordination of care

Reorganisation of care/team working

New team member

Predominantly organisational

Barley 2014

Nurse training

Participant information

Prioritisation to create

goals and health plan

Case manager provided personalised care

Regular planned participant visits

Weekly team meetings

Nurse case manager

Pilot study and primary outcome was feasibility and deemed successful

Bogner 2008

Individualised programme

Case manager

Regular planned participant visits

Improved blood pressure control and depression scores

Boult 2011

Nurse training

Individual management plans

Support for self‐management

Guided care nurses coordinated care

Guided care 'pods' consisting of nurse and PCP

Monthly monitoring of participants

No impact on healthcare utilisation

Coventry 2015

Practice team training

Personalised goals and participant workbooks

Collaborative care using stepped care protocols

Joint consultation between participant, psychologist and practice nurse

Psychologist

Supervision and input from team psychiatrist

Modest reduction in depression scores

Hogg 2009

Individualised care plans

Multidisciplinary team‐based management with home based assessment

Medication review

Pharmacist

Modest improvements in quality of chronic care delivery

Katon 2010

Individualised management plans and targets

Support for self‐management

Team‐based care

Stepped care treatment protocols

Weekly team meeting

Psychologist and psychiatrist supported depression care

Improvements in composite outcome of glycaemic control, blood pressure, lipids and depression scores

Kennedy 2013

Practice training

Support for self‐management

Participant guidebooks

Systems‐based approach to self‐management support with practice supports and links made with related local services

No intervention effect noted

Krska 2001

Individualised pharmaceutical care plans

Practice team‐implemented care plans

Pharmacist undertook medication review and devised pharmaceutical care plans

Reduction in pharmaceutical care issues

Martin 2013

Training for community psychologists

Cognitive behavioural therapy sessions

Psychological care programme designed for headache and depression

Community psychologists

Reduced headaches and improved depression scores

Morgan 2013

Practice nurse training

Support for self‐management

Goal setting

Individualised care plans

Nurse case manager

Quarterly reviews with practice nurse with GP stepping up care as needed

Improved depression scores

Sommers 2000

Risk reduction plan

Team based care with home assessment followed by team discussion, treatment plan and targets

Social worker

Reduced hospitalisation

Wakefield 2012

Participation in home telehealth monitoring

Nurse case manager using telehealth monitoring and treatment algorithms

Improved blood pressure, no effect on glycaemic control

Predominantly Patient‐oriented interventions

Eakin 2007

Support for self‐management with focus on diet and physical activity

Regular visits and follow‐up telephone calls

Health educator

Improvements in diet but not in physical activity

Garvey 2015

Occupational therapist (OT) training

OT‐led, group‐based support for self‐management programme (6 weeks)

Goal setting and peer support

GP and primary care team referral

OT with input from physiotherapist and pharmacist

Improvements in activity participation

 

 

 

 

 

 

 

Hochhalter 2010

Training for coaches running intervention

Patient Engagement workshop (x1)

Two follow‐up phone calls

Coach who delivered workshop

No effect on outcomes

Lorig 1999

Training for volunteer lay group leaders

Chronic Disease Self Management Support Programme (six sessions)

Peer support

Volunteer lay group leaders supported by study team

No primary outcome specified. Multiple outcomes reported with mixed effects

Lynch 2014

Diabetes self management support groups (18 sessions)

Peer support

Goal setting and behaviour skills training

Dietician led groups

No effect on primary outcome of weight reduction

The predominant intervention component is highlighted in bold text for each study

No study contained a financial‐type intervention element

Figures and Tables -
Table 1. Multimorbidity intervention components
Table 2. Costs

Study

Study type

Outcome

Result

Notes

Barley

RCT

Cost‐effectiveness

The intervention demonstrated marginal cost effectiveness up to a QALY threshold of GBP 3035

Boult

RCT

Total healthcare cost

Saving of USD 75,000 per GCN and USD 1364 per participant

USD in 2007

Initial result only

ns

Katon

RCT

Cost‐effectiveness

Mean reduction of 114 days in depression free days and an estimated difference of 0.335 QALYs (95% CI −0.18 to 0.85). The intervention was associated with lower OPD costs with a reduction of USD 594 per participant (95% CI USD −3241 to USD 2053).

Non‐significant but 99.7% probability that the intervention met the threshold of < USD 20,000 per QALY

Krska

RCT

Mean cost of medicines

Int: 38.83

Con: 42.61

Absol diff 3.78

Rel %diff 9%

GBP in 2000

ns

SES = 0.13

Lorig

RCT

Intervention cost per completed participant

USD 70

USD in 1998

See text for assumptions made

Lorig

RCT

Cost savings per individual

USD 750

USD in 1998

See text for assumptions made

Sommers

RCT

Savings per individual

USD 90

USD in 1994

See text for assumptions made

* refers to whether original study reported statistically significant improvement in this outcome

Figures and Tables -
Table 2. Costs
Table 3. Overview of outcomes

Outcome category

Outcome

No. studies with this outcome

No. studies with p< 0.05 for this outcome

 

Physical Health

Hb1Ac

5

2

 

BP

6

2

 

Cholesterol

2

1

 

Other symptom score

 

4

1

Mental Health

Depression scores

8

6

 

% improved depression

1

1

 

Anxiety scores

4

3

 

Cognitive symptom management

1

0

 

Psychosocial

QoL/general health

10

4

 

Functional impairment & disability

5

2

 

Social (activity/support)

4

1

 

Self efficacy

7

3

 

Home hazards

1

0

 

Health service use

Visits/use service

5

0

 

Hospital admission related

6

2

 

Patient health related behaviours

Exercise/diet

6

2

 

Medication adherence

5

2

 

Provider behaviour

Prescribing

3

2

 

Disease management

3

3

 

Costs

Direct costs or cost effectiveness

6

Not applicable

 

* Multimorbidity is defined as two or more independent conditions within the same individual whereas comorbidity refers to linked conditions. In this review comorbidity studies included depression and diabetes or depression and hypertension

** The scales or measurements used in each study for the outcomes are described in the Table of included studies

Figures and Tables -
Table 3. Overview of outcomes
Table 4. Clinical Outcomes

Study

Study type

Outcomes

Results

Notes

Barley

RCT

% with angina (Rose Angina score)

Int 22/31 Con 30/37

Absol diff 8, Rel % diff 27%

ns

Bognor

RCT

Systolic BP

Int 127.3 (SD 17.7) Con 141.3 (SD 18.8)

Absol diff 14, Rel % diff 10%

*

SES = 0.78

Bognor

RCT

Diastolic BP

Int 83 (SD 10.7) Con 81.4 (SD 11.1)

Absol diff 9.2, Rel % diff 11%

*

SES = 0.15

Hogg

RCT

Systolic BP

Int 124.3 Con 124.2

Absol diff 0.1, Rel % diff < 0.1%

ns

(No SDs

reported)

Hogg

RCT

HbA1c

Int 7.01 Con 6.78

Absol diff 0.23, Rel % diff 3%

ns

Katon

RCT

Systolic BP

Int 131 (SD 18.4) Con 132.3 (SD 17.2)

Absol diff 1.3, Rel % diff 1%

*

SES = 0.07

Katon

RCT

HbA1c

Int 7.33 (SD 1.21) Con 7.81 (SD 1.9)

Absol diff 0.48, Rel % diff 6%

*

SES = 0.32

Katon

RCT

Cholesterol

Int 91.9 (SD 36.7) Con 101.4 (SD 36.6)

Absol diff 9.5, Rel % diff 9%

*

SES = 0.26

Katon

RCT

Composite: all three risk factors

(BP, HbA1c and cholesterol) below guidelines

Int 36/97 (0.37) Con: 19/87 (0.22)

Absol diff 15, Rel % diff 68%

*

Lorig

RCT

Pain/ physical discomfort

Int 59.8 (SD 20.1) Con 60.6 (SD 17.1)

Absol diff 0.8, Rel % diff 1%

SES = 0.04

ns

Lorig

RCT

Energy/fatigue

Int 2.18 (SD 0.73) Con 2.02 (SD 0.75)

Absol diff 0.16, Rel % diff 8%

ns

Lorig

RCT

Shortness of breath

Int 1.34 (SD 0.91) Con 1.58 (SD 0.83)

Absol diff 0.24, Rel % diff 15%

ns

Lynch

RCT

HbA1C

Int 7.4 (SD 1.6) Con 7.5 (SD 1.6)

Absol diff 0.1, Rel % diff 6.7%

ns

SES = 0.06

Lynch

RCT

% with at least 0.5 absolute reduction in HbA1c

Int 15/30 (0.05) 7/31 Con (0.21)

Absol diff 29, Rel % diff 138%

*

Lynch

RCT

Mean SBP

Int 135.8 (SD 21.4) Con 136.7 (SD 23)

Absol diff 0.9, Rel % diff 0.6%

ns

SES = 0.04

Morgan

RCT

HbA1C

Int 6.9 (SD 1.21) Con 7.4 (SD 1.42)

Absol diff 0.5, Rel % diff 6.7%

*

SES = 0.38

Morgan

RCT

Systolic BP

Int 132.4 (SD 19) Con 131.2(SD 19.6)

Absol diff 1.2, Rel % diff 0.9%

ns

SES = 0.02

Morgan

RCT

Cholesterol

Int 4.22 (SD 0.94) Con 4.44 (SD 1.06)

Absol diff 0.22, Rel % diff 5%

ns

SES = 0.22

Morgan

RCT

Mean BMI

Int 31.2 (SD 6) Con 31.0 (SD 6)

Absol diff 0.2, Rel % diff 0.6%

ns

SES = 0.03

Sommers

RCT

Symptom scores

Int 17.2 Con 18.9

Absol diff 1.7, Rel % diff 9%

ns

Wakefield

RCT

HbA1c

Int 6.9 (1.1) Con 6.95 (1.1)

Absol diff 0.05, Rel % diff 0.7%

ns

SES = 0.05

Wakefield

RCT

Systolic BP

Int 133 (16.6) Con 137 (17.3)

Absol diff 4, Rel % diff 3%

ns

SES = 0.24

Martin

RCT

Mean headache rating

Int 0.63 (SD 0.5) Con 1.01 (SD 0.83)

Absol diff 0.38, Rel % diff 38%

*

SES = 0.58

* refers to whether original study reported statistically significant improvement in this outcome

** Total number with final data collected was 384. No final numbers of intervention and control participants presented.

Figures and Tables -
Table 4. Clinical Outcomes
Table 5. Mental Health Outcomes

Study

Study type

Outcome

Result

Notes

Barley

RCT

PHQ9 depression score

Int 12.6 (SD 7.1) Con 12 (SD 6.9)

Absol diff 0.6, Rel % diff 8%

ns

SES = 0.09

Barley

RCT

HADS depression score

Int 9.5 (SD 4.6) Con 8.8 (SD 4.8)

Absol diff 0.7, Rel % diff 8%

ns

SES = 0.15

Barley

RCT

HADS anxiety score

Int 9.9 (SD 7.1) Con 9.5 (SD 5.4)

Absol diff 0.4, Rel % diff 4%

ns

SES = 0.08

Bognor

RCT

CES depression score

Int 9.9 (SD 10.7) Con 19.3 (SD 15.2)

Absol diff 9.4, Rel % diff 49%

*

SES = 0.75

Coventry

RCT

SCL‐D13 depression score

Int 1.76 (SD 0.9) Con 2.02 (SD 0.9)

Absol diff 2.6, Rel % diff 13%

*

SES = 0.28

Coventry

RCT

PHQ9 depression score

Int 11.3 (SD 6.5) Con 13.1 (SD 6.5)

Absol diff 1.8, Rel % diff 14%

*

SES = 0.28

Coventry

RCT

GAD‐7 anxiety score

Int 8.2 (SD 5.8) Con 9.7 (SD 5.9)

Absol diff 1.5, Rel % diff 15%

*

SES = 0.26

Garvey

RCT

HADS total score

Int 15.6 (SD 8.3) Con 16.7 (SD 8.2)

Absol diff 1.1, Rel % diff 6.5%

ns

SES = 0.13

Katon

RCT

SCL 20 depression score

Int 0.83 (SD 0.66) Con 1.14 (SD 0.68)

Absol diff 0.31, Rel % diff 27%

*

SES = 0.46

Katon

RCT

Patient global improvement in depression

Int 41/92 Con 16/91

Absol diff 27, Rel % diff 150%

*

Lorig

RCT

Cognitive symptom management score

Int 1.75 Con 0.98

Absol diff 0.77, Rel % diff 79%

ns

Martin

RCT

PHQ9 depression score

Int 6.7 (SD 4.6) Con 12.6 (SD 5.3)

Absol diff 5.9, Rel % diff 47%

*

SES = 1.18

Martin

RCT

BDI ‐Depression score

Int 13.1 (SD 8.6) Con 28.7 (SD 9.5)

Absol diff 15.6, Rel % diff 54%

*

SES = 1.73

Martin

RCT

BAI Anxiety score

Int 10.5 (SD 10.8) Con 16.4 (SD 9.3)

Absol diff 5.9, Rel % diff 36%

*

SES = 0.1

Morgan

RCT

PHQ9 depression score

Int 7.1 (SD 4.7) Con 9.0 (SD 5.5)

Absol diff 1.9, Rel % diff 21%

*

SES = 0.37

Sommers

RCT

GDS score (depression)

Int 4.1 Con 4.1

Absol diff 0, Rel % diff 0%

ns

* refers to whether original study reported statistically significant improvement in this outcome

Figures and Tables -
Table 5. Mental Health Outcomes
Table 6. Patient‐reported outcome measures

Study

Study type

Outcome

Result

Notes

Health Related Quality of Life

Barley

RCT

SF12 PCS

Int 32.4 (SD 10.7) Con 33.3 (SD 9.2)

Absol diff 0.7, Rel % diff 2%

ns

SES = 0.07

Barley

RCT

SF12 MCS

Int 34.5 (SD 11.6 ) Con 33.6 (SD 12.5 )

Absol diff 0.9, Rel % diff 3%

ns

SES = 0.08

Barley

RCT

HRQoL (WEMWBS)

Int 40.6 (SD 11.2) Con 39.6(SD 12.3)

Absol diff 1, Rel % diff 2.5%

ns

SES = 0.08

Coventry

RCT

HRQoL (WHOQOL)

Int 2.99 (SD 0.6) Con 2.91 (SD 0.6)

Absol diff 0.08, Rel % diff 3%

*

SES = 0.13

Garvey

RCT

HRQoL (EQ5D VAS)

Int 65.7 (SD 20.2) Con 50.5 (SD 16.3)

Absol diff 15.2, Rel % diff 30%

*

SES = 0.84

Hogg

RCT

SF 36 Mental Health

Int 52.4 Con 52.2

Absol diff 0.2, Rel % diff 0.3%

ns

Hogg

RCT

SF 36 Physical Health

Int 44.3 Con 41.5

Absol diff 2.8, Rel % diff 6.7%

ns

Katon

RCT

QoL score

Int 6.0 (SD 2.2) Con 5.2 (SD 1.9)

Absol diff 0.8, Rel % diff 15%

*

SES = 0.44

Kennedy

RCT

HRQoL (EQ5D)

Int 0.56 (SD 0.34) Con 0.57 (SD 0.32)

Absol diff 0.01, Rel % diff 1%

ns

SES = 0.03

Lorig

RCT

Psychological well‐being

Int 3.47 Con 3.33

Absol diff 0.04, Rel % diff 4%

ns

SES = 0.21

Martin

RCT

HRQol (AQOL)

Int 26.3 (SD 4.76) Con 28.4 (SD 4.97)

Absol diff 2.1, Rel % diff 7 %

*

SES = 0.4

Sommers

RCT

SF36 score

Int 2.2 Con 3.3

Absol diff 1.1, Rel % diff 33%

ns

Self‐efficacy

Barley

RCT

Self‐efficacy score

Int 28.6 (SD 6.7) Con 27.9 (SD 8.1)

Absol diff 0.11, Rel % diff 2.5%

ns

SES = 0.09

Coventry

RCT

Self‐efficacy score

Int 5.72 (SD 1.9) Con 5.53 (SD 1.9)

Absol diff 0.18, Rel % diff 3.2%

ns *

SES = 0.09

Garvey

RCT

Self efficacy score

Int 6.8 (SD 1.5) Con 5.3 (SD 1.9)

Absol diff 1.47, Rel % diff 28%

*

SES = 0.86

Hochhalter

RCT

Self‐efficacy

Int 7.4 Con 8.0

Absol diff 0.6, Rel % diff 7.5%

ns

Kennedy

RCT

Self‐efficacy

Int 68 (SD 23.4) Con 68.7 (SD 23.1)

Absol diff 0.7, Rel % diff 1%

ns

SES = 0.03

Wakefield

RCT

Self‐efficacy

Int 8.1 (SD 1.9) Con 8.3 (SD 1.9)

Absol diff 0.2, Rel % diff 2.4%

ns

SES = 0.11

Daily function and disability

Coventry

RCT

Sheehan Disability Score

Int 5.73 (SD 2.8) Con 5.83 (SD 2.8)

Absol diff 0.1, Rel % diff 2%

*

SES = 0.04

Garvey

RCT

Frenchay Activities Index

Int 21.3 (SD 7.9) Con 18.9 (SD 7.2)

Absol diff 2.4, Rel % diff 13%

*

SES = 0.32

Garvey

RCT

Activities daily living: NEADL (total)

Int 47.2 (SD 11.9) Con 40.7 (SD 10.7)

Absol diff 6.5, Rel % diff 16%

*

SES = 0.58

Hogg

RCT

IADL

Int 10.6 Con 10.9

Absol diff 0.3, Rel % diff 2.7%

ns

Lorig

RCT

Disability

Int 0.86 Con 0.96

Absol diff 0.1, Rel % diff 10%

ns

Lorig

RCT

Social role/activity limitation

Int 1.91, Con 1.98

Absol diff 0.07, Rel % diff 4%

ns

Illness perceptions

Coventry

RCT

Multimorbidity illness perception scale

Int 2.1 (SD 0.9) Con 2.28 (SD 0.9)

Absol diff 0.18, Rel % diff 8%

ns

SES = 0.2

Barley

RCT

Illness perceptions (BIPQ)

Int 40 (SD 14.8) Con 43(SD 31.1)

Absol diff 3, Rel % diff 7%

ns

SES = 0.22

Social support

Coventry

RCT

Social support (ESSI)

Int 3.29 (SD 1.1) Con 3.4 (SD 1.0)

Absol diff 0.11, Rel % diff 3%

ns

SES = 0.11

Eakin

RCT

Multilevel support for healthy lifestyle

Int 2.7 Con 2.59

Absol diff 0.11, Rel % diff 4%

ns

Other PROMs

Barley

RCT

Patient‐reported needs (PSYCHLOPS)

Int 13.6 (SD 5.1) Con 13.4 (SD 5.4)

Absol diff 0.2, Rel % diff 1.5%

ns

SES = 0.04

Hochhalter

RCT

Total unhealthy days

Int 15.3 Con 14.1

Absol diff 1.2, Rel % diff 9%

ns

Hogg

RCT

Total unhealthy days

Int 7.6 Con 9.9

Absol diff 2.3, Rel % diff 23%

ns

Kennedy

RCT

Shared decision making (HCCQ)

Int 67.7 (SD 28) Con 69.3 (SD 26.1)

Absol diff 1.6, Rel % diff 2%

ns

SES = 0.06

Lorig

RCT

Self‐rated health

Int 3.42 Con 3.44

Absol diff 0.02, Rel % diff 0.6%

ns

Lorig

RCT

Health distress

Int 1.97 Con: 2.13

Absol diff 0.16, Rel % diff 7.5%

ns

SES = 0.16

Sommers

RCT

Social activities count

Int 8.7 Con:8.6

Absol diff 0.1, Rel % diff 1%

* (when adjusted

for baseline diff)

Sommers

RCT

HAQ score

Int 0.44 Con 0.5

Absol diff 0.06, Rel % diff 12%

ns

* refers to whether original study reported statistically significant improvement in this outcome

Figures and Tables -
Table 6. Patient‐reported outcome measures
Table 7. Health service use

Study

Study type

Outcome

Result

Notes

Boult

RCT

No. hospital admissions

Int 0.7 Con 0.72

Absol diff 0.02, Rel % diff 3%

ns

Boult

RCT

No. days in hospital

Int 4.26 Con 4.49

Absol diff 0.23, Rel % diff 5%

ns

Boult

RCT

No. ED visits

Int 0.44 Con 0.44

Absol diff 0, Rel % diff 0

ns

Boult

RCT

No. PC visits

Int 9.98 Con 9.88

Absol diff 0.1, Rel % diff 1%

ns

Boult

RCT

No. specialist visits

Int 9.04 Con 8.49

Absol diff 0.55, Rel % diff 6%

ns

Boult

RCT

No. home healthcare episodes

Int 0.99 Con 1.3

Absol diff 0.31, Rel % diff 24%

*

Hogg

RCT

No. hospital admissions

Int 0.4 Con 0.46

Absol diff 0.06, Rel % diff 13%

ns

Hogg

RCT

Proportion hospitalised

Int 0.26, Con 0.26

Absol diff 0, Rel % diff 0%

ns

Hogg

RCT

No. ED visits

Int 0.63 Con 0.73

Absol diff 0.01, Rel % diff 14%

ns

Hogg

RCT

Proportion with ED visit

Int 0.38 Con 0.42

Absol diff 0.04, Rel % diff 9%

ns

Katon

RCT

Proportion hospitalised

Int 0.26 Con 0.22

Absol diff 0.04, Rel % diff 18%

ns

Lorig

RCT

No. doctor and ED visits

Int 6.51 Con 7.08

Absol diff 0.57, Rel % diff 8%

ns

Lorig

RCT

No. hospital stays in past 6 months

Int 0.26 Con 0.31

Absol diff 0.05, Rel % diff 6%

*

Lorig

RCT

No. nights in hospital in last 6 months

Int 1.3 Con 1

Absol diff 0.3 Rel % diff 30%

*

Sommers

RCT

No. hospital admissions per individual per year

Int 0.36 Con 0.52

Absol diff 0.16, Rel % diff 31%

*

Sommers

RCT

≥1 60 day readmission

Int 3.6 Con 9.4

Absol diff 5.8, Rel % diff 62%

*

Sommers

RCT

≥ 1 hospital admission

Int 8.8 Con 7.7

Absol diff 1.1, Rel % diff 14%

*

Sommers

RCT

No. PCP visits

Int 6.0 Con 6.1

Absol diff 0.1, Rel % diff 2%

ns

Sommers

RCT

No. office visits

Int 11 Con 12.5

Absol diff 1.5, Rel % diff 12%

*

Sommers

RCT

≥ 1 home care visit

Int 19.5 Con 18.8

Absol diff 0.7, Rel % diff 4%

ns

Sommers

RCT

No. medical specialist visits

Int 1.4 Con 1.7

Absol diff 0.3, Rel % diff 18%

ns

Sommers

RCT

No. other visits

Int 3.9 Con 4.3

Absol diff 0.4, Rel % diff 9%

*

Sommers

RCT

≥ 1 ED visit

Int 21.4 Con 16.7

Absol diff 4.7, Rel % diff

ns

* refers to whether original study reported statistically significant improvement in this outcome

Figures and Tables -
Table 7. Health service use
Table 8. Medication use and adherence and prescribing

Study

Study type

Outcome

Results

Notes

Bognor

RCT

≥80% adherence to antidepressant medication (MEMS caps)

Int 23/32 Con 10/32

Absol diff 0.41, Rel % diff 132%

*

Bognor

RCT

≥80% adherence to antihypertensive medication (MEMS caps)

Int 25/32 Con 10/32

Absol diff 0.47, Rel % diff 152%

*

Martin

RCT

Mean daily medication use

Int 2.4 (SD 3.2) Con 3.0 (SD 2.8)

Absol diff 0.6, Rel % diff 20%

ns

SES = 0.2

Morgan

RCT

% taking antidepressant medication

Int 34/62 Con 36/113

Absol diff 0.11, Rel % diff 34%

*

Wakefield

RCT

Adherence (Edward's scale)

Int 3.4 (SD 0.5) Con 3.3 (SD 0.5)

Absol diff 0.1, Rel % diff 3%

ns

SES = 0.2

Wakefield

RCT

Medication Taking Adherence Score

Int 100 (SD 1.4) Con 98.9 (SD 6.0)

Absol diff 1.1, Rel % diff 1%

ns

SES = 0.28

* refers to whether original study reported statistically significant improvement in this outcome

Figures and Tables -
Table 8. Medication use and adherence and prescribing
Table 9. Health‐related participant behaviours

Study

Study type

Outcome

Results

Notes

Hochhalter

RCT

PAM (patient activation measure)

Int 66.8 Con 66.2

Absol diff 0.6, Rel % diff 1%

ns

Eakin

RCT

Diet behaviour scores

Int 2.2 Con 2.41

Absol diff 0.21, Rel % diff 9%

*

Eakin

RCT

Change minutes of walking/week

Int +8 Con −10

Absol diff 18, Rel % diff 180%

*

Katon

RCT

General adherence to diet score

Int 0.86 Con 0.81

Absol diff 0.05, Rel % diff 6%

ns

Katon

RCT

General adherence to exercise score

Int 0.54 Con 0.44

Absol diff 0.1, Rel % diff 23%

ns

Lorig

RCT

Exercise: stretching and strengthening (mins/week)

Int 53.1 Con 40.4

Absol diff 12.7,Rel % diff 31%

ns

Lorig

RCT

Exercise: aerobic (mins/week)

Int 101.8 Con 88

Absol diff 13.8, Rel % diff 157%

ns

Lorig

RCT

Communication with doctor

(score 1‐5)

Int 3.34 Con 3.2

Absol diff 0.14, Rel % diff 4%

ns

Lynch

RCT

Physical activity (kcal expenditure per week, CHAMPS)

Int 1913.6 Con −603

Absol diff 2516, Rel % diff 417%

*

Morgan

RCT

Smoking

Int 13/162 Con 13/110

Absol diff 0.04, Rel % diff 33%

ns

Morgan

RCT

Alcohol

Int 51/104 Con 27/42

Absol diff 0.15, Rel % diff 23%

ns

Morgan

RCT

Exercise (30 minutes/day for 5 days/ week)

Int 97/162 Con 22/75

Absol diff 0.31, Rel % diff 106%

*

Sommers

RCT

Nutrition checklist score

Int 2.0 Con1.9

Absol diff 0.1, Rel % diff 5%

ns

* refers to whether original study reported statistically significant improvement in this outcome

Figures and Tables -
Table 9. Health‐related participant behaviours
Table 10. Provider behaviour

Study

Study type

Outcome

Result

Notes

Boult

RCT

PACIC score

(patient measure of quality of care received)

Int 3.14 Con 2.85

Absol diff 0.29, Rel % diff 10%

*

Coventry

RCT

PACIC score

Int 2.37 (SD 1.1) Con 1.98 (SD 1.0)

Absol diff 0.39, Rel % diff 20%

ns

SES = 0.39

Hogg

RCT

Chronic Disease Mangement Score

Int 0.84 Con 0.77

Absol diff 0.07, Rel % diff 9%

*

Hogg

RCT

Preventive Care Score

Int 0.89 Con 0.7

Absol diff 0.19, Rel % diff 27%

*

Krska

RCT

% Pharmaceutical care issues resolved from baseline

Int 950/1206 Con 542/1380

Absol diff 0.4, Rel % diff 102%

*

Morgan

RCT

% Referred to mental health

Int 58/162 Con 10/111

Absol diff 0.27, Rel % diff 300%

*

Morgan

RCT

% Referred to exercise programme

Int 58/162 Con 24/114

Absol diff 0.15, Rel % diff 71%

*

* refers to whether original study reported statistically significant improvement in this outcome

Figures and Tables -
Table 10. Provider behaviour
Comparison 1. Glycaemic control (HbA1c) studies targeting diabetes

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1.1 HBA1c Show forest plot

3

561

Mean Difference (IV, Random, 95% CI)

‐0.19 [‐0.47, 0.10]

Figures and Tables -
Comparison 1. Glycaemic control (HbA1c) studies targeting diabetes
Comparison 2. Systolic Blood Pressure: studies targeting hypertension

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

2.1 Systolic blood pressure Show forest plot

5

892

Mean Difference (IV, Random, 95% CI)

‐3.10 [‐7.26, 1.06]

Figures and Tables -
Comparison 2. Systolic Blood Pressure: studies targeting hypertension
Comparison 3. PHQ9 depression scores: studies targeting depression

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

3.1 PHQ9 Depression scores Show forest plot

4

Mean Difference (IV, Random, 95% CI)

Totals not selected

Figures and Tables -
Comparison 3. PHQ9 depression scores: studies targeting depression
Comparison 4. Depression scores: studies targeting depression

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

4.1 Depression scores Show forest plot

6

1062

Std. Mean Difference (IV, Random, 95% CI)

‐0.41 [‐0.63, ‐0.20]

Figures and Tables -
Comparison 4. Depression scores: studies targeting depression
Comparison 5. Health related quality of life

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

5.1 HRQoL Show forest plot

6

Std. Mean Difference (IV, Random, 95% CI)

Totals not selected

Figures and Tables -
Comparison 5. Health related quality of life
Comparison 6. Self‐Efficacy

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

6.1 Self‐efficacy score Show forest plot

5

3639

Std. Mean Difference (IV, Random, 95% CI)

0.05 [‐0.12, 0.22]

6.1.1 Studies targeting self‐efficacy

5

3639

Std. Mean Difference (IV, Random, 95% CI)

0.05 [‐0.12, 0.22]

Figures and Tables -
Comparison 6. Self‐Efficacy