Study design and sample
The data used in this study came from the first wave of The Irish Longitudinal Study on Ageing (TILDA) which was conducted by Trinity College Dublin between October 2009 and February 2011. Details of the survey and its sampling procedure have been published previously [29, 30]. In brief, TILDA was a nationally representative survey of community-based adults aged 50 and above living in Ireland. The target sample included every household resident meeting this age criterion. Clustered random sampling was used to obtain a nationally representative sample. Individuals who were institutionalized and those who had doctor-diagnosed dementia were excluded. If severe cognitive impairment (judged at the interviewer’s discretion) prevented individuals from providing written informed consent to participate in the survey, they were also excluded . The data was collected by trained interviewers using computer-assisted personal interviewing (CAPI), and with the use of self-completion questionnaires (SCQs). All individuals that underwent a CAPI interview were also asked to complete the SCQ. The overall response rate was 62%, while 84% of those who participated in the survey returned the SCQ [29, 30].
In total, 8504 people aged ≥50 years (n = 8175) and their spouses or partners younger than 50 years (n = 329) comprised the survey sample. In the current study, the analysis was restricted to participants aged 50 years and above and those who completed the SCQ. These conditions were necessary as information on certain variables (e.g., loneliness, anxiety etc.) was obtained from the SCQ. Following these restrictions, the analytic sample comprised 6903 individuals. The Faculty of Health Sciences Ethics Committee of Trinity College Dublin provided ethical approval for TILDA, with written informed consent being obtained from all participants.
Loneliness (Dependent variable)
The short form of the University of California, Los Angeles (UCLA) Loneliness Scale was used to assess feelings of loneliness [32, 33]. The short form UCLA Loneliness Scale, which assesses subjective feelings of social isolation, is a commonly used measure in loneliness research. The dominant factor underlying the UCLA Loneliness scale is ‘perceived social isolation’ [34, 35]. The UCLA three-item scale is comprised of three negatively-worded questions relating to feelings of isolation, feeling left out and companionship. The three response options are coded as 1 (hardly ever), 2 (some of the time), and 3 (often). Scores are summed to create a total score that runs from 3 to 9, with higher scores indicating a greater degree of loneliness (Cronbach’s alpha = 0.81). Previous research has indicated that this scale has an acceptable degree of reliability and has both concurrent and discriminant validity . As the distribution of the loneliness variable was right-skewed, in this study we used a dichotomous loneliness variable for the regression analyses. Specifically, in accordance with a recent study, a score of 4–9 was categorized as feeling lonely while a score of 3 (i.e., replying ‘hardly ever’ to all of the questions) was classified as not feeling lonely .
Urinary incontinence (UI) (Independent variable)
Any UI was assessed by the question ‘During the last 12 months, have you lost any amount of urine beyond your control?’ with the answer options ‘yes’ or ‘no’. For those who responded affirmatively to this question, follow-up questions on the frequency of UI and limitations in activity due to UI were asked. Frequency was assessed by the question ‘Did this happen more than once during a 1 month period?’ and activity limitations were examined by the question ‘Do you ever limit your activities, for example, what you do or where you go, because of UI?’ Both of these questions had ‘yes’ or ‘no’ as answer options.
Depressive symptoms were measured with the 20-item Center for Epidemiologic Studies Depression (CES-D) scale , which assesses symptoms experienced in the preceding week. Its 20 items are scored on a scale from 0 (rarely or none of the time, less than one day in the week) to 3 (most or all of the time, five to seven days in the week). In order to avoid an overlap with the outcome (loneliness), and following the lead of an earlier study , we excluded the item on loneliness (‘I felt lonely’) that is included in the CES-D scale. Thus, scores from the remaining 19 items were summed to create a scale with values ranging from 0 to 57 where higher scores signified more depressive symptoms (Cronbach’s alpha = 0.87). Previous studies have highlighted the validity of the CES-D scale as a measure of depression in community-dwelling older adults [38, 39].
The Hospital Anxiety and Depression Scale (HADS-A)  was used to assess anxiety symptoms. This scale measures the presence of anxiety symptoms without reference to a specific time frame. The scale consists of seven items rated on a four-point scale from 0 (not at all) to 3 (very often indeed), five of which are reverse coded. The scores from the individual items were summed to create a total score that ranged from 0 to 21, with higher scores indicating more anxiety (Cronbach’s alpha = 0.65). Previous research has indicated that the HADS is a reliable measure in both younger and older persons .
Social network index
The Berkman-Syme Social Network Index (SNI) was used to assess social networks. The SNI is a validated self-report questionnaire  that assesses the degree to which a person is socially integrated. Information is elicited on marital/partnership status (married/with partner versus not), sociability (number of children, close relatives, and close friends and the frequency of contact with them), and church group or community organization membership. A composite score is calculated that ranges from 0 to 4. In this study, we used what is regarded as the standard categorization [i.e., 0–1 (most isolated), 2 (moderately isolated), 3 (moderately integrated), and 4 (most integrated)] . Further information on the psychometric properties of the SNI and evidence relating to its predictive validity has been provided elsewhere .
Chronic medical conditions
To assess chronic health conditions, participants were presented with a list of 17 medical conditions and asked, “has a doctor ever told you that you have any of the conditions on this card?” These conditions were: high blood pressure or hypertension; angina; heart attack (including myocardial or coronary thrombosis); congestive heart failure; diabetes or high blood sugar; stroke (cerebral vascular disease); ministroke or transient ischemic attack; high cholesterol; heart murmur; abnormal heart rhythm; any other heart trouble; chronic lung disease such as chronic bronchitis or emphysema; asthma; arthritis (including osteoarthritis, or rheumatism); osteoporosis; cancer or a malignant tumor (including leukemia or lymphoma but excluding minor skin cancers); cirrhosis or serious liver damage. The total number of chronic medical conditions was calculated and divided into three categories: 0 (none), 1, or ≥2.
Activities of daily living (ADL) disability
To assess ADL disability participants were asked to indicate whether they had difficulty performing six activities (dressing, walking, bathing, eating, getting in or out of bed, and using the toilet) . Participants having difficulty with one or more ADLs were categorized as having an ADL disability.
Sociodemographic characteristics included age (50–59, 60–69, 70–79, and ≥80 years), sex, education, and wealth. Education was divided into three categories: primary (some primary/not complete; primary or equivalent); secondary (intermediate/junior/group certificate or equivalent; leaving certificate or equivalent); and tertiary (diploma/certificate; primary degree; postgraduate/higher degree). As more than 50% of the income values were missing, a proxy measure (financial strain) was used to assess wealth. Participants were thus asked to respond to the statement that a ‘shortage of money stops me from doing the things I want to do’ using one of the answer options, ‘never’, ‘rarely’, ‘sometimes’, and ‘often’.
Stata version 14.1 (Stata Corp LP, College Station, Texas) was used to perform the analysis. In the first stage, descriptive statistics are presented of the study sample. The difference in sample characteristics by the presence of UI was tested by using Chi-square and Student’s t-tests for categorical and continuous variables, respectively. Logistic regression analysis was then used to firstly assess the association between any UI (independent variable) and loneliness (dependent variable) based on the question ‘During the last 12 months, have you lost any amount of urine beyond your control?’. A hierarchical analysis was conducted by including different variables sequentially in different models to assess how these variables influenced the association between UI and loneliness. Six different models were thus constructed: Model 1: unadjusted; Model 2: adjusted for age, sex, education, financial strain, number of chronic conditions, and ADL disability; Model 3: adjusted for the variables in Model 2 and the SNI; Model 4: adjusted for the variables in Model 3 and depression; Model 5: adjusted for the variables in Model 3 and anxiety; Model 6: adjusted for the variables in Model 3, depression, and anxiety. The selection of the variables used for adjustment was based on past literature.
To assess the association between UI severity and loneliness, we repeated the analytic method described above but replaced the any UI variable with a three-category UI variable which incorporates the frequency of urinary inconsistence [UI (-); UI (+) once a month or less; UI (+) more than once a month], or activity limitations due to UI [UI (-); UI (+) but no activity limitations; UI (+) with activity limitations]. This analysis used ‘no UI’ as the reference category. Finally, we also performed this analysis while restricting it to those with UI to assess whether the frequency of UI or activity limitations due to UI confers an increased risk for loneliness among those with UI. All variables included in the models were categorical variables apart from depression and anxiety which were continuous variables. The dataset also included sampling weights that were created based on the age, sex and educational attainment values in the Quarterly National Household Survey 2010. In order to obtain nationally representative estimates, the sample weighting and the complex study design, including within household clustering, was taken into account in all analyses. Results are expressed as odds ratios (OR) and 95% confidence intervals (95% CIs). A p-value <0.05 was considered to be statistically significant.