Reducing Care Utilisation through Self-management Interventions (RECURSIVE): a systematic review and meta-analysis

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Chapter 1 Background

In the context of the increasing prevalence and impact of long-term conditions,1 and increasing numbers of patients reporting multiple conditions,2 there is worldwide interest in innovations in service delivery that can better manage patients with long-term conditions in a way that is effective, patient-centred and efficient. 3

Current NHS policy for long-term conditions has been influenced by work done at Kaiser Permanente in the USA, and envisages care for long-term conditions based around three tiers representing three broad groups of patients with different needs. Care for patients in those tiers is supposed to be qualitatively different in content and process – the various aspects of care in each tier are shown in Box 1.

Self-management and demand management

Self-management is an attractive proposition to the management of long-term conditions for a number of reasons. As well as the potential benefits for health, self-management offers a more participatory approach to health care, with patients making a critical contribution to achieving health gain and making decisions to ensure that their care is personalised to their needs.

However, a key part of the driver for health policy is the potential of self-management to make a significant contribution to the efficient delivery of health care. The influential Wanless report suggested that the future costs of health care would be related to the degree to which people became engaged with their health and its management. 22 Although the health costs associated with ageing are a matter of controversy,23 health services are facing major challenges in terms of the projected increases in those aged ≥ 65 years, the consequent prevalence of multimorbidity and concomitant increases in demand associated with these demographic changes.

The global financial crisis and central government pressure for major savings has meant that even greater focus is being placed on efficiency in health-care delivery. The Quality, Innovation, Productivity and Prevention (QIPP) initiative in the NHS is designed to identify efficiencies through service redesign. Increasing self-management support is a major focus of the programme. 24

Although self-management support has been highlighted as having a significant contribution to make to efficiency, there are uncertainties about the scale of that contribution. Initial reports of major effects of self-management support on health-care utilisation25 have not always been replicated26 and the fact that the main impact of some interventions is on intermediate outcomes (such as self-efficacy) rather than health and health-care utilisation has led to controversy over the overall impact of self-management. 27,28 Some implementation of self-management support may have inadvertently driven up demand in populations to which self-management is directed. 29

Economic analysis in health services is based on the principle of opportunity cost, i.e. any one use of resources involves a ‘cost’ associated with the lost potential from alternative uses. Efficiency involves maximising outcomes for a given cost or minimising costs for a given level of outcome.

However, many health-care interventions improve outcomes and increase costs, which means decision-makers are faced with decisions about ‘allocative efficiency’: additional resources are required to provide the new service, which incurs an opportunity cost for other groups of patients. 30 Economists use the concept of the cost-effectiveness plane to illustrate the relationships between costs and outcomes (Figure 1). Many health-care interventions are placed in the ‘top right’ quadrant of the cost-effectiveness plane and raise such ‘allocative efficiency’ questions for decision-makers.

FIGURE 1.

Cost-effectiveness plane.

However, the financial pressures faced by health systems means that there is increasing interest in interventions that are ‘technically efficient’. This is defined as an intervention which is less costly and at least as effective as current treatments. 30 An implicit assumption underlying interest in self-management support is that delivering care in this way has the potential to be technically efficient, by shifting some activity from health services to the patient and by more effective management of problems to avoid crises and the need for more extensive health service intervention.

Assessing the technical efficiency of self-management support is best achieved through comprehensive economic analyses using an assessment (and quantification) of both quality of life (QoL) and costs, to assess the location of the intervention on the cost-effectiveness plane. Although there are increasing numbers of full economic analyses, many self-management studies have not conducted such a full economic analysis, but many have included data on outcomes and costs, which may allow placement on the plane.

The aim of this review is to conduct a comprehensive assessment of the current evidence around self-management support to judge the degree to which current models of support reduce utilisation without compromising outcomes.

The results of the Reducing Care Utilisation through Self-management Interventions (RECURSIVE) review need to be considered alongside the Practical Systematic Review of Self-management support for long-term conditions (PRISMS) study,31 which is a broader assessment of the role of self-management support in long-term conditions using a variety of metareview techniques. 31

Chapter 2 Research questions

What models of self-management support are associated with significant reductions in health services utilisation (including admissions) without compromising outcomes, among patients with long-term conditions?

• Population: patients with long-term conditions.

• Intervention: self-management support.

• Comparison: usual care.

• Outcomes: service utilisation (including admissions) and QoL.

• Study design: randomised controlled trials (RCTs).

What are the key recommendations for service commissioners and research funding bodies on delivery of self-management support and future research priorities?

Chapter 3 Review methods

Analyses

Accurate placement of studies on the cost-effectiveness plane requires accurate quantification of the magnitude of both effects on costs and outcomes, which requires particular forms of data beyond simple text descriptions of significance and p-values.

We sought data that would allow us to report a standardised mean difference (or ‘effect size’) for health outcomes and costs (Box 2). This generally requires reporting of means, standard deviations (SDs) and sample sizes, although other presentations of those data can be used (such as mean difference statistics), and other presentations (i.e. use of dichotomous outcomes such as rates rather than means) can be translated to a standardised mean difference through appropriate transformation. 91 When single parameters were missing (such as a SD, or a sample size at follow-up), we imputed based on other data in the review, or heuristics (e.g. assuming that 70% follow-up would be achieved from numbers of participants randomised at baseline). We excluded studies that lacked data if there were no other studies in the review to allow imputation.

BOX 2 - Effect sizes

A RCT assesses the effect of a treatment by comparing the outcomes in the treatment and control groups. Many measures of QoL are continuous, providing a score that varies from 0 up to a maximum based on the number and response range of the items.

Comparing the mean scores of patients in the treatment and control groups gives a good indication of the impact of the treatment. For example, if patients in the treatment group have a mean score at the end of the study of 20, and the controls have a mean of 15, the mean difference is 5 points (i.e. treatment leads to an improvement in QoL of 5 points on average). One difficulty is that it takes an expert to know whether or not a difference of 5 points is important or trivial. A second problem is that studies often use different measures. Knowing that a treatment causes a mean improvement of 5 points when QoL has been measured on two completely different scales makes comparison impossible.

Effect sizes overcome these difficulties by standardising. Essentially, this involves dividing the mean difference from each trial by a measure of the underlying variability of the scores on that outcome (the so-called SD). If scores are generally very variable, then a large mean difference would be required to demonstrate that treatment was better than control. If scores do not vary markedly, then a small mean difference may still represent an important effect of treatment. The mean difference divided by a measure of variability in this way is often described as an effect size.

Standardising in this way means that the difference between treatment and control groups can be described in terms of the same unit (i.e. units of SD). So, if one RCT finds a mean difference of 5 points and the SD is 10, then the effect size is 0.5 (and the difference in QoL is half a SD). A second trial using a different measure might report a larger mean difference of 15 but, if the SD of scores in that trial is 25, then the effect size is actually only slightly increased (15/25 = 0.6) even though the mean difference is much larger.

A convention has emerged to judge the magnitude of effect sizes calculated in this way. An effect size of around 0.2 is often described as ‘small’, an effect size of 0.5 as ‘medium’ and an effect size of 0.8 as ‘large’. 87 These are convenient labels with some validity88,89 and they provide a useful rule of thumb to assess the effect of interventions in the context of the wider literature. Nevertheless, decision-makers need to be careful in their interpretation.

Outcomes reported on dichotomous scales (such as proportion of patients using a hospital following treatment) are often reported using different metrics (such as odds ratios, relative risks and NNT). However, they can be translated to an equivalent effect size. For example, a ‘small’ effect size (0.2) is equivalent to a NNT of approximately 18, while effect sizes of 0.5 and 0.8 are equivalent to NNTs of approximately 4 and 2.5, respectively. 90

NNT, number needed to treat; SD, standard deviation.

It is generally the case that many measures of utilisation (e.g. hospital length of stay) and data on costs demonstrate significant skew (where many patients report low costs, but a small proportion have disproportionately large values). In line with published reviews,92 we identified those outcomes for which the SD multiplied by two was greater than the mean, as in these cases it is argued that the mean is not a good indicator of the centre of the distribution,93 although skewed data are less problematic if the sample size is large.

We explored statistical heterogeneity through the I2 statistic,94 which provides an estimate of the percentage of total variation across studies that can be attributed to heterogeneity rather than chance. We labelled levels of heterogeneity as ‘low’ (1–25%), ‘moderate’ (26–74%) and ‘high’ (≥ 75%). Caution should be applied in the interpretation of pooled effects in meta-analyses with ‘high’ levels of heterogeneity.

A minority of self-management support trials use cluster allocation to reduce bias associated with contamination. Such studies were identified and the precision of analyses adjusted using a sample size/variation inflation method recommended by the Effective Practice and Organisation of Care group of the Cochrane Collaboration,95 assuming an intraclass correlation of 0.02.

Some studies reported multiple self-management support interventions against a single control. In these cases, we extracted each self-management support intervention as a separate comparison and entered them where relevant in the meta-analysis, dividing the control group sample size appropriately to avoid double counting in the analysis (although this method assumes effect sizes are independent).

The aim of the analysis was to conduct a quantitative systematic review to identify self-management support interventions associated with significant reductions in health services utilisation (including hospital admissions) without compromising outcomes.

The primary analysis was structured by type of long-term condition, with a separate analysis for studies including mixed groups of patients with varying long-term conditions. We also conducted sensitivity analyses to explore the PRISMS categories of conditions (see Table 1) as an alternative typology, restricting those analyses to the two most prevalent categories (PRISMS 1 and 3) (see Table 1).

For each condition category, we present a description of the search and identification of the studies, including the total number identified and the subset of studies including analysable data on QoL, on utilisation and costs and on both outcomes. Our primary interest was on studies reporting both forms of data, because studies that reported only one outcome cannot formally be placed in the cost-effectiveness plane.

We present the results of the included studies for each condition group according to a permutation plot for all studies reporting both outcomes (i.e. QoL and hospital use and QoL and costs), plotting the effect of interventions on utilisation and outcomes simultaneously and placing them in quadrants of the cost-effectiveness plane depending on the pattern of outcomes (Figure 2). The plot shows the pattern of results at the level of the individual study, gives a visual impression of the distribution of studies across the cost-effectiveness plane, and identifies studies in the appropriate quadrant (i.e. those that reduce costs without compromising outcomes) and those in problematic quadrants (i.e. those that reduce costs but also compromise outcomes, or those that compromise both outcomes and costs).

FIGURE 2.

Example permutation plot showing utilisation and health outcomes.

Chapter 4 Results

Study characteristics

Overall, we screened 12,078 titles and abstracts for eligibility in the review. The flow of studies through the search process is outlined in Figure 3.

FIGURE 3.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 flow diagram: entire review. Overall pattern of the results.

Full details of data extracted from individual studies (population, conditions, comparisons, risk of bias, economic analyses) are provided in Appendices 4–8.

We also identified 24 studies reporting data on QoL and health-care utilisation in other long-term conditions,99–122 such as hypertension (n = 5), inflammatory bowel disease (n = 6), lung disease (n = 3), multiple sclerosis (n = 2), chronic kidney disease (n = 1), Parkinson’s disease (n = 1), migraine/headache (n = 2), insomnia (n = 1), psoriasis (n = 1), acid-peptic disease (n = 1) and ulcerative colitis (n = 1) (Table 4). Although these studies met the eligibility criteria of the review, we excluded studies where there were very low numbers in particular condition categories, where our analytic methods were unlikely to be productive.

TABLE 4 - Basic descriptive data on the studies

SM, self-management.

Category	Characteristics	n (%); (N = 184)
Context	Country
	UK	43 (23)
	USA	65 (35)
	European	44 (24)
	Other	32 (17)
Patients	Condition
	Arthritis	14 (8)
	Cardiovascular	53 (29)
	Diabetes	11 (6)
	Mental health	29 (16)
	Mixed disease	13 (7)
	Respiratory	44 (24)
	Pain	20 (11)
	Mean age (years) (SD)	58 (13)
	% male	49
Intervention	Content
	Pure SM	9 (5)
	Supported SM	36 (20)
	Intensive SM	87 (47)
	Case management	52 (28)
	Technology involved	43 (23)
	Mean (SD, range)	275 (202, 23–1801)
External validity	Excluded patients with other long-term conditions	65 (35)
	Proportion of eligible patients who did not take part in the study
	Not clear	48 (26)
	< 20%	40 (22)
	21–40%	55 (30)
	41–60%	25 (14)
	61–80%	14 (8)
	81–100%	2 (1)

Figures 4 and 5 show the overall permutation plots, plotting QoL and hospital use outcomes (see Figure 4) and QoL and costs (see Figure 5).

FIGURE 4.

Permutation plot (all studies): QoL and hospital use outcomes.

FIGURE 5.

Permutation plot (all studies): QoL and total costs.

In terms of hospital use, the bulk of studies are in the lower right quadrant (i.e. they are associated with improvements in QoL and reductions in utilisation). Only a minority of studies report decrements in QoL and a smaller proportion of studies report improved outcomes with increases in utilisation.

In terms of costs, the picture is more mixed with more studies in the top right quadrant, reporting improved outcomes with increases in utilisation. Of the studies reporting costs, almost all demonstrated significant skew (i.e. the SD multiplied by two was more than twice the mean).

Note that the plots do not represent the uncertainty around point estimates, which in many studies would be considerable.

Analyses of studies for patients with respiratory problems

The studies identified in respiratory problems are detailed in Figure 6. 118,123–129,166–200

FIGURE 6.

Flow chart of studies in patients with respiratory problems.

Figures 7 and 8 show the permutation plots for interventions for patients with respiratory problems.

FIGURE 7.

Permutation plot: respiratory (hospital use and QoL).

FIGURE 8.

Permutation plot: respiratory (total costs and QoL).

Most studies reporting hospital data were in the bottom right quadrant of the plots, reporting improvements or no differences in QoL and hospital use. Benefits in utilisation were less pronounced in total costs.

In analyses including all studies, self-management support interventions for patients with respiratory problems were associated with small but significant improvements in QoL. Variation across trials was moderate (Figure 9).

FIGURE 9.

Forest plot: respiratory (QoL). CI, confidence interval; ES, effect size.

In analyses including all studies, self-management support interventions for patients with respiratory problems were associated with small but significant reductions in hospital use. Variation across trials was moderate (Figure 10).

FIGURE 10.

Forest plot: respiratory studies (hospital use). CI, confidence interval; CM, nurse-assisted collaborative management; ES, effect size; MM, nurse-assisted medical management. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with respiratory problems were associated with non-significant increases in costs. Variation across trials was high (Figure 11).

FIGURE 11.

Forest plot: respiratory studies (costs); CI, confidence interval; ES, effect size.

In analyses exploring the impact of different types of self-management support, there was evidence that ‘case management’ interventions produced small but significant improvements in QoL and small but significant reductions in hospital use, but no significant difference in costs. ‘Self-management’ interventions showed small but significant improvements in QoL and small but significant reductions in hospital use, but no significant difference in costs.

Analyses of studies for patients with cardiovascular problems

The studies identified in cardiovascular problems are detailed in Figure 12. 134–137,201–247

FIGURE 12.

Flow chart of studies in patients with cardiovascular problems.

Figures 13 and 14 show the permutation plots for patients with cardiovascular problems.

FIGURE 13.

Permutation plot: cardiovascular (hospital use and QoL).

FIGURE 14.

Permutation plot: cardiovascular (costs and QoL).

Most studies were in the bottom right quadrant of the plots, reporting improvements or no differences on QoL and hospital use.

In analyses including all studies, self-management support interventions for patients with cardiovascular problems were associated with small but significant improvements in QoL. Variation across trials was moderate (Figure 15).

FIGURE 15.

Forest plot: cardiovascular (QoL). CI, confidence interval; ES, effect size; GC, group counselling intervention; TC, individual telephone counselling intervention; TH, telehealth post-discharge support; VH, video health post-discharge support. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with cardiovascular problems were associated with small but significant reductions in hospital use. Variation across trials was high (Figure 16).

FIGURE 16.

Forest plot: cardiovascular (hospital use). CI, confidence interval; ES, effect size; GC, group counselling intervention; TC, individual telephone counselling intervention; TH, telehealth post-discharge support; VH, video health post-discharge support. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with cardiovascular problems were associated with small but significant reductions in costs. Variation across trials was moderate (Figure 17).

FIGURE 17.

Forest plot: cardiovascular (costs). CI, confidence interval; ES, effect size.

In analyses exploring the impact of different types of self-management support, there was evidence that ‘case management’ interventions produced small but significant improvements in QoL and reductions in hospital use and costs. ‘Self-management’ interventions showed small but significant improvements in QoL and reductions in hospital use, but no significant reductions in costs.

Analyses of studies for patients with arthritis problems

The studies identified in respiratory problems are detailed in Figure 18. 146,148–151,153–155,248,249

FIGURE 18.

Flow chart of studies in patients with arthritis problems.

Figures 19 and 20 show the permutation plots for patients with arthritis problems.

FIGURE 19.

Permutation plot: arthritis (hospital use and QoL).

FIGURE 20.

Permutation plot: arthritis (total costs and QoL).

Most studies were in the top right quadrant of the plots, reporting improvements in QoL and increases in costs.

In analyses including all studies, self-management support interventions for patients with arthritis problems were associated with small but significant improvements in QoL. There was no significant variation across trials beyond that expected by chance (Figure 21).

FIGURE 21.

Forest plot: arthritis (QoL). CI, confidence interval; CIn, combined (education and social support) intervention; CR, combined (group and individual) rehabilitation; EI, educational intervention; ES, effect size; GR, group rehabilitation; IR, individual rehabilitation; SSI, social support intervention. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with arthritis problems were associated with non-significant reductions in hospital use. Variation across trials was moderate (Figure 22).

FIGURE 22.

Forest plot: arthritis (hospital use). CI, confidence interval; CIn, combined (education and social support) intervention; ES, effect size. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with arthritis problems were associated with non-significant increases in costs. Variation across trials was moderate (Figure 23).

FIGURE 23.

Forest plot: arthritis (costs). CI, confidence interval; CIn, combined (education and social support) intervention; CR, combined (group and individual) rehabilitation; EI, educational intervention; ES, effect size; GR, group rehabilitation; IR, individual rehabilitation; SSI, social support intervention. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses exploring the impact of different types of self-management support, there was evidence that ‘case management’ interventions produced non-significant improvements in QoL and small but significant reductions in hospital use and costs, while ‘self-management’ interventions had small but significant benefits on QoL, non-significant effects on hospital use and small but significant increases in costs.

Analyses of studies for patients with pain problems

The studies identified in pain problems are detailed in Figure 24. 156–160,250–256

FIGURE 24.

Flow chart of studies in patients with pain problems.

Figures 25 and 26 show the permutation plots for patients with pain problems.

FIGURE 25.

Permutation plot: pain (hospital use and QoL).

FIGURE 26.

Permutation plot: pain (total costs and QoL).

Most studies were in the top right quadrant of the plots, reporting improvements in QoL and increases in utilisation.

In analyses including all studies, self-management support interventions for patient with pain problems were associated with small but significant improvements in QoL. Variation across trials was low (Figure 27).

FIGURE 27.

Forest plot: pain (QoL). CBT, group cognitive–behavioural therapy intervention; CI, confidence interval; CIn, combined intervention; DI, dietary intervention; EGI, exercise and graded activity intervention; EI, exercise intervention; ES, effect size; II, information-only intervention; IP, inpatient pain management programme; LI, lay-led self-care intervention; OP, outpatient pain management programme; PI, psychologist-led self-care intervention; TCBT, telephone-delivered cognitive–behavioural therapy; WEI, work-based exercise and graded activity intervention. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with pain problems were associated with non-significant reductions in hospital use. Variation across trials was low (Figure 28).

FIGURE 28.

Forest plot: pain (hospital use). CI, confidence interval; EGI, exercise and graded activity intervention; ES, effect size; WEI, work-based exercise and graded activity intervention. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with pain problems were associated with non-significant increases in costs. Variation across trials was high (Figure 29).

FIGURE 29.

Forest plot: pain (costs). CI, confidence interval; CIn, combined (telephone-delivered cognitive–behavioural therapy and exercise) intervention; EGI, exercise and graded activity intervention; EI, exercise intervention; ES, effect size; LI, lay-led self-care intervention; PI, psychologist-led self-care intervention; TCBT, telephone-delivered cognitive–behavioural therapy; WEI, work-based exercise and graded activity intervention. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses exploring the impact of different types of self-management support, the effects of ‘case management’ interventions on QoL and hospital use were non-significant, but showed moderate and significant reductions in costs. ‘Self-management’ interventions showed small but significant improvements in QoL but non-significant effects in costs.

Analyses of studies for patients with diabetes problems

The studies identified in diabetes problems are detailed in Figure 30. 130–133,257–262

FIGURE 30.

Flow chart of studies in patients with diabetes problems.

Figures 31 and 32 show the permutation plots for patients with diabetes problems.

FIGURE 31.

Permutation plot: diabetes (hospital use and QoL).

FIGURE 32.

Permutation plot: diabetes (total costs and QoL).

Most studies were in the bottom right quadrant of the plots, reporting improvements in QoL and equal or decreased utilisation.

In analyses including all studies, self-management support interventions for patients with diabetes problems were associated with significant improvements in QoL. Variation across trials was high (Figure 33).

FIGURE 33.

Forest plot: diabetes (QoL). CI, confidence interval; ES, effect size. Note: when studies are reported twice, this refers to different arms within the same study. ‘Low intensity’ is use of blood glucose meter and advice to contact GP for interpretation; ‘high intensity’ is use of blood glucose meter and training to interpret results.

In analyses including all studies, self-management support interventions for patients with diabetes problems were associated with non-significant reductions in hospital use. Variation across trials was moderate (Figure 34).

FIGURE 34.

Forest plot: diabetes (hospital use). CI, confidence interval; ES, effect size. Note: when studies are reported twice, this refers to different arms within the same study. ‘Low intensity’ is use of blood glucose meter and advice to contact GP for interpretation; ‘high intensity’ is use of blood glucose meter and training to interpret results.

In analyses including all studies, self-management support interventions for patients with diabetes problems were associated with non-significant reductions in costs. Variation across trials was moderate (Figure 35).

FIGURE 35.

Forest plot: diabetes (costs). CI, confidence interval; ES, effect size. ‘Low intensity’ is use of blood glucose meter and advice to contact GP for interpretation; ‘high intensity’ is use of blood glucose meter and training to interpret results.

In analyses exploring the impact of different types of self-management support, ‘self-management’ interventions showed significant improvements in QoL but non-significant reductions in hospital use or costs.

Analyses of studies for patients with mental health problems

The studies identified in mental health problems are detailed in Figure 36. 138–143,145,165,263–281

FIGURE 36.

Flow chart of studies in patients with mental health problems.

Figures 37 and 38 show the permutation plots for patients with mental health problems.

FIGURE 37.

Permutation plot: mental health (hospital use and QoL).

FIGURE 38.

Permutation plot: mental health (total costs and QoL).

Most studies were in the right quadrant of the plots, reporting improvements in QoL with varied effect on utilisation or costs.

In analyses including all studies, self-management support interventions for patients with mental health problems were associated with small but significant improvements in QoL. Variation across trials was moderate (Figure 39).

FIGURE 39.

Forest plot: mental health (QoL). CCM, case management supported by a consumer; CI, confidence interval; ES, effect size; mail, internet self-help and mailed reminders; PCM, case management supported by a professional; TCM, telephone care management; tel, internet self-help and telephone reminders; TPCM, telephone psychotherapy and care management. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with mental health problems were associated with non-significant reductions in hospital use. Variation across trials was low (Figure 40).

FIGURE 40.

Forest plot: mental health (hospital use). CCM, case management supported by a consumer; CI, confidence interval; ES, effect size; PCM, case management supported by a professional; TCM, telephone care management; TPCM, telephone psychotherapy and care management. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with mental health problems were associated with non-significant increases in costs. Variation across trials was low (Figure 41).

FIGURE 41.

Forest plot: mental health (costs). CI, confidence interval; ES, effect size; TCM, telephone care management; TPCM, telephone psychotherapy and care management. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses exploring the impact of different types of self-management support, there was evidence that ‘case management’ interventions produced significant improvements in QoL but no significant reductions in hospital use and costs. ‘Self-management’ interventions showed no significant improvements in QoL and no significant reductions in hospital use or costs.

Analyses of studies for patients with mixed problems

The studies identified in mixed problems are detailed in Figure 42. 162,163,282–290

FIGURE 42.

Flow chart of studies in patients with mixed problems.

Figures 43 and 44 show the permutation plots for patients with mixed problems.

FIGURE 43.

Permutation plot: mixed (hospital use and QoL).

FIGURE 44.

Permutation plot: mixed (total costs and QoL).

Most studies were in the right quadrant of the plots, reporting improvements in QoL with no effect on utilisation or costs.

In analyses including all studies, self-management support interventions for patients with mixed problems were associated with small but significant improvements in QoL. Variation across trials was moderate (Figure 45).

FIGURE 45.

Forest plot: mixed (QoL). CDSMP home, peer-led, face-to-face CDSMP variant; CDSMP phone, telephone-based CDSMP variant; CI, confidence interval; ES, effect size. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with mixed problems were associated with small but significant reductions in hospital use. Variation across trials was moderate (Figure 46).

FIGURE 46.

Forest plot: mixed (hospital use). Counselling, face-to-face counselling with a nurse; CDSMP home, peer-led, face-to-face CDSMP variant; CDSMP telephone, telephone-based CDSMP variant; CI, confidence interval; ES, effect size; telephone counselling, telephone counselling with a nurse. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with mixed problems were associated with non-significant increases in costs. There was no significant variation across trials beyond that expected by chance (Figure 47).

FIGURE 47.

Forest plot: mixed (costs). Counselling, face-to face counselling with a nurse; CDSMP home, peer-led, face-to-face CDSMP variant; CDSMP telephone, telephone-based CDSMP variant; CI, confidence interval; ES, effect size; telephone counselling, telephone counselling with a nurse. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses exploring the impact of different types of self-management support, ‘case management’ interventions produced non-significant effects on QoL, hospital use and costs. ‘Self-management’ interventions showed non-significant improvements in QoL, small but significant reductions in hospital use and non-significant increases in costs.

Analyses of studies for patients with long-term conditions in PRISMS cluster 1: long-term conditions with marked variability in symptoms over time (see Table 1)

Figures 48 and 49 show the permutation plots for patients in PRISMS cluster 1: long-term conditions with marked variability in symptoms over time.

FIGURE 48.

Permutation plot: PRISMS cluster 1 (hospital use and QoL).

FIGURE 49.

Permutation plot: PRISMS cluster 1 (total costs and QoL).

Most studies were in the right quadrant of the plots, reporting improvements in QoL with mixed effects on utilisation or costs.

In analyses including all studies, self-management support interventions for patients with cluster 1 conditions were associated with small but significant improvements in QoL. Variation across trials was moderate (Figure 50).

FIGURE 50.

Forest plot: PRISMS cluster 1 (QoL). CBT, group cognitive–behavioural therapy intervention; CCM, case management supported by a consumer; CI, confidence interval; CIn, combined intervention; CR, combined (group and individual) rehabilitation; DI, dietary intervention; EGI, exercise and graded activity intervention; EI, exercise intervention; ES, effect size; GR, group rehabilitation; II, information-only intervention; IIMR, internet self-help intervention with mail reminders; IITR, internet self-help intervention with telephone reminders; IP, inpatient pain management programme; IR, individual rehabilitation; LI, lay-led self-care intervention; OP, outpatient pain management programme; PCM, case management supported by a professional; PI, psychologist-led self-care intervention; TCBT, telephone-delivered cognitive–behavioural therapy; TCM, telephone care management; TPCM, telephone psychotherapy and care management; WEI, work-based exercise and graded activity intervention. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with cluster 1 conditions were associated with non-significant reductions in hospital use. Variation across trials was moderate (Figure 51).

FIGURE 51.

Forest plot: PRISMS cluster 1 (hospital use). CCM, case management supported by a consumer; CI, confidence interval; EGI, an exercise and graded activity intervention; ES, effect size; PCM, case management supported by a professional; TCM, telephone care management; TPCM, telephone psychotherapy and care management; WEI, work-based exercise and graded activity intervention. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with cluster 1 conditions were associated with non-significant increases in costs. Variation across trials was moderate (Figure 52).

FIGURE 52.

Forest plot: PRISMS cluster 1 (costs). CBT, group cognitive–behavioural therapy intervention; CI, confidence interval; CIn, combined (telephone-delivered cognitive–behavioural therapy and exercise) intervention; CR, combined (group and individual) rehabilitation; EGI, exercise and graded activity intervention; EI, exercise intervention; ES, effect size; GR, group rehabilitation; IR, individual rehabilitation; LI, lay-led self-care intervention; PI, psychologist-led self-care intervention; TCBT, telephone-delivered cognitive–behavioural therapy; TCM, telephone care management; TPCM, telephone psychotherapy and care management; WEI, work-based exercise and graded activity intervention. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses exploring the impact of different types of self-management support, ‘case management’ interventions produced small but significant improvements in QoL and had no significant effects in hospital use and costs. ‘Self-management’ interventions showed very small but significant improvements in QoL and no significant effects in hospital use or costs.

Analyses of studies for patients with long-term conditions in PRISMS cluster 3: ongoing long-term conditions with exacerbations (see Figure 4)

Figures 53 and 54 show the permutation plots for patients in PRISMS cluster 3: ongoing long-term conditions with exacerbations.

FIGURE 53.

Permutation plot: PRISMS cluster 3 (hospital use and QoL).

FIGURE 54.

Permutation plot: PRISMS cluster 3 (total costs and QoL).

Most studies were in the bottom right quadrant of the plots, reporting improvements in QoL with reductions in utilisation or costs.

In analyses including all studies, self-management support interventions for patients with cluster 3 conditions were associated with small but significant improvements in QoL. Variation across trials was moderate (Figure 55).

FIGURE 55.

Forest plot: PRISMS cluster 3 (QoL). CI, confidence interval; ES, effect size; TH, telehealth post-discharge support; VH, video health post-discharge support. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with cluster 3 conditions were associated with small but significant reductions in hospital use. Variation across trials was moderate (Figure 56).

FIGURE 56.

Forest plot: PRISMS cluster 3 (hospital use). CI, confidence interval; CM, nurse-assisted collaborative management; ES, effect size; MM, nurse-assisted medical management. Note: when studies are reported twice, this refers to different arms within the same study.

In analyses including all studies, self-management support interventions for patients with cluster 3 conditions were associated with small but significant reductions in costs. Variation across trials was moderate (Figure 57).

FIGURE 57.

Forest plot: PRISMS cluster 3 (costs). CI, confidence interval; ES, effect size.

In analyses exploring the impact of different types of self-management support, there was evidence that ‘case management’ interventions produced small but significant improvements in QoL and small but significant reductions in hospital use and costs. ‘Self-management’ interventions showed small but significant improvements in QoL and reductions in hospital use but no significant reductions in costs.

Summary of the results

The core results are summarised in Tables 5–7.

TABLE 5 - Summary: hospital use. Shaded cells have an effect size of ≥ 0.2 and are statistically significant

CI, confidence interval; ES, effect size; N/A, not applicable.

Condition	Combined QoL, overall ES (95% CI, n, I2)	Self-management QoL, overall ES (95% CI, n, I2)	Case management QoL, overall ES (95% CI, n, I2)	Combined hospital use, overall ES (95% CI, n, I2)	Self-management hospital use, overall ES (95% CI, n, I2)	Case management hospital use, overall ES (95% CI, n, I2)
Respiratory	0.27 (0.16 to 0.37, 34, moderate)	0.28 (0.16 to 0.41, 27, moderate)	0.19 (0.02 to 0.36, 7, low)	−0.21 (−0.32 to −0.09, 31, moderate)	−0.19 (−0.33 to −0.05, 25, moderate)	−0.26 (−0.42 to −0.10, 6, zero)
Cardiac	0.21 (0.14 to 0.28, 40, moderate)	0.19 (0.10 to 0.27, 27, moderate)	0.26 (0.12 to 0.39, 13, moderate)	−0.23 (−0.34 to −0.13, 38, high)	−0.20 (−0.33 to −0.07, 25, high)	−0.29 (−0.47 to −0.11, 13, high)
Arthritis	0.16 (0.07 to 0.26, 11, zero)	0.17 (0.07 to 0.27, 7, zero)	0.13 (−0.13 to 0.39, 4, zero)	−0.06 (−0.22 to 0.10, 6, moderate)	−0.02 (−0.19 to 0.16, 5, moderate)	−0.24 (−0.48 to 0.00, 1, N/A)
Pain	0.13 (0.04 to 0.21, 19, low)	0.12 (0.02 to 0.22, 15, low)	0.20 (−0.10 to 0.50, 4, zero)	−0.03 (−0.34 to 0.28, 3, low)	No data reported	−0.03 (−0.34 to 0.28, 3, low)
Diabetes	0.44 (0.14 to 0.75, 10, high)	0.44 (0.14 to 0.75, 10, high)	No data reported	−0.12 (−0.29 to 0.05, 5, moderate)	−0.12 (−0.29 to 0.05 5, moderate)	No data reported
Mental health	0.22 (0.11 to 0.33, 26, high)	0.05 (−0.07 to 0.17, 15, moderate)	0.38 (0.24 to 0.51, 11, high)	−0.03 (−0.10 to 0.04, 21, low)	−0.03 (−0.16 to 0.10, 13, moderate)	−0.04 (−0.13 to 0.05, 8, zero)
Mixed	0.13 (0.02 to 0.24, 10, moderate)	0.11 (−0.03 to 0.24, 7, moderate)	0.22 (−0.03 to 0.48, 3, moderate)	−0.12 (−0.20 to −0.03, 11, moderate)	−0.09 (−0.17 to −0.02, 8, zero)	−0.13 (−0.40 to 0.14, 3, moderate)

TABLE 6 - Summary: costs. Shaded cells have an effect size of ≥ 0.2 and are statistically significant

CI, confidence interval; ES, effect size; N/A, not applicable.

	Combined QoL, overall ES (95% CI, n, I2)	Self-management QoL, overall ES (95% CI, n, I2)	Case management QoL, overall ES (95% CI, n, I2)	Combined costs, overall ES (95% CI, n, I2)	Self-management costs, overall ES (95% CI, n, I2)	Case management costs, overall ES (95% CI, n, I2)
Respiratory	0.27 (0.16 to 0.37, 34, moderate)	0.28 (0.16 to 0.41, 27, moderate)	0.19 (0.02 to 0.36, 7, low)	0.09 (−0.14 to 0.33, 9, high)	0.09 (−0.19 to 0.37, 6, high)	0.09 (−0.46 to 0.64, 3, high)
Cardiac	0.21 (0.14 to 0.28, 40, moderate)	0.19 (0.11 to 0.27, 27, moderate)	0.26 (0.12 to 0.39, 13, moderate)	−0.25 (−0.47 to −0.04, 9, moderate)	−0.25 (−0.82 to 0.32, 4, high)	−0.27 (−0.44 to −0.10, 5, moderate)
Arthritis	0.16 (0.07 to 0.26, 11, zero)	0.17 (0.07 to 0.27, 7, zero)	0.13 (−0.13 to 0.39, 4, zero)	0.07 (−0.07 to 0.20, 11, moderate)	0.14 (0.01 to 0.27, 8, moderate)	−0.28 (−0.53 to −0.03, 3, zero)
Pain	0.13 (0.04 to 0.21, 19, zero)	0.12 (0.02 to 0.22, 15, low)	0.20 (−0.11 to 0.51, 4, zero)	0.07 (−0.13 to 0.28, 13, high)	0.15 (−0.06 to 0.36, 11, high)	−0.41 (−0.74 to −0.08), 2, zero)
Diabetes	0.44 (0.14 to 0.75, 10, high)	0.44 (0.14 to 0.75, 10, high)	No data reported	0.19 (−0.18 to 0.55, 4, moderate)	0.19 (−0.18 to 0.55, 4, moderate)	No data reported
Mental health	0.22 (0.11 to 0.33, 26, high)	0.05 (−0.07 to 0.17, 15, moderate)	0.38 (0.24 to 0.51, 11, high)	0.03 (−0.05 to 0.11, 14, low)	−0.04 (−0.23 to 0.15, 4, moderate)	0.05 (−0.04 to 0.13, 10, low)
Mixed	0.13 (0.02 to 0.24, 10, moderate)	0.11 (−0.03 to 0.24, 7, moderate)	0.22 (−0.03 to 0.48, 3, low)	0.06 (−0.02 to 0.13, 7, zero)	0.05 (−0.04 to 0.13, 6, zero)	0.11 (−0.09 to 0.31, 1, N/A)

TABLE 7 - Outcomes in all studies and those reporting both outcomes

CI, confidence interval; ES, effect size.

Disease, outcome, analysis	Combined QoL, overall ES (95% CI, n, I2)	Self-management QoL, overall ES (95% CI, n, I2)	Case management QoL, overall ES (95% CI, n, I2)	Combined utilisation, overall ES (95% CI, n, I2)	Self-management utilisation, overall ES (95% CI, n, I2)	Case management utilisation, overall ES (95% CI, n, I2)
Respiratory, hospital use, all	0.27 (0.16 to 0.37, 34, moderate)	0.28 (0.16 to 0.41, 27, moderate)	0.19 (0.02 to 0.36, 7, low)	−0.21 (−0.32 to −0.09, 31, moderate)	−0.19 (−0.33 to −0.05, 25, moderate)	−0.26 (−0.42 to −0.10, 6, zero)
Respiratory, hospital use, both	0.28 (0.14 to 0.43, 22, moderate)	0.31 (0.14 to 0.48, 17, high)	0.18 (−0.07 to 0.43, 5, moderate)	−0.26 (−0.41 to −0.11, 22, moderate)	−0.25 (−0.44 to −0.07, 17, moderate)	−0.29 (−0.48 to −0.09, 5, zero)
Cardiac, hospital use, all	0.21 (0.14 to 0.28, 40, moderate)	0.19 (0.10 to 0.27, 27, moderate)	0.26 (0.12 to 0.39, 13, moderate)	−0.23 (−0.34 to −0.13, 38, high)	−0.20 (−0.33 to −0.07, 25, high)	−0.29 (−0.47 to −0.11, 13, high)
Cardiac, hospital use, both	0.17 (0.08 to 0.26, 26, moderate)	0.15 (0.06 to 0.23, 18, low)	0.21 (0.00 to 0.41, 8, moderate)	−0.23 (−0.38 to −0.08, 26, high)	−0.18 (−0.35 to 0.00, 18, high)	−0.36 (−0.66 to −0.05, 8, high)
Pain, costs, all	0.13 (0.04 to 0.21, 19, zero)	0.12 (0.02 to 0.22, 15, low)	0.20 (−0.11 to 0.50, 4, zero)	0.07 (−0.13 to 0.28, 13, high)	0.15 (−0.06 to 0.36, 11, high)	−0.41 (−0.74 to −0.08, 2, zero)
Pain, costs, both	0.13 (0.00 to 0.25, 12, moderate)	0.13 (−0.01 to 0.27, 10, moderate)	0.12 (−0.21 to 0.45, 2, zero)	0.10 (−0.12 to 0.31, 12, high)	0.18 (−0.05 to 0.41, 10, high)	−0.41 (−0.74 to −0.08, 2, high)
Mental health, hospital use, all	0.22 (0.11 to 0.33, 26, high)	0.05 (−0.07 to 0.17, 15, moderate)	0.38 (0.24 to 0.51, 11, high)	−0.03 (−0.10 to 0.04, 21, low)	−0.03 (−0.16 to 0.10, 13, moderate)	−0.04 (−0.13 to 0.05, 8, zero)
Mental health, hospital use, both	0.18 (0.02 to 0.33, 18, high)	−0.04 (−0.20 to 0.12, 10, moderate)	0.38 (0.18 to 0.57, 8, high)	−0.01 (−0.08, 0.06, 18, zero)	0.03 (−0.10 to 0.15, 10, low)	−0.04 (−0.13 to 0.05, 8, zero)

Table 5 shows the impact of self-management support on hospital use and QoL. Results are highlighted in the table that show an effect size of 0.2 (at least a ‘small’ effect by current convention), for which the effect is statistically significant. As can be seen from Table 5, such impacts are found in a number of cells in relation to QoL, but are restricted to interventions in respiratory and cardiovascular populations in relation to hospital use.

Table 6 is structured in the same way, but details the impact of self-management support on costs and QoL. Significant reductions in costs are found only in relation to cardiovascular problems overall, and in case management interventions in cardiovascular, pain and arthritis problems.

It should be noted that some of the differences between Tables 5 and 6 reflect changes in the number of studies included in the analysis and associated precision of the estimates.

Table 7 represents a sensitivity analyses, testing whether or not the broad results in Tables 5 and 6 endure when analyses are restricted to studies which report both QoL and utilisation/cost data. The results were very similar, suggesting that the main analyses were robust.

Study outcomes and risk of bias

Table 8 shows the effects of self-management support on the three core outcomes, grouped according to our risk of bias measure (based on reported allocation concealment). Studies judged at high risk of bias reported better effects on QoL and greater reductions in hospitalisation and costs than those judged at low risk of bias, although they were also associated with increases in total costs.

TABLE 8 - Overall effects by risk of bias

CI, confidence interval.

Outcome	Overall effect size (I2, 95% CI)	Effect size (high risk of bias) (I2, 95% CI)	Effect size (low risk of bias) (I2, 95% CI)
QoL	0.22 (0.17 to 0.26)	0.23 (0.18 to 0.29)	0.18 (0.12 to 0.25)
Hospital use	−0.16 (−0.20 to −0.11)	−0.18 (−0.24 to −0.11)	−0.10 (−0.16 to −0.04)
Costs	0.02 (−0.05 to 0.08)	0.07 (−0.05 to 0.18)	−0.01 (−0.09 to −0.07)

Small-study bias

The funnel plot for the studies reporting QoL outcomes is presented in Figure 58. The plot was symmetrical and the regression statistics did not show evidence of small-study bias [intercept 0.47, 95% confidence interval (CI) –0.16 to 1.10; p = 0.14].

FIGURE 58.

Funnel plot: QoL.

The funnel plot for the studies reporting hospital use outcomes is presented in Figure 59. The plot was not symmetrical and the regression statistics showed evidence of small-study bias (intercept –0.91, 95% CI –1.55 to –0.27; p = 0.01).

FIGURE 59.

Funnel plot: hospital use.

The funnel plot for the studies reporting costs is presented in Figure 60. The plot was symmetrical and the regression statistics did not show evidence of small-study bias (intercept –0.46, 95% CI –1.71 to 0.79; p = 0.47).

FIGURE 60.

Funnel plot: total costs.

External validity and reach

The degree to which the results of a trial conducted in a particular setting can be generalised to a different setting (that is the external validity) is always an issue in the interpretation of findings of systematic reviews. The impact of variation in context may be greater when considering complex service-related interventions that are designed to impact on individual behaviour, or when the focus is on utilisation outcomes that may themselves reflect important differences in the context in which the study is run.

To explore this issue, we calculated a permutation plot for the hospitalisation data, identifying UK studies in the plot to assess whether the pattern of results was different. The plot is shown in Figure 61.

FIGURE 61.

Permutation plot: hospitalisation (UK vs. other studies).

The comparison is somewhat crude, as there may be similarities in the systems of care between the UK and other countries (e.g. the Dutch health-care system is similar in having a strong primary care focus). Nevertheless, there was no strong evidence from the plot that the pattern of findings about the relationship between QoL outcomes and utilisation was markedly different in UK studies from the wider international literature.

We also calculated the overall effect sizes for QoL, hospitalisation and total costs by country, to assess whether or not the effect of self-management interventions on these individual outcomes varied markedly in UK and non-UK settings. The results are shown in Table 9.

TABLE 9 - Overall effects by country

Outcome	Overall effect size (95% CI)	Effect size, UK studies (95% CI)	Effect size, non-UK studies (95% CI)
QoL	0.22 (0.17 to 0.26)	0.10 (0.05 to 0.14)	0.25 (0.19 to 0.30)
Hospital use	−0.16 (−0.20 to −0.11)	−0.23 (−0.35 to −0.11)	−0.14 (−0.19 to −0.09)
Costs	0.02 (−0.05 to 0.08)	0.13 (0.02 to 0.24)	−0.04 (−0.12 to 0.04)

The results suggest that studies in the UK demonstrated smaller effects on QoL. Conversely, studies in the UK demonstrated larger reductions in hospitalisation, but those were not matched by cost data, for which UK studies showed a moderate increase in overall costs. It should be noted that these differences are associations only and may reflect other differences in studies conducted in the UK, other than the context.

The original study protocol sought to assess studies according to the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework (http://re-aim.org/291), in terms of the ‘percentage and risk characteristics of persons who receive or are affected by a policy or program’. 292,293 Generally, data on such issues are poorly reported in trials and often the data that are reported are not comparable between studies. We extracted data from trials on the proportion of eligible patients who did not take part and those data are presented in Appendix 6. However, interpretation of such data is difficult, as it requires knowledge of the exact recruitment procedures involved for effective comparison.

Chapter 5 Conclusions and recommendations

Acknowledgements

We thank Kate Light and the staff at the Centre for Reviews and Dissemination, University of York, York, UK, for their assistance with the searches, and Jacqui Harte, University of Manchester, Manchester, UK, for assistance with the report. We would also like to thank Ailsa Donnelly and Kris Mackay for their involvement in the PRISMS workshop.

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Appendix 1 Summary of the review protocol

(PDF download)

Appendix 2 Database search strategy

Appendix 3 Economic checklists

Appendix 4 Details of individual studies: context