Abstract

Background

Many theories have been proposed to explain the high levels of ‘excess’ mortality (i.e. higher mortality over and above that explained by differences in socio-economic circumstances) shown in Scotland—and, especially, in its largest city, Glasgow—compared with elsewhere in the UK. One such proposal relates to differences in optimism, given previously reported evidence of the health benefits of an optimistic outlook.

Methods

A representative survey of Glasgow, Liverpool and Manchester was undertaken in 2011. Optimism was measured by the Life Orientation Test (Revised) (LOT-R), and compared between the cities by means of multiple linear regression models, adjusting for any differences in sample characteristics.

Results

Unadjusted analyses showed LOT-R scores to be similar in Glasgow and Liverpool (mean score (SD): 14.7 (4.0) for both), but lower in Manchester (13.9 (3.8)). This was consistent in analyses by age, gender and social class. Multiple regression confirmed the city results: compared with Glasgow, optimism was either similar (Liverpool: adjusted difference in mean score: −0.16 (95% CI −0.45 to 0.13)) or lower (Manchester: −0.85 (−1.14 to −0.56)).

Conclusions

The reasons for high levels of Scottish ‘excess’ mortality remain unclear. However, differences in psychological outlook such as optimism appear to be an unlikely explanation.

Introduction

Excess mortality in Scotland and Glasgow

Despite the important, and well-established, link between poverty and poor health, previous research has shown that there is an ‘excess’ level of mortality in Scotland compared with other parts of the UK: that is, higher mortality over and above that explained by higher levels of poverty and deprivation.1–9 This excess has been shown to be ubiquitous in Scotland, but greatest in and around Glasgow and the West Central Scotland conurbation. For example, one study showed that after adjustment for area deprivation, premature mortality (under 65 years) in Glasgow was 30% higher than Liverpool and Manchester—cities with similar histories of industrialization, deindustrialization and associated deprivation. Deaths at all ages were almost 15% higher.4,5 This city excess was shown across the whole population (including among those living in non-deprived neighbourhoods), and is increasing over time. Similar levels of excess for Scotland have been shown in analyses based on individual socio-economic status (rather than area deprivation)2,3 and when controlling for a range of biological and behavioural risk factors.8,10

Many theories have been proposed to explain the excess seen in Glasgow compared with Liverpool and Manchester (and in Scotland compared with the rest of the UK).11,12 These have included topics as diverse as political impacts, the effects of climate, genetics and more negative childhood environments. One such suggestion is that there are differences in the psychological outlook of the population, specifically that people in Glasgow may be associated with lower levels of optimism, which would influence health behaviours and choices and, therefore, ultimately health outcomes. This is based on evidence from a number of studies which have highlighted the health benefits of an optimistic outlook.13–16 The aim of this study was to establish whether there was any evidence of lower optimism among the population of Glasgow in comparison with those in Liverpool and Manchester which might support this suggestion.

Methods

Population survey

A representative survey of the adult population of Glasgow, Liverpool and Manchester was carried out in 2011. Full details of the survey design and implementation are available elsewhere.17,18 In brief, a stratified clustered random probability sample design was employed, from which face-to-face ‘in home’ household interviews were undertaken for a representative sample of more than 3700 adults (over 1200 in each city). The response rate was 55% (comparable with that obtained for many contemporary national population surveys), ranging from 53% in Manchester to 58% in Glasgow (the rate for Liverpool was 55%), and from 53% in the least deprived areas of the three cities to 58% in the most deprived areas. Data were weighted to ensure they were as representative of the households and cities as possible: the weighting adjusted for differential response by deprivation decile and ‘up-weighted’ multiple households, large households, younger ages and men to adjust for the lower probability of sampling in the former two and the lower response rates in the latter two. Representativeness was further assessed by means of comparisons with a range of other survey and administrative data.17

Optimism was measured using the Life Orientation Test (Revised) (LOT-R),19 deemed to be a ‘highly reliable and valid measure of generalized optimism’ and ‘the best measure of optimism’.20 The LOT-R scale is made up of 10 statements against which respondents' level of agreement (from ‘strongly disagree’ to ‘strongly agree’) is recorded. Four of the statements are ‘dummy’ statements (or ‘fillers’) and are excluded from the overall score. Thus, the minimum score that can be calculated is 0 (representing extreme pessimism) and the maximum is 24 (representing extreme optimism). In calculating the total score for each question, negatively worded statements (e.g. ‘if something can go wrong for me it will’) are reverse-coded (i.e. ‘strongly agree’ is coded as 0 rather than 4) to ensure higher scores represent higher levels of optimism.

The survey was approved by the University of Glasgow Medical Faculty Ethics Committee (project reference no. zFM06910).

Statistical analyses

LOT-R scores between the cities were compared, while adjusting for the different characteristics of the samples, by means of multiple linear regression models. The dependent variable was the LOT-R score, and the independent variables were the city of residence (Glasgow, Liverpool or Manchester) plus age (as a categorical variable to allow for non-linearity), gender, ethnicity, social class (measured by ‘social grade’, the socio-economic classification used by the Market Research and Marketing Industries and in analyses of UK Census data), area deprivation (a comparable measure of ‘income deprivation’ created for previous research in Glasgow, Liverpool and Manchester4,5), educational attainment, employment status, marital status and length of residence in the city. (The latter was a crude estimate derived from other survey variables. No specific question on length of residence in the city was included in the survey. Thus, a crude measure of likely length of residence was derived from other available information: respondents were asked how long they had lived in their neighbourhood (with options ranging from ‘under 6 months’ to ‘over 5 years’), and those who lived through the 1980s (i.e. were aged at least 36 at the time of the survey) were additionally asked in which city they were resident for most of that decade. From those two questions, respondents were categorized as being ‘Possibly long-term resident’ (based on either being resident in their neighbourhood for 5 years or more, or having been in the same city in the 1980s) or ‘length of residence in city unknown’.) From the data collected in the survey, these were deemed the most relevant sample characteristics to include in the models in terms of their potential influence on both health status, and possible predictors of health status such as optimism. These variables are listed and defined in Table 1. Note that social grades ‘A’ and ‘B’ were combined into one single category because of the very small number of respondents in each city classed as grade ‘A’.

Table 1

Independent variables used in regression modelling analyses

Variable Categories 
City of residence Glasgowa 
Liverpool 
Manchester 
Gender Malea 
Female 
Age 16–29a 
30–44 
45–64 
65 and older 
Social grade A (higher managerial, administrative or professional) and B (intermediate managerial, administrative or professional)a 
C1 (supervisory, clerical and junior managerial, administrative or professional) 
C2 (skilled manual workers) 
D (semi and unskilled manual workers) 
E (on state benefits/unemployed/lowest grade workers) 
Employment status Employed (PT/FT)a 
Unemployed 
Ill/disabled 
Retired 
Looking after home/family 
In education/training (PT/FT) 
Educational attainment No qualificationsa 
Some qualifications, but not degree level 
1st degree and above (includes NVQ/SVQ Level 5 or equivalent) 
Deprivation quintile 1 (Most deprived)a 
5 (Least deprived) 
Ethnicity Not a member of ethnic minority groupa 
Member of ethnic minority group 
Marital status Never marrieda 
Married/civil partnership 
Separated/divorced 
Widowed/surviving partner 
Length of residence (approximate) Time in city not knowna 
Possibly long-term resident 
Variable Categories 
City of residence Glasgowa 
Liverpool 
Manchester 
Gender Malea 
Female 
Age 16–29a 
30–44 
45–64 
65 and older 
Social grade A (higher managerial, administrative or professional) and B (intermediate managerial, administrative or professional)a 
C1 (supervisory, clerical and junior managerial, administrative or professional) 
C2 (skilled manual workers) 
D (semi and unskilled manual workers) 
E (on state benefits/unemployed/lowest grade workers) 
Employment status Employed (PT/FT)a 
Unemployed 
Ill/disabled 
Retired 
Looking after home/family 
In education/training (PT/FT) 
Educational attainment No qualificationsa 
Some qualifications, but not degree level 
1st degree and above (includes NVQ/SVQ Level 5 or equivalent) 
Deprivation quintile 1 (Most deprived)a 
5 (Least deprived) 
Ethnicity Not a member of ethnic minority groupa 
Member of ethnic minority group 
Marital status Never marrieda 
Married/civil partnership 
Separated/divorced 
Widowed/surviving partner 
Length of residence (approximate) Time in city not knowna 
Possibly long-term resident 

aReference category.

Models (run using SPSS statistical software) were built incrementally, but only significant (P < 0.05) variables were included in the final models.

Models were run using both weighted and unweighted data. The results of the former are reported here (there were very little differences between the coefficients obtained for the overall city differences—the main focus of the analyses—in the two sets of models). The accuracy and ‘robustness’ of the models (including the need to ensure that key assumptions were met) were checked by means of a range of tests and analyses such as: examination of the value of R2 and adjusted R2 statistics, and the value and significance of the F ratio statistic in the analysis of variance, to check the fit of the models; examination of histograms and normal probability plots of residuals to check the assumption of normally distributed errors; as well as checks for co-linearity among independent variables, homoscedasticity, the independent errors assumption (for unweighted models only), and for any cases exerting undue influence in the models.

Two-way interactions between the independent variables (excluding city) were tested for in development of the models: although some were identified (significant at P < 0.05) (e.g. age–gender, age–social class), they did not alter the coefficients of the overall city differences (the main focus of all the analyses), nor did they increase the amount of variation explained in the models by any great extent, and so are not reported here.

Missing data were negligible, thus no imputation was required for the modelling analyses: Table 2 lists, by city, the weighted counts and percentages for each variable category.

Table 2

Weighted counts and percentages for categories of variables by city

Variable Categories Glasgow
 
Liverpool
 
Manchester
 
n % n % n % 
City of residence Total 1288 100.0 1193 100.0 1216 100.0 
Missing 0.0 0.0 0.0 
Gender Male 616 47.8 561 47.0 621 51.1 
Female 672 52.2 632 53.0 594 48.9 
Missing 0.0 0.0 0.0 
Age 16–29 366 28.4 399 33.4 490 40.3 
30–44 349 27.1 244 20.5 315 25.9 
45–64 362 28.1 340 28.5 257 21.1 
65 and older 210 16.3 211 17.7 149 12.2 
Missing 0.1 0.0 0.4 
Social Grade A (higher managerial, administrative or professional) and B (intermediate managerial, administrative or professional) 144 11.2 137 11.5 180 14.8 
C1 (supervisory, clerical and junior managerial, administrative or professional) 355 27.5 372 31.2 297 24.4 
C2 (skilled manual workers) 268 20.8 239 20.0 208 17.1 
D (semi and unskilled manual workers) 258 20.0 257 21.6 162 13.3 
E (on state benefits/unemployed/lowest grade workers) 211 16.4 133 11.2 293 24.1 
Missing 52 4.0 54 4.5 76 6.3 
Employment status Employed (PT/FT) 559 43.4 453 38.0 416 34.2 
Unemployed 154 12.0 107 9.0 181 14.9 
Ill/disabled 91 7.1 86 7.2 67 5.5 
Retired 251 19.5 264 22.1 183 15.1 
Looking after home/family 103 8.0 113 9.5 134 11.0 
In education/training (PT/FT) 129 10.0 170 14.2 211 17.3 
Missing 0.0 0.0 24 2.0 
Educational attainment No qualifications 408 31.7 313 26.2 426 35.1 
Some qualifications, but not degree level 721 56.0 679 56.9 619 50.9 
1st degree and above (includes NVQ/SVQ Level 5 or equivalent) 158 12.3 202 16.9 170 14.0 
Missing 0.0 0.0 0.0 
Deprivation quintile 1 (Most deprived) 230 17.9 213 17.9 224 18.4 
252 19.6 240 20.1 224 18.4 
262 20.3 217 18.2 243 20.0 
258 20.0 271 22.7 272 22.4 
5 (Least deprived) 285 22.2 251 21.0 252 20.7 
Missing 0.0 0.0 0.0 
Ethnicity Not a member of ethnic minority group 1183 91.9 1138 95.4 865 71.1 
Member of ethnic minority group 104 8.1 55 4.6 351 28.9 
Missing 0.0 0.0 0.0 
Marital status Never married 555 43.1 529 44 587 48.3 
Married/civil partnership 495 38.4 471 39 451 37.1 
Separated/divorced 128 10.0 111 99 8.2 
Widowed/surviving partner 110 8.5 83 78 6.4 
Missing 0.0 0.0 0.0 
Length of residence (approximate) Time in city not known 528 41.0 435 36 638 52.5 
Possibly long-term resident 760 59.0 758 64 578 47.5 
Missing 0.0 0.0 0.0 
Variable Categories Glasgow
 
Liverpool
 
Manchester
 
n % n % n % 
City of residence Total 1288 100.0 1193 100.0 1216 100.0 
Missing 0.0 0.0 0.0 
Gender Male 616 47.8 561 47.0 621 51.1 
Female 672 52.2 632 53.0 594 48.9 
Missing 0.0 0.0 0.0 
Age 16–29 366 28.4 399 33.4 490 40.3 
30–44 349 27.1 244 20.5 315 25.9 
45–64 362 28.1 340 28.5 257 21.1 
65 and older 210 16.3 211 17.7 149 12.2 
Missing 0.1 0.0 0.4 
Social Grade A (higher managerial, administrative or professional) and B (intermediate managerial, administrative or professional) 144 11.2 137 11.5 180 14.8 
C1 (supervisory, clerical and junior managerial, administrative or professional) 355 27.5 372 31.2 297 24.4 
C2 (skilled manual workers) 268 20.8 239 20.0 208 17.1 
D (semi and unskilled manual workers) 258 20.0 257 21.6 162 13.3 
E (on state benefits/unemployed/lowest grade workers) 211 16.4 133 11.2 293 24.1 
Missing 52 4.0 54 4.5 76 6.3 
Employment status Employed (PT/FT) 559 43.4 453 38.0 416 34.2 
Unemployed 154 12.0 107 9.0 181 14.9 
Ill/disabled 91 7.1 86 7.2 67 5.5 
Retired 251 19.5 264 22.1 183 15.1 
Looking after home/family 103 8.0 113 9.5 134 11.0 
In education/training (PT/FT) 129 10.0 170 14.2 211 17.3 
Missing 0.0 0.0 24 2.0 
Educational attainment No qualifications 408 31.7 313 26.2 426 35.1 
Some qualifications, but not degree level 721 56.0 679 56.9 619 50.9 
1st degree and above (includes NVQ/SVQ Level 5 or equivalent) 158 12.3 202 16.9 170 14.0 
Missing 0.0 0.0 0.0 
Deprivation quintile 1 (Most deprived) 230 17.9 213 17.9 224 18.4 
252 19.6 240 20.1 224 18.4 
262 20.3 217 18.2 243 20.0 
258 20.0 271 22.7 272 22.4 
5 (Least deprived) 285 22.2 251 21.0 252 20.7 
Missing 0.0 0.0 0.0 
Ethnicity Not a member of ethnic minority group 1183 91.9 1138 95.4 865 71.1 
Member of ethnic minority group 104 8.1 55 4.6 351 28.9 
Missing 0.0 0.0 0.0 
Marital status Never married 555 43.1 529 44 587 48.3 
Married/civil partnership 495 38.4 471 39 451 37.1 
Separated/divorced 128 10.0 111 99 8.2 
Widowed/surviving partner 110 8.5 83 78 6.4 
Missing 0.0 0.0 0.0 
Length of residence (approximate) Time in city not known 528 41.0 435 36 638 52.5 
Possibly long-term resident 760 59.0 758 64 578 47.5 
Missing 0.0 0.0 0.0 

As a number of commentators argue for the need for multilevel modelling to explore and distinguish between individual and area influences on health,21–23 the final model was also run as a multilevel linear regression model using MLwiN software (version 2.26). There were two levels: the individual (i.e. the survey respondents) and the neighbourhood (the survey sampling points with an average population size of ∼300 people17).

Results

Unadjusted analyses showed that LOT-R scores were similar in Glasgow and Liverpool (mean score (standard deviation) of 14.7 (4.0) for both), but lower in Manchester (13.9 (3.8)), a consistent pattern across all strata of gender, age (for which a u-shape distribution was evident, with higher optimism among younger and older groups compared with the middle aged), area deprivation (data not shown) and social class (for which the predicted gradient (optimism increasing with social status) was evident in all three cities—see Fig. 1).

Fig. 1

LOT-R mean score (possible score range: 0–24), by social grade.

Fig. 1

LOT-R mean score (possible score range: 0–24), by social grade.

The results of the multiple regression analyses were similar to the unadjusted differences between the cities. Table 3 shows that after adjustment for all differences in the characteristics of the samples, the Manchester sample was associated with a score 0.85 lower than the fully adjusted mean score for the Glasgow sample (regression coefficient: −0.85 (95% CI: −1.14 to −0.56); the mean score for the Liverpool sample was not markedly different to that of Glasgow's sample (fully adjusted difference in mean score: −0.16 (95% CI −0.45 to 0.13)). The modelling analyses also showed expected associations between levels of optimism and some of the independent variables included in the models: for example, higher optimism among those living in less deprived areas (compared with those in the most deprived areas) and among those with higher educational qualifications (compared with those with none), and lower optimism among those of low social grade compared with those of highest, and those not working (through unemployment, being sick or looking after home and family) compared with those who were working.

Table 3

Multivariate linear regression analysis of the factors associated with LOT-R score

Variable/category n (weighted) Adjusted meana Δμb (95% CI) Significancec 
City 
 Glasgowd 1288 14.29   
 Liverpool 1193 14.13 −0.16 (−0.45 to 0.13)  
 Manchester 1216 13.44 −0.85 (−1.14 to −0.56) *** 
Socio-economic group 
 A (higher managerial/admin/prof) and B (intermediate managerial/admin/prof)d 461 14.29   
 C1 (Supervisory, clerical, junior managerial/admin/prof) 1024 14.16 −0.13 (−0.51 to 0.25)  
 C2 (Skilled manual) 716 14.31 0.02 (−0.39 to 0.43)  
 D (Semi-skilled/unskilled manual) 677 13.97 −0.32 (−0.75 to 0.11)  
 E (On state benefit/unemployed/lowest grade workers) 637 13.46 −0.83 (−1.33 to −0.32) ** 
Deprivation quintile 
 1 (Most deprived)d 668 14.29   
 2 716 14.74 0.45 (0.07 to 0.84) 
 3 723 15.37 1.08 (0.69 to 1.47) *** 
 4 802 14.71 0.42 (0.03 to 0.81) 
 5 (Least deprived) 788 15.36 1.07 (0.67 to 1.47) *** 
Educational attainment 
 No qualificationsd 1148 14.29   
 Some qualifications, but not degree level 2019 14.95 0.66 (0.35 to 0.97) *** 
 1st degree and above (includes NVQ/SVQ Level 5 or equivalent) 531 16.11 1.82 (1.37 to 2.27) *** 
Employment status 
 Employed (PT & FT)d 1428 14.29   
 Unemployed 442 13.05 −1.24 (−1.71 to −0.78) *** 
 Ill/disabled 245 11.76 −2.53 (−3.08 to −1.97) *** 
 Retired 698 14.23 −0.06 (−0.59 to 0.47)  
 Looking after home/family 351 13.62 −0.67 (−1.14 to −0.20) ** 
 In education/training (PT/FT) 509 14.64 0.35 (−0.09 to 0.80)  
Marital status 
 Never marriedd 1671 14.29   
 Married/civil partnership 1416 14.81 0.52 (0.19 to 0.85) ** 
 Separated/divorced 339 14.49 0.21 (−0.27 to 0.68)  
 Widowed/surviving partner 271 14.90 0.61 (0.01 to 1.21) 
Age group 
 16–29d 1255 14.29   
 30–44 908 13.62 −0.67 (−1.03 to −0.3) *** 
 45–64 958 13.46 −0.83 (−1.24 to −0.42) *** 
 65+ 569 13.94 −0.35 (−0.99 to 0.30)  
Variable/category n (weighted) Adjusted meana Δμb (95% CI) Significancec 
City 
 Glasgowd 1288 14.29   
 Liverpool 1193 14.13 −0.16 (−0.45 to 0.13)  
 Manchester 1216 13.44 −0.85 (−1.14 to −0.56) *** 
Socio-economic group 
 A (higher managerial/admin/prof) and B (intermediate managerial/admin/prof)d 461 14.29   
 C1 (Supervisory, clerical, junior managerial/admin/prof) 1024 14.16 −0.13 (−0.51 to 0.25)  
 C2 (Skilled manual) 716 14.31 0.02 (−0.39 to 0.43)  
 D (Semi-skilled/unskilled manual) 677 13.97 −0.32 (−0.75 to 0.11)  
 E (On state benefit/unemployed/lowest grade workers) 637 13.46 −0.83 (−1.33 to −0.32) ** 
Deprivation quintile 
 1 (Most deprived)d 668 14.29   
 2 716 14.74 0.45 (0.07 to 0.84) 
 3 723 15.37 1.08 (0.69 to 1.47) *** 
 4 802 14.71 0.42 (0.03 to 0.81) 
 5 (Least deprived) 788 15.36 1.07 (0.67 to 1.47) *** 
Educational attainment 
 No qualificationsd 1148 14.29   
 Some qualifications, but not degree level 2019 14.95 0.66 (0.35 to 0.97) *** 
 1st degree and above (includes NVQ/SVQ Level 5 or equivalent) 531 16.11 1.82 (1.37 to 2.27) *** 
Employment status 
 Employed (PT & FT)d 1428 14.29   
 Unemployed 442 13.05 −1.24 (−1.71 to −0.78) *** 
 Ill/disabled 245 11.76 −2.53 (−3.08 to −1.97) *** 
 Retired 698 14.23 −0.06 (−0.59 to 0.47)  
 Looking after home/family 351 13.62 −0.67 (−1.14 to −0.20) ** 
 In education/training (PT/FT) 509 14.64 0.35 (−0.09 to 0.80)  
Marital status 
 Never marriedd 1671 14.29   
 Married/civil partnership 1416 14.81 0.52 (0.19 to 0.85) ** 
 Separated/divorced 339 14.49 0.21 (−0.27 to 0.68)  
 Widowed/surviving partner 271 14.90 0.61 (0.01 to 1.21) 
Age group 
 16–29d 1255 14.29   
 30–44 908 13.62 −0.67 (−1.03 to −0.3) *** 
 45–64 958 13.46 −0.83 (−1.24 to −0.42) *** 
 65+ 569 13.94 −0.35 (−0.99 to 0.30)  

R2 = 0.15; adjusted R2 = 0.14.

Total sample size: 3701 (1288 in Glasgow, 1202 in Liverpool and 1211 in Manchester).

Missing data were negligible; thus no imputation required.

aAdjusted mean.

bDifference in mean compared with reference category after adjustment for other factors in the model.

cSignificance level: *P < 0.05; **P < 0.01; ***P < 0.001.

dReference category of variable.

There was no real difference between the results of the above model and similar analyses based on multilevel modelling methodology. This was true generally (e.g. the amount of variation explained by the model), and in particular in terms of the regression coefficients for the cities (the main focus of the analyses) and associated P values: in the multilevel modelling analysis, the regression coefficients for Liverpool and Manchester were, respectively, −0.21 (95% CI −0.67 to 0.15) and −0.80 (−1.27 to −0.42).

Discussion

Main finding of this study

Based on these analyses, there is no evidence that Glasgow's population has lower levels of optimism compared with those living in the two English cities. The mean LOT-R score among the Glasgow sample was very similar to that of the Liverpool sample, and higher than that of Manchester. Although based on cross-sectional survey data which do not allow any measure of impact on subsequent levels mortality, the results nonetheless suggest that it is unlikely that differences in optimism play a part in explaining the excess mortality recorded in the Glasgow compared with the two English cities.

What is already known on this topic

Excess mortality has been shown for Scotland (and particular parts of Scotland) compared with England & Wales in a large number of studies. This includes evidence from surveys with similar response rates to those obtained in the present study10 (demonstrating that populations at risk of higher rates of mortality have been shown to be included within, not excluded from, these types of surveys).

A number of studies have highlighted the health benefits of an optimistic outlook13–16 and, more generally, of ‘positive psychological wellbeing’.24,25 For example, a 2012 review suggested that such a positive psychological outlook ‘protects consistently against cardiovascular disease (CVD), independently of traditional risk factors … [being] positively associated with restorative health behaviours … and inversely associated with deteriorative health behaviours’.26 In the same review, optimism in particular was highlighted as a factor in reducing risk of CVD, and a separate ‘meta-analytic’ review in 2009 of optimism and physical health (including studies of mortality, CVD, cancer outcomes and immune function) concluded that ‘optimism is a significant predictor of positive physical health outcomes’.27

In terms of the specific measure used in the analyses, there are different ways of measuring optimism, and different survey scales have been developed. However, the most commonly used27 is probably the LOT, or, as used here, its shorter, revised version, the LOT-R.19 Both have been independently assessed as good measures of optimism, and the shorter, revised version especially so.19,20,28,29 The same meta-analytic review cited above calculated effect sizes for 84 studies of optimism and health, the vast majority of which measured optimism by means of the LOT (36) or the LOT-R (30). The effect size for those studies was shown to be in the ‘small to moderate range’.27 Although there have been criticisms of the LOT-R scale, for example in relation to it being a general, ‘context-free’, measure (whereas context-specific measures may be more appropriate in some settings),20,30 and in terms of whether it captures just one dimension of psychological outlook (optimism alone) or two dimensions (optimism and its opposite, pessimism),31 its advantages have generally been perceived to outweigh its disadvantages (and in relation to the latter criticism, studies in 2006 and 2012 concluded that the LOT-R accurately captures both dimensions, optimism and pessimism29,32). To the authors' knowledge, no directly relevant (e.g. population-level data for Scotland/England, or for the three cities) LOT-R data are available for comparison with the results presented in this study.

What this study adds

This is the first time LOT-R has been used to assess levels of optimism across the whole populations of these three post-industrial UK cities. The analyses were based on a survey the response rate for which was far better than that achieved in many other local,33,34 regional35–37 and even national38,39 surveys, and—as discussed elsewhere17—this relatively high rate was obtained across all neighbourhood types (deprived and non-deprived) in all three cities. The data have been shown to be broadly representative of the cities' populations, while all the analyses that were undertaken entailed a multiple regression modelling component, ensuring that any reported differences between the cities were independent of the characteristics of the survey samples.

In undertaking this study, this is also the first time that empirical data have been available with which to assess the suggestion that differences in levels of optimism among Scottish populations may play a part in the country's (and, in particular, its largest city's) levels of unexplained higher mortality. The results suggest it is an unlikely hypothesis, and this implausibility is reinforced by the results of other analyses of ‘psychological outlook’ (relating to time and risk preferences, aspirations, hedonism, self-efficacy, meaningfulness of life) which, similarly, did not show Glasgow's population to be characterized by a more adverse, or potentially more health-damaging, outlook compared with those in Liverpool and Manchester.17

The differences in levels of optimism by age echoes other analyses of optimism and other psychological aspects (e.g. happiness) across the life-course.40 The analyses also confirm that the previously noted relationship between optimism and socio-economic status41 holds within these UK settings. Of course, the analyses also beg questions relating to the proven relationship between LOT-R and health outcomes, the similar or, in the case of the comparison with Manchester, relatively higher optimism scores associated with the Glasgow sample, and whole population-level differences in mortality between the cities. This may relate to the criticism of LOT-R as a ‘context-free’ measure (discussed above) and, thus, one prone to the influence of demographic, socio-economic or cultural differences between populations (as has been shown in relation to self-reporting of general health status between different populations (including within the UK)42–44). However, this would require further research.

Limitations of this study

As stated, the analyses have been based on cross-sectional survey data which do not, therefore, allow any measure of impact, or otherwise, on individuals' subsequent mortality (an important component of the original hypothesis). Any population survey, especially one based on such a sample size and with an overall 55% response rate, is unlikely to be entirely representative of its target population: we have to be aware that it is probable that not all sections of society are represented within the collected data. That said, however, with similar response rates achieved across the three cities, an identical sampling methodology employed in each, and statistical adjustment made for differences in the characteristics of the samples by means of multiple regression, it is unlikely that this impacted on any of the analyses presented here.

Conclusions

The reasons for high levels of ‘excess' mortality in Scotland (and particularly in Glasgow) remain unclear. However, from the evidence presented here, differences in ‘psychological outlook’ such as optimism appear to be an unlikely explanation. The ongoing research into this phenomenon should focus on other, more plausible, potential explanations. These are likely to include underlying vulnerabilities in the Scottish population and inadequate measurement of the lived experience of poverty and deprivation in and around Scotland's largest city.

Funding

The survey was jointly funded by NHS Health Scotland and the Glasgow Centre for Population Health.

Acknowledgements

This work would not have been possible without the co-operation, participation and assistance of a number of individuals and organizations. Thanks are due, first and foremost, to all the survey respondents in Glasgow, Liverpool and Manchester for giving up their time to complete the questionnaire. Grateful thanks are also due to the following for their help, time and efforts: Jo Christensen, Paul Murphy, Jeremy Hardin and Jodie Knight at AECOM Social and Market Research; Ruth McLaughlin, formerly of GCPH, for initial work in the development of the questionnaire; Catherine Ferrell at the MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, for help and insights in commissioning the survey; David Regan, Public Health Manchester, and Paula Grey, Liverpool Primary Care Trust, as well as Colin Cox (Public Health Manchester), Julia Taylor (formerly of Liverpool Primary Care Trust and Liverpool Healthy Cities) and Alison Petrie-Brown (Liverpool Primary Care Trust), for invaluable help in encouraging local participation in the survey; Phil Mason, Mark Livingston and Maria Gannon, all University of Glasgow, for additional statistical advice. Please note that the Chief Scientist Office of the Scottish Government Health Directorates funds HERU. The views expressed in this paper are those of the authors only and not those of the funding body.

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