Results of the regression analysis

4.4.3.1 Adjustment for confounding

Based on the DAG presented in Figure 4-2, the following adjustments were applied when conducting each of the linear regression models: age was adjusted for gender;

education was adjusted for age and gender; employment was adjusted for age, gender and education; household structure was adjusted for gender, age, education and employment; and mental and physical health were both adjusted for age, gender, education, household structure and employment.

Figure 4-2 DAG representing the association between sleep, sociodemographic features and health indicators. The variables are represented by rectangles squares, and the direction of potential causal influence between variables are represented by unidirectional arrows.

4.4.3.2 The association between sociodemographic, health features and sleep

In comparison to the six sleep patterns, the seven individual sleep characteristics displayed far less variability in their associations with sociodemographic and health features. (Table 4-11, Table 4-12, Table 4-13 and Table 4-14)

Female participants were more likely to display sleep latency, sleep disturbance and poorer sleep quality, and to report using sleep-related medication than men. In addition, female participants were far less likely to be associated with cluster 3 ‘good long sleeper’ compared to men and this might in part reflect the effect of hormonal differences and hormonal variability (i.e. during menstruation, menopause and pregnancy as well as postpartum). In addition, older participants were less likely to report sleep latency and daytime sleepiness than younger participants. Participants who were older and female were significantly more likely to have particular sleep patterns, such that: compared to those aged less than 20 years, people aged between 20–39 years tended to be found within cluster 1 (“short bad sleeper”), and those between 40–69 years tended to be found in either cluster 1 or 6 (“snoring good sleeper”); while participants who were older than 70 years had a six times higher odds of being snorers with a good perceived sleep quality.

Participants with lower educational attainment were more likely to exhibit all of the unfavourable sleep examined (with the exception of disturbance). Participants who were unemployed also had a higher risk of experiencing unfavourable sleep events,

while participants with poor health indicators (i.e. unemployment due to sickness, a lower score in the mental and physical component of the SF12V and poor reported subjective health) were more likely to report unfavourable sleep events across all seven individual sleep characteristics. These trends in the association between individual sleep characteristics and health was noticeably absent when the association between health and latent sleep clusters was examined. Instead, health displayed a range of different relationships (positive and negative, strong and weak) with the six sleep pattern clusters/classes.

Full-time trainees/students had the lowest risk of being associated with ostensibly unfavourable sleep patterns compared to those who were employed or on leave. This might in part be related to an age effect. Meanwhile, educational attainment was associated with a range of different sleep pattern clusters: participants with postgraduate qualifications had twice the odds of being in cluster 5 (“struggle-to-sleep-er”) compared to those with a degree. Having no qualifications increased the risk of being classified in cluster 6 (“snoring good sleeper”) as well as in cluster 5.

Household structure, in terms of presence of children or living as couples, had a range of different associations with different sleep pattern clusters. For example, in comparison to single participants without children, being single with children decreased the risk of being associated with cluster 6 (“snorers, good sleepers”), perhaps because snorers are usually not aware of their snoring unless informed by a bed partner (i.e. acting as a witness). Being a couple without children significantly decreased the odds of being in cluster 5 (“struggle-to-sleep-er”) – as such it was associated with a lower odds of experiencing sleep latency, or disturbance or protracted sleep duration. Perhaps caring for young children might cause some degree of sleep disturbance as parents often awake to settle, change or feed their children if these wake in the night. Living with others, either as couples or not, and with/without children, decreased the odds of being associated with sleep patterns that had unfavourable events (as compared with living alone). However, this finding should not be mistaken with the likely effects of overcrowding or cohabitation with extended families on the availability of sleeping space/accommodation as the latter had a higher odds of being associated with bad sleep quality and shorter duration sleep (Fowler et al., 2014).

4.4.3.2.1 Sociodemographic features and sleep

In regards to the seven sleep characteristics, when compared to participants younger than 20 years of age, those who were older than 40 years of age had an odds of short sleep duration that was three times higher (OR= 3.31, CI = 3.03, 3.61), while the odds of disturbance (OR=2.30, CI=2.14, 2.47) and snoring (OR=2.75, CI=2.52, 3.00) were

both over twice as high. In participants older than 60 years of age, the odds of using medication to help with sleep was two times higher (OR=2.52, CI=2.21, 2.88). Female participants displayed a higher tendency towards unfavourable sleep events than males, excluding snoring or coughing (OR=0.66, CI=0.64, 0.69) and short sleep duration (OR=0.95, CI=0.913, 0.98) where females had a substantially and modestly lower odds thereof, respectively. In general, participants with lower education attainment had a modestly higher odds of unfavourable sleep events, except the odds of disturbance, which was marginally lower amongst participants with lower educational attainment (OR=0.95, CI=0.91, 0.99). Compared to employed individuals, participants who were unemployed or undergoing training displayed a higher odds of unfavourable sleep events. On the other hand, the odds of reporting poor sleep quality (OR=0.90, CI=0.82, 0.99), short sleep (OR=0.72, CI=0.681, 0.76) and snoring or coughing (OR=0.76, CI=0.70, 0.82) were all lower amongst participants who were retired. The association between each of the categories of household structure and the risk of unfavourable sleep events varied, but in general it appeared that: a those participants living alone without any children had a higher odds of unfavourable sleep events, except snoring or coughing. Having children increased these odds to OR=1.18 (CI=1.08, 1.29), having a partner increased the odds to OR=1.36 (CI=1.29, 1.45) and having a partner plus children increased the odds to OR= 1.20 (CI=1.13, 1.28).

In regards to sleep clusters, female participants were more likely to be in clusters 1, 5 and 6 than males. The highest odds for females was to be in cluster 5 (OR =1.61, CI=1.47, 1.31). Being older than 20 years of age increased the odds of being included in clusters 1 and 5. The odds of being in cluster 1 was three times higher in the age group 40-59 years (OR=3.07, CI=2.84, 3.32), two times higher in the age group 20 to 39 years (OR=2.14, CI=1.98, 2.31) and more than two times higher for the age group older than 60 (OR=2.67, CI=2.46, 2.90).Lower education was associated with a higher odds of being included in clusters 4, 5 and 6 with the highest odds being membership of cluster 5 (OR=1.75, CI=1.60, 1.91).

The association between employment and cluster membership varied. Participants who were enrolled in education or training programs had a higher odds of being in cluster 2 (OR=1.35, CI=1.23, 1.48) and cluster 4 (OR=1.26, CI=1.11, 1.42).

Unemployed participants had a higher odds of being in cluster 5 (OR=1.86, CI=1.64, 2.11) and cluster 6 (OR=1.77, CI=1.52, 2.07). Similarly, more retired participants tended to be in cluster 5 (OR=1.59, CI=1.30, 1.94) and cluster 6 (OR=1.53, CI=1.19, 1.97).

Compared to single participants without children, having children increased the odds of being in cluster 1 by OR= 1.26 (1.16, 1.37). In addition, having a partner increased

the odds of being in cluster 1 by OR=1.13 (CI=1.07, 1.19) and being in cluster 3 by OR=1.24 (CI=1.12, 1.38). In a very similar fashion, having a partner and children increased the odds of being in cluster 1 by OR=1.31 (CI=1.24, 1.39).

4.4.3.2.2 Health indicators and sleep

Overall, participants with low scores for the mental and physical components of the SF12v (i.e. total score < 32), and participants who reported a bad to poor level of perceived general health, showed an elevated risk of developing unfavourable sleep events. A low score on the SF12v physical component increased the odds of reporting poor sleep quality by more than six times (OR=6.19, CI=5.67, 6.76) and by more than two times for using sleep-related medication (OR=2.64, CI=2.41, 2.89) and reporting next-day sleepiness (OR=2.40, CI=2.14, 2.70). A low score for the mental components of the SF12v was also associated with an increase of three times the odds of reporting poor sleep quality (OR=3.03, CI=2.77, 3.83) and daytime sleepiness (OR=3.07, CI=2.77, 3.41), and of almost twofold for each the remaining sleep characteristics.

Likewise, poor general health was associated with an increased odds of reporting poor quality sleep by over three times (OR=3.61, CI=3.40, 3.83) and an elevated odds of daytime sleepiness by over two times (OR=2.39, CI=2.21, 2.88).

Participants who reported current unemployment secondary to sickness or disability also reported an increased odds of developing unfavourable sleep. For example, the odds of reporting poor quality sleep was 4 times higher (OR=4.56, CI=4.11, 5.07), and the odds of using sleep-related medication was more than three and a half times higher (OR=3.75, CI=3.36, 4.20). Elsewhere, the odds of other unfavourable sleep characteristics amongst participants who were unemployed due to sickness or disability was almost doubled (Table 4-12).

Unlike the associations between health indicators and individual sleep characteristics items, which tended to reflect the ill-effect of illness on sleep (and/or vice versa), the odds of being included in a sleep cluster varied substantially according to the sociodemographic and health characteristics examined (Table 4-13 and Table 4-14).

Participants with poor mental and physical health scores were only more likely to be included in clusters 5 and 6; while reporting a poor subjective general health increased the odds of being in cluster 5 by over three times (OR=3.24, CI= 3.24, 3.94) and was also associated with a more modestly elevated odds of being in cluster 6 (OR=1.54, CI=1.35, 1.75). Participants with a low physical health score for the SF12v had three times the odds of being in cluster 5 (OR=3.11, CI=2.70, 3.53) as well as a higher odds of being in cluster 6 (OR=1.75, CI=1.47, 2.09). Participants with a low score in the mental health components of the SF12v had over four times the odds of being in cluster 5 (OR=4.75, CI=4.24, 5.33).

4.4.3.2.3 Choosing a reference point for the latent sleep variable

‘Long good sleeper’ was chosen as the referent for the latent sleep variable (with six separate categories) for each of the following reasons:

1- The pattern of the ‘long good sleeper’ category was characterised by the absence of any unfavourable sleep events; hence it was easier and theoretically more plausible to use this category as the referent for

‘good’/’favourable’ sleep.

2- Participants who had a lower probability of displaying a ‘long good sleeper’

pattern were those who were mentioned in the literature (Chapter 1, Section 1.4, page 29) as groups at high risk of sleep complications, these groups were:

I. older participants

II. participants who were currently not working because they were unemployed, sick /disabled or retired

III. participants with children or couples living with children in the same household

IV. participants with poor physical health V. participants with poor psychological health

3- The pattern of the relationships between participants classified as ‘long good sleepers’ and available health indicators was similar to the inverse of the pattern observed between unfavourable individual sleep characteristics and health (i.e.

poor health was associated with an increased the risk of unfavourable individual sleep characteristics).