Study selection and characteristics

Forty-one studies were included (figure 1). Inter-rater reliability was good for title and abstract screening (κ=0.71), and full-text (κ=0.77).³²

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Figure 1

PRISMA flow diagram.

Online appendix 4 details key study characteristics. Studies were conducted in a range of countries, the most common being Canada, England and Spain. Sample sizes ranged from 232 to 13 581 191. Twenty-five studies included participants from across the life-course, while nine focused on adults aged 50 and over. Household SDoH included measures of household income, tenure and composition, self-reported by participants in all studies. Area-level SDoH included measures of socioeconomic deprivation and rurality, the former measured using validated indices (16/17 studies) and polling data (one study).

Defining and measuring multimorbidity

Most studies (34/41) defined multimorbidity as two or more conditions taken from a pre-specified list of ‘long-term’ or ‘chronic’ conditions. Eight also used three or more conditions as the cut-off and seven studies used a count of conditions as one outcome. Six studies examined if associations differed with multimorbidity type: multimorbidity specifically comprising physical and mental health conditions (physical–mental multimorbidity), physical conditions (physical-only multimorbidity) and mental health conditions (mental-only multimorbidity).

Across the 41 studies, the number of conditions included on the pre-specified list ranged from five conditions to 146 diagnostic clusters defined using O’Halloran’s criteria for chronicity.³³ Thirty-six of 41 studies included a mix of chronic physical and mental health conditions,^{4 22 34–67} while four included physical conditions only.^68–71 One study was unclear about the conditions included.⁷² To determine the presence of conditions, 17 studies used self-reported participant data, 17 screened electronic health records (EHRs), and six used a combination of the two. One study did not detail how they identified multimorbidity presence⁷⁰ (online appendix 4).

Study results

Household income (n=15): Thirteen studies consistently found multimorbidity prevalence or incidence was markedly and negatively associated with household income and, of all SDoH investigated, associations were consistently strongest for household income.^{39–41 43 48 53 60–62 65 66 68 69} Higher quality studies reported comparatively small estimated effect sizes, for example, Agborsangaya et al reported that an annual household income <$30 000 CAD was associated with a 2.39-fold increase in multimorbidity prevalence (95% CI 1.72 to 3.33) compared with ≥$100 000 CAD, after multiple adjustments.⁶² In contrast, Roberts et al—a lower quality study—reported chances of multimorbidity 4.4 times higher for participants with the lowest level of income compared with the highest in multivariate analyses (OR 4.4, 95% CI 3.6 to 5.5).⁵³

Two further studies—of low and moderate quality, respectively—examined problems managing household income and reported mixed results.^{52 59} Verest et al reported those with ‘lots of problems’ were over 5 times likely to have multimorbidity compared with those with ‘no problems’ (OR 5.36, 95% CI 4.88 to 5.88). Inequalities were similar by gender and ethnicity.⁵⁹ In contrast, Prazeres et al found no evidence of an association when screening EHRs.⁵²

See table 2 for key results and quality assessments for these 13 studies, and online appendix 4 for more details on study characteristics.

Household composition (n=7): Four studies measured household composition as living alone vs cohabiting and three studies measured it as living alone, living with various family members or living in other situations (including care homes).

Four cohort studies of older adults (50–84) reported mixed findings on the risk of living alone vs cohabiting.^{37 45 47 70} Two high-quality studies found living alone increased chances of multimorbidity vs living with others,^{37 70} for example, Cantarero-Prieto et al found living alone increased chances of multimorbidity by 20% (OR 1.20, 95% CI 1.04 to 1.39, p<0.05).⁷⁰ Whereas two other studies—high and moderate quality—found no evidence, living alone was associated with multimorbidity incidence.^{45 47} Differences in study characteristics such as methods of ascertaining multimorbidity presence could not explain these mixed findings.

Of the three studies with alternative measures of composition, one moderate-quality cross-sectional study found that odds of multimorbidity were over 2 times greater if not living with children vs living with children (OR 2.11, 95% CI 1.60 to 2.78; adjusted for age).⁶¹ Two further moderate-quality studies (one included solely older adults) found no evidence of any associations with multimorbidity when living alone was compared with living as a couple, with family/others or living in situations such as care homes.^{52 60}

See table 3 for key results and quality assessments for these seven studies, and online appendix 4 for more details on study characteristics.

Household tenure (n=4): Findings from studies investigating tenure were mixed and hard to compare given different reference groups and comparators; two studies compared homeowners and non-homeowners, one compared renters with homeowners and one compared social housing residents with homeowners, private renters and subsidised housing residents.^{60 65 66 69} All four were of moderate quality. Lebenbaum et al found the odds of multimorbidity decreased by 18% for homeowners compared with non-homeowners (OR 0.82, 95% CI 0.78 to 0.87, p<0.001),⁶⁶ whereas Johnson-Lawrence et al reported 19% higher odds for renters vs homeowners (OR 1.19, 95% CI 1.15 to 1.24).⁶⁹ In contrast, Schäfer et al—the only cohort study—found no evidence of an association between homeownership and count of conditions in older adults.⁶⁰ One study found that, compared with social housing residents, homeowners and private renters had 17% (OR 1.17, 95% CI 1.11 to 1.24, p=0.003) and 19% (OR 1.19, 95% CI 1.09 to 1.29, p=0.041) higher odds of multimorbidity, respectively.⁶⁵ Differences in study characteristics could not explain mixed findings.

See table 3 for key results and quality assessments for these four studies, and online appendix 4 for more details on study characteristics.

Household determinants in childhood (n=5): Two studies examined associations between paternal social class at birth and multimorbidity. Findings were mixed. One higher quality study from Johnston et al found lower paternal social class at birth was associated with increased multimorbidity in middle age.⁷² Conversely, one study of lower quality reported no association.³⁸

Two studies investigated associations between self-reported childhood financial hardships and multimorbidity and, again, findings were mixed.^{37 71} One higher quality study found no evidence of an association,³⁷ whereas one fairly low-quality study found evidence that the number of chronic conditions for those reporting hardships was 1.19 times that of those not reporting hardships (95% CI 1.07 to 1.32, p<0.001).⁷¹

One further study, moderate in quality, found the odds of multimorbidity increased by 40% among those who had experienced household dysfunction during childhood (eg, parental divorce) in multivariate analyses (OR 1.4, 95% CI 1.1 to 1.7, p<0.05).⁵⁶

Household primary language and education (n=2): One moderate-quality Australian study found associations between living in a household where English was the first language and multimorbidity prevalence differed depending on the source of health information.⁴³ One lower quality study found higher odds of multimorbidity for participants living in households where no residents had completed high school compared with post-secondary school education (OR 1.8, 95% CI 1.6 to 2.1, adjusting for age and sex).⁵³

Household rurality (n=7): Studies that investigated associations between residing in rural vs non-rural/urban areas and multimorbidity reported mixed results.^{36 43 52–54 66 70} Only two studies were high quality and provided clear rurality definitions; these both suggested odds of multimorbidity decreased with increased rurality.^{36 54} Conversely, two studies, one low⁵³ and one moderate quality⁴³ reported greater odds of multimorbidity with increased rurality. Three further studies found no evidence of any association.^{52 66 70} Aside from study quality, differences in study characteristics could not explain these mixed findings.

See table 3 for key results and quality assessments for these seven studies, and online appendix 4 for more details on study characteristics.

Area-level socioeconomic deprivation (n=17): Studies that investigated how the socioeconomic situation of participants’ residential area was associated with multimorbidity prevalence or incidence showed fairly consistent findings.^{4 22 34 35 39 42 44 46 49–51 54 55 57 63 64 67} In general, multimorbidity was higher for participants residing in areas of greater deprivation than those living in more affluent areas. Odds of multimorbidity prevalence were 42% higher for participants residing in the most vs the least deprived areas when available data were pooled (OR 1.42, 95% CI 1.41 to 1.42; figure 2). Differences in study quality could not explain differences in reported effect sizes across studies.

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Figure 2

Multimorbidity prevalence with quintiles of area-level deprivation (1=least deprived and 5=most deprived). Calculations conducted by authors and based on available and relevant pooled data from N=7 out of 17 studies.

Four studies found inequalities with area-level deprivation were greater among women than men.^{35 49–51} Only one low-quality study investigated how associations differed by ethnicity, and they found that inequalities were greater for Pacific vs Maori New Zealand residents.⁶⁷

Trust in neighbours (n=1): One fairly low-quality study found that participants who somewhat distrusted their neighbours had increased risk of developing multimorbidity within 11 years compared with those who strongly disagreed with the statement ‘One cannot trust each other here’ (RR 1.13, 95% CI 1.03 to 1.23).⁵⁸

Older adult populations and older adult sub-group analyses (n=21): Nine studies included samples of adults solely aged 50 and over.^{37 38 45–47 50 60 70 71} Five of these examined associations between household composition and multimorbidity (results described above).^{37 45 47 60 70} There were no differences in results for studies with older adults only compared with studies that included younger adults.

Thirteen studies examined whether associations differed with subgroups of age.^{4 22 35 36 42 44 46 50 51 53 57 61 63} Estimated effect sizes were greater in younger compared with older adults for household income—for example, Roberts et al reported greater odds of multimorbidity amongst 35–49 year olds compared with over 65s for those with the lowest income vs the highest (OR 7.5, 95% CI 4.0 to 13.7 vs OR 2.5, 95% CI 1.8 to 3.5, respectively; see table 2).⁵³ However, Agborsangaya et al (2012) found that associations between multimorbidity and not living with children (vs living with children) were greater for those ≥65 years of age compared with those 25–44, adjusting for sex and household income (OR 8.45, 95% CI 2.02 to 35.41 vs OR 2.00, 95% CI 1.29 to 3.02, respectively; see table 3).⁶¹ Ten studies found that differences in multimorbidity prevalence with area deprivation reduced in older age.^{4 22 35 42 44 46 50 51 57 63} Inequalities across deprivation categories were greater in middle age for general multimorbidity,^{4 22 35 42 44 51 57 63} in younger age groups for physical–mental multimorbidity^{4 44} and mental-only multimorbidity,⁴⁴ and in older age for physical-only multimorbidity.⁴⁴ One study examining household rurality reported data showing similar associations in older vs young groups³⁶ (see table 3).

Online appendix 2 outlines our full data extraction table for all 41 studies.

Possible explanations for our findings

Previous research has proposed that household factors are often overlooked in studies exploring SDoH, despite households (or families) influencing physical and mental health through various material and psychosocial factors.^73–75

In this review, we identified seven studies that investigated household composition and four that investigated household tenure. Composition studies presented mixed results; living alone was associated with increased multimorbidity in two studies and not associated in four. These studies included different reference groups and comparators, making them hard to compare. For example, ill-health greatly drives care home admissions⁷⁶ and therefore comparing ‘living alone’ with either ‘not living alone’ or ‘living in a care home’ would likely be comparing groups in different health, leading to differential associations between household composition and multimorbidity. One further study found living with children (vs not) was associated with increased chances of multimorbidity, and this effect was greater for over 65s. Chronic illness may give rise to older individuals residing with family and may lead younger individuals unable to (or decide not to) have children. Interestingly, none of the included studies examining household composition adjusted for care provision, which can differ considerably for those living with a partner, family or alone⁷⁷ and could plausibly influence the relationship between composition and multimorbidity. Further research should gather data on care provision and adjust accordingly. Unpicking whether social circumstances drive multimorbidity, or vice versa, also requires better designed longitudinal studies. This could aid the targeting of resources for prevention.

Studies investigating household tenure reported contradicting results; homeownership was associated with both increased and decreased chances of multimorbidity. Comparing these results was, again, complicated by different reference groups and comparators; however, study contexts may be more pertinent here. These studies were conducted in Hong Kong, Canada, USA and Germany. The degree of homeownership, and supply and conditions of social housing, may vary across these locations, for example, approximately 45% of the Hong Kong population lived in public housing in 2019 compared with 10% of the German population in 2017.^{78 79} This, plus other social circumstances, could profoundly influence the status and stigma associated with owning, renting, or residing in social housing across geographies and over time, differentially impacting health and associations between tenure and multimorbidity.^80–82

A minority of studies examined whether associations differed by age, gender or ethnicity. Findings suggest women experience greater inequality in multimorbidity prevalence with area-level deprivation, in line with research highlighting an increase in life expectancy inequality for UK women.⁸³ Prevalence with area-level deprivation was also greater for younger populations for physical–mental multimorbidity, unsurprising given the consistently high prevalence of mental ill-health among young, deprived communities.^{84 85} This, however, suggests that studies excluding mental health conditions from multimorbidity definitions or specifying multimorbidity as specifically crossing physical and mental health may report different associations than studies not. Future research should consider physical and mental dimensions of multimorbidity and examine whether associations differ by key demographics. Further avenues for future research should also examine the main explanatory factors, for example, whether individual socioeconomic factors (such as education) or behavioural factors (such as tobacco use) confound or explain any observed associations. This could also aid the development of tailored prevention and intervention strategies.

Lack of consensus around a multimorbidity definition is a consistently raised issue.⁸⁶ In this review, most studies defined multimorbidity as two or more chronic conditions (the most used definition in the literature⁸⁶); however, several also used a cut-off point of three or more or a count of conditions. To ascertain the presence of multimorbidity, the included studies used either self-reported data, data from EHRs or a combination of the two. This hampered effective comparisons of study findings, yet we found no evidence suggesting differences in findings could be explained by differences in multimorbidity definitions or measurement methods. There was also no variation in determinants of multimorbidity by measurement methods used. Consistent definitions of multimorbidity and consistent methods for ascertaining its presence are needed to improve the comparability of findings.

Strengths and limitations of this review

Strengths of this paper include the systematic inclusion of household SDoH, which has captured studies missed by previous reviews,^{11 15–18} and the careful assessment of each study for risk of four dimensions of bias using pre-specified criteria tailored to the study. Limitations include that we excluded the term ‘comorbidity’ and its linguistic variations from our search despite it being used interchangeably with ‘multimorbidity’.⁸⁷ While this may have missed some relevant literature, a subsequent ad hoc search in MedLine, that included this term, did not identify any additional, relevant hits. A large proportion of our included studies were also identified via citation searching. We believe that this is an intrinsic issue when conducting these types of reviews; in the literature, SDoH are referred to by the determinant of interest (eg, ‘rurality’) and search strategies need to pre-specify terms to search for these, potentially missing relevant studies. We also restricted our search to English-language publications and excluded studies conducted in low- and middle-income countries (LMICs) as the socioeconomic gradient in multimorbidity is reversed in LMICs.⁸⁸

Implications for policy, research and practice

Household determinants of multimorbidity other than income are often overlooked. Given the comparatively large effect sizes for household compared with area-level SDoH, our study suggests that strategies to tackle multimorbidity should consider household-level factors. There is also a need for additional studies in different geographical contexts to gain a better understanding of the role of household SDoH on multimorbidity. Policies aimed at reducing social inequalities could be important components of strategies to tackle multimorbidity.