This systematic review of disparities related to SMS interventions has reinforced observations (18-20, 22, 25) that there is a lack of research in this area. Although many studies of low SES groups have been undertaken, very few have focused on whether the outcomes compare favourably to those in higher SES groups. There are practical and statistical challenges in comparing population subgroups. Many studies had SES groupings that were fairly homogenous, limiting the ability to compare outcomes within the analysis, and almost all subgroup analyses were insufficiently powered. Larger studies and co-operation between different study populations are needed so that there is a more distinct contrast between SES levels across groups.
Responses to study questions
1. Is there evidence that SES influences participation rates in SMS interventions?
This review confirms that low SES groups are significantly less likely to participate in SMS interventions (42-46). Thus, healthcare disparity is increasing before an intervention even commences. In order to reach those who need the intervention, targeted recruitment and retention strategies will be needed. Self-selection runs the risk of spending limited resources on those who need them least (46).
2. Is there evidence that SES influences rates of retention or dropout from SMS interventions?
The findings in relation to retention and dropout are less clear-cut, with few studies and small sample sizes. Social factors do appear to be important (49-51), although a simple measure of SES may not capture the barriers to engagement.
3: Is there evidence that SES affects clinical, behavioural or other specified outcomes following SMS interventions?
With the limited number of high-quality studies available, there was some evidence that SES does affect outcomes following SMS interventions, depending on the type of intervention on offer. No trends were observed in terms of the SM components, which varied little between studies, or the type of service providers involved.
Programme structure (group or individual) did seem to affect both dropout rates and outcomes, with fewer benefits observed in the group interventions. In the few programmes that recorded dropout by SES, it appeared that attrition was also greater from group programmes (see Table 3). High rates of dropout from group programmes have been reported in several reviews of CD interventions in low SES and other vulnerable groups (21, 59), while other reviews (13, 60, 61) have noted that individually tailored interventions appear to reduce disparity. Other authors have noted that although group programmes provide beneficial social support and peer modelling (5), they can also present many barriers to a low SES population who may have less flexibility in terms of work, transport or caring demands (21, 59). In the current review, interventions over longer time periods (6-12 months) also seemed to be more effective at reducing disparity (53, 56, 57), consistent with a CD review on similar populations (13).
Interpretation of findings
1. ‘Low SES’ is a heterogenous group
This review suggests that SMS interventions may impact differently on low SES populations, and that more individualised treatment over longer time periods may be needed. Some writers have suggested that SES could be used as a ‘high risk’ predictor to identify those needing an earlier or more intensive intervention (23, 62), although this encompasses a large population group and has significant resource implications, emphasising the need for appropriate targeting of interventions.
Data from the current review indicates that low SES groups are heterogeneous, with additional factors such as literacy, social stressors and social capital influencing SM ability, engagement, health outcomes (49, 50, 53, 57) and thus disparity. Therefore, some low SES groups may benefit simply from better marketing of and access to generic SM courses (45) and lower-level interventions, while others will require a more intensive, tailored approach. The ability to accurately identify these groups, perhaps by using a triage instrument, could lead to more effective resource allocation, increased participation and better outcomes in terms of both efficacy and equity.
2. Are self-management mechanisms different in low SES populations?
Few studies reviewed described the theory behind the proposed SMS intervention, as noted in other reviews of SMS (12, 63), although several referred to the role of self-efficacy (40, 54, 58, 64), as described in Bandura’s social-cognitive theory (4, 5). The studies which targeted a low SES or otherwise diverse population did note particular challenges for disadvantaged groups in terms of knowledge or literacy (47, 53, 56, 57), and those which adapted to these challenges often had better outcomes. In contrast, ‘one size fits all’ programmes (45, 46, 54, 58) had fewer benefits, and in some cases increased disparity.
SMS approaches informed only by self-efficacy have been criticised as overly individualistic (10, 11, 15) and it has been observed that the relationship between self-efficacy and self-management ability is weaker in vulnerable groups (65), indicating that other barriers play an important part. Furthermore, since the development of self-efficacy depends both on one’s behaviour and on social/environmental feedback (66), several authors (11, 58) have suggested that increasing self-efficacy may be harder if environmental feedback (e.g. job or housing insecurity) negates a belief in control over one’s circumstances.
3. What other factors are important for self-management in low SES groups?
This suggests that for SMS interventions to be effective in low SES populations, attention should be paid to other factors that influence self-management ability. Health provider/system issues (67, 68); resources (literacy, financial, job/carer demands) (67, 69-71); and condition demands (multimorbidity, treatment burden) (48, 71, 72) have been consistently identified in qualitative reviews as barriers to self-management. Each of these factors will impact disproportionately on a low SES population. Health providers/systems can be less accessible due to cost, literacy levels and a limited understanding of the social determinants of health by providers (67, 68). Although few studies of SM in disadvantaged populations look at interventions at the health provider/system level (18, 21), it would seem a potentially effective way to reduce disparity without increasing the patient’s treatment burden.
Barriers related to resources and condition demands are far greater for the low SES population (73-75), who have fewer financial and social resources; higher levels of overall social complexity (job/housing insecurity, family demands, trauma history (3)); and higher rates of multimorbidity at earlier ages (76). They experience both more disease-related workload (treatment burden) and non-disease workload (life burden) (73, 77). Unfortunately, many SMS interventions, especially those requiring regular attendances or homework, will increase workload. Approaches that reduce patient workload or increase access to resources are rarely tried, but are likely to be important in low SES groups (73). Phone consultations, problem-solving of specific barriers, integrating healthcare with social services and directing interventions toward healthcare practitioners rather than individual patients can all reduce treatment burden and maximise resources. Coventry (76), in a qualitative study of SM and multimorbidity, identifies three factors required for engagement in SM: capacity (resources, knowledge and energy); responsibility (shared understanding between the patient and provider about how to manage the treatment workload) and motivation. All three are negatively impacted by low SES, yet many SMS interventions (10) aim to increase motivation without recognising responsibility or capacity, and thus may contribute to increasing disparity in low SES groups.