Association of social isolation and loneliness with risk of incident hospital-treated infections: an analysis of data from the UK Biobank and Finnish Health and Social Support studies - The Lancet

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Study design and population

We assessed the association of loneliness and social isolation with hospital-treated infectious diseases using data from the prospective UK Biobank cohort study,

¹⁵

• Sudlow C
• Gallacher J
• Allen N
• et al.

UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.

and replicated this analysis using data from an independent cohort, the nationwide population-based Finnish Health and Social Support (HeSSup) study.

¹⁶

• Korkeila K
• Suominen S
• Ahvenainen J
• et al.

Non-response and related factors in a nation-wide health survey.

Baseline data for the UK Biobank were collected between 2006 and 2010 in 22 research assessment centres across the UK. We included participants aged 38–73 years, who were linked to national health registries, had no history of hospital-treated infections at or before baseline, and had complete data on loneliness or social isolation. The HeSSup study comprised a random sample of individuals in Finland aged 20–54 years.

¹⁶

• Korkeila K
• Suominen S
• Ahvenainen J
• et al.

Non-response and related factors in a nation-wide health survey.

We included individuals from the HeSSup study with available data on loneliness or social isolation who were linked to national health registries. The HeSSup study included repeated assessments of loneliness and social isolation (in 1998 and 2003), which allowed evaluation of reverse causality. We excluded participants with missing data on hospital-treated infections, loneliness, and social isolation from both cohorts. The outcome of interest was defined as hospital admissions with a primary diagnosis of infection, ascertained via linkage to electronic health records.

All participants provided written informed consent for the baseline assessments and for registry linkage. The UK Biobank was approved by the National Health Service National Research Ethics Service (11/NW/0382), and the HeSSup study by the ethics committee of Turku University Central Hospital and the Finnish Population Register Centre (VRK 2605/410/14).

Procedures

In the UK Biobank, loneliness was assessed by asking two questions: “Do you often feel lonely?” (no, 0; yes, 1) and “How often are you able to confide in someone close to you?” (0, almost daily–once every few months; 1, never or almost never). We defined a person as lonely only if they responded positively to both questions. In the sensitivity analysis, we used a single-item measure (“Do you often feel lonely?” Yes or no) to measure loneliness, as such single-item measures are highly correlated with the UCLA loneliness scale, the most commonly used multi-item measure of loneliness in population surveys.

¹⁷

• Hughes ME
• Waite LJ
• Hawkley LC
• Cacioppo JT

A short scale for measuring loneliness in large surveys: results from two population-based studies.

In the UK Biobank, social isolation was assessed by asking three questions:

¹⁵

• Sudlow C
• Gallacher J
• Allen N
• et al.

UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.

(1) “Including yourself, how many people live in your household? Include those who usually live in the house such as students living away from home during term time, and partners in the armed forces or in professions such as pilots” (1 point for living alone); (2) “How often do you visit friends or family or have them visit you?” (1 point for less than one friend or family visit per month); and (3) “Which of the following (leisure or social activities) do you engage in once a week or more often? You may select more than one” (1 point for not participating in any social activities at least weekly). The sum of the responses to these three questions resulted in a scale ranging from 0 to 3. We classified respondents with 2 or 3 points as socially isolated. The loneliness and social isolation measures were dichotomised with no weighting of responses.

In the HeSSup study, we constructed binary variables of loneliness and social isolation measures to make the measures and distributions as comparable as possible with those in the UK Biobank. Loneliness was assessed by asking “Do you currently feel lonely?” (Yes, very much so; yes, to some extent; no). We dichotomised responses to this question into yes versus no. The social isolation measure included four items of the longer Social Support Questionnaire, reflecting different ways of receiving support.

¹⁸

• Sarason IG
• Sarason BR
• Potter EH
• Antoni MH

Life events, social support, and illness.

The respondents could choose one or more of six alternatives (husband, wife, or partner; some other relative; close friend; close co-worker; close neighbour; or someone else close). The responses to the items were combined so that each source of support contributed one point to the final social support score (range 0–20).

¹⁸

• Sarason IG
• Sarason BR
• Potter EH
• Antoni MH

Life events, social support, and illness.

We used dichotomised scores in our analyses (0–6, socially isolated; 7–20, not isolated). Additional references and a description of validity issues are provided in the appendix (p 2).

Data regarding sex were acquired from central registry at recruitment, but in some cases were updated by the participant (categories were male and female). We selected baseline covariates based on factors used in previous studies in the field (appendix p 2), including demographic characteristics such as age, sex, and ethnicity, which can act as confounders, and other factors that can act as confounders, mediators, or both (appendix p 3). In the UK Biobank, baseline covariates (self-reported unless otherwise specified, as in the HeSSup study) were sex, age, ethnicity (White or non-White), education (low [no secondary education], intermediate [secondary education], or high [university degree]), the Townsend deprivation index (a continuous measure of neighbourhood deprivation), chronic diseases (self-reported long-standing illness without any specific diagnosis), current smoking (yes or no), physical activity (moderate and vigorous physical activity five or more times a week vs other), frequency of alcohol intake (three or four times a week or more vs once or twice a week or less), BMI (kg/m²), depressed mood in the past 2 weeks (Patient Health Questionnaire classified as low [not at all, several days] and high [more than half of my days, nearly every day]), and C-reactive protein (mg/L). Using linked electronic health records, we assessed physical conditions that, according to the US Centers for Disease Prevention and Control (2017), increase the risk of infectious diseases (a list of conditions with International Classification of Diseases 10th Revision [ICD-10] codes is provided in the appendix p 2).

In the HeSSup cohort, all covariates were self-reported and included age, sex (categories male and female), education (low [no occupational education], intermediate [vocational education], or high [university education]), current smoking (yes or no), alcohol consumption (none or moderate [1 to 21 units], or heavy [>21 units per week]), BMI (kg/m²), and depressed mood (moderate depression based on Beck Depression Inventory [score >18]). Physical activity was assessed as Metabolic Equivalent of Tasks

¹⁹

• Eisenberger NI
• Moieni M
• Inagaki TK
• Muscatell KA
• Irwin MR

In sickness and in health: the co-regulation of inflammation and social behavior.

(METs) and was dichotomised on the basis of median split (high, 3·6 or more; low,

²⁰

• Mendes MA
• da Silva I
• Ramires V
• et al.

Metabolic equivalent of task (METs) thresholds as an indicator of physical activity intensity.

The UK Biobank participants were linked to the Hospital Episode Statistics Admitted Patient Care (England), the Scottish Morbidity Records General/Acute Inpatient and Day Case Admissions (Scotland), and the Patient Episode Database (Wales) until Feb 7, 2018. HeSSup participants were linked to the Finnish National Registry for Hospitalisations until Dec 31, 2012. In both studies, we retrieved primary diagnoses of infectious diseases from inpatient hospital discharge information using ICD-10 codes.

²¹

• Sipila PN
• Heikkila N
• Lindbohm JV
• et al.

Hospital-treated infectious diseases and the risk of dementia: a large, multicohort, observational study with a replication cohort.

We classified hospital-treated infectious diseases according to 925 ICD-10 codes (appendix pp 5–9). For comparison, we examined the associations of loneliness and social isolation with other broad disease categories including cancers; diseases of the endocrine, circulatory, respiratory, digestive, musculoskeletal, genitourinary, and nervous systems; diseases of the blood, eye, ear, and skin; and mental and behavioural disorders.

Statistical analysis

On the basis of a log-rank test and assuming a 5% confidence level and a statistical power of 80%, the minimum sample size for the detection of a small relative risk of 1·1 was 15 055 for loneliness and 14 925 for social isolation. After we assessed the proportional hazards assumption (appendix pp 9–10), we used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% CIs separately for the associations of loneliness and social isolation with the first hospital-treated infection episode. Participants with a hospital-treated infection at or before baseline were excluded before the analysis. Follow-up was from study entry until the first hospital episode due to infection, death, or end of follow-up, whichever came first (no data were available on migration). These analyses applied to both the UK Biobank and HeSSup studies (HeSSup data did not include information on ethnicity, area deprivation, or C-reactive protein).

We examined the associations of loneliness and social isolation with hospital-treated infectious diseases separately in the following steps. First, the associations were tested in the UK BioBank cohort by adjusting HRs and 95% CIs for age, sex, and ethnicity. To examine whether the associations were observable in subgroups, we conducted analyses stratified by sex, age, education, C-reactive protein, long-term disease status, and depressed mood at baseline. These variables were selected because they are potential effect modifiers and were used in stratified analyses in previous UK Biobank studies on loneliness.

⁸

• Elovainio M
• Hakulinen C
• Pulkki-Raback L
• et al.

Contribution of risk factors to excess mortality in isolated and lonely individuals: an analysis of data from the UK Biobank cohort study.

^, 

²²

• Elovainio M
• Lahti J
• Pirinen M
• et al.

Association of social isolation, loneliness and genetic risk with incidence of dementia: UK Biobank cohort study.

The interaction effect was tested by adding interaction terms into each model.

Second, we performed stepwise multivariable analyses in the UK BioBank cohort to test the extent to which the associations were independent of baseline covariates, and whether the multivariable-adjusted results were replicable in the first 3 years of follow-up and from year 3 onwards. All models included loneliness or social isolation as the exposure and covariates were added as follows. Model 1 included age and sex. In addition to age and sex, other models included ethnicity (Model 2); education and the Townsend deprivation index (Model 3); smoking, alcohol consumption, physical activity, and BMI (Model 4); long-term illness (Model 5); C-reactive protein (Model 6); depressed mood (Model 7); and all the aforementioned covariates (Model 8). Given that the covariates can act as both as confounders and mediators, we interpreted the results cautiously and considered the association between loneliness or social isolation and infectious diseases independent of other factors only if the association remained significant after adjustment for the covariates. We calculated the percentage of excess risk attributable to covariates (PERM) for the associations of social isolation and loneliness with infections using the following formula:

PERM% = ([HR(age and sex) – HR(age, sex, and covariates adjusted)]/[HR(age and sex adjusted) – 1]) × 100.

Third, in sensitivity analyses of the UK BioBank cohort, we tested whether the associations were robust to the exclusion of participants with physical conditions that increase the risk of infectious diseases. To examine reverse causation in the HeSSup study, we tested whether infectious diseases at baseline were associated with loneliness or social isolation at follow-up among those who did not report these exposures at baseline. The exposure was a hospital-treated infectious disease and the outcome loneliness or social isolation. We included those with and without an infectious disease at baseline (the exposure) but excluded those who reported being lonely or isolated. Incident cases were those who had become lonely or socially isolated at follow-up. To investigate disease specificity, we examined associations between loneliness or social isolation and other disease categories. In each step, participants with missing data on covariates were excluded from the analysis.

A two-sided p value of less than 0·05 was considered to indicate statistical significance. Because this was a hypothesis-testing study with multiple sensitivity analyses rather than an exploratory study with multiple independent tests, we did not correct for multiple testing.

In the HeSSup study, the analyses were done in two steps, first adjusted for age and sex and second adjusted for age, sex, education, alcohol consumption, smoking status, physical activity, and depressive symptoms.

We did all data analyses in R (version 4.1.1) between December, 2021, and January, 2022. The code for the analyses is available in the appendix (pp 11–44).

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Association of social isolation and loneliness with risk of incident hospital-treated infections: an analysis of data from the UK Biobank and Finnish Health and Social Support studies - The Lancet
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