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Modifiable lifestyle behaviors and functional health in the European Prospective Investigation into Cancer (EPIC)-Norfolk population study
Preventive Medicine 44 (2007) 109 – 116 www.elsevier.com/locate/ypmed
Modifiable lifestyle behaviors and functional health in the European Prospective Investigation into Cancer (EPIC)-Norfolk population study Phyo K. Myint a,d,⁎, Paul G. Surtees a , Nicholas W.J. Wainwright a , Nicholas J. Wareham b , Sheila A. Bingham c , Robert N. Luben a , Ailsa A. Welch a , Richard D. Smith d , Ian M. Harvey d , Kay-Tee Khaw a a
Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK b MRC Epidemiology Unit, Elsie Widdowson Laboratories, Cambridge, UK c MRC Dunn Human Nutrition Unit, Cambridge, UK d School of Medicine, Health Policy and Practice, University of East Anglia, Norwich, UK Available online 27 October 2006
Abstract Objective. To examine the association between modifiable lifestyle behaviors and functional health. Method. Population-based cross-sectional study in 16,678 men and women aged 40–79 years at baseline in 1993–1997 participating in the European Prospective Investigation into Cancer (EPIC)-Norfolk cohort. Results. Smoking and physical inactivity were associated with poorer physical functional health, equivalent to being 7 years and 10–13 years older, respectively, and poorer mental functional health compared to non-smoking or being physically active. After adjusting for age, body mass index, social class, education, prevalent illness, and other lifestyles; men and women who currently smoke were more likely to report poor physical functional health compared to non-smokers {Odds Ratio (OR) = 1.85 (95% confidence interval (CI): 1.49, 2.30) and 1.56 (1.30, 1.87)} and poor mental functional health {1.38 (1.12, 1.70); 1.77 (1.51, 2.07)}, respectively. The OR for good physical function in those who were physically active compared to inactive was 1.67 (1.41, 1.97) in men and 1.63 (1.39, 1.91) in women. Moderate alcohol consumption was positively associated with good physical and mental functional health. Conclusion. Modifiable behavioral factors are associated with substantial differences in the observed age-related decline in physical functional health and the prevalence of those in good and poor functional health in the community. © 2006 Elsevier Inc. All rights reserved. Keywords: Smoking; Alcohol consumption; Physical activity; SF-36; Physical functional health; Mental functional health
Introduction Most studies examining the relationship between behavioral factors, including smoking, alcohol consumption and physical activity and health have focused on clinical end points, such as death and/or cardiovascular disease. Many studies examining the relationship between behavioral factors and subjective functional health are limited by sample size or ⁎ Corresponding author. Clinical Gerontology Unit, Level 2 F and G Block, Box-251, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 2QQ, UK. Fax: +44 0 1223 336928. E-mail address: [email protected] (P.K. Myint). 0091-7435/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ypmed.2006.09.007
composition (e.g. restricted age, sex or patient groups) (Stewart et al., 2003; Woolf et al., 1999; Painter et al., 2000; Kraemer et al., 2002; Arday et al., 2003; Strandberg et al., 2004; Leino-Arjas et al., 2004; Cassidy et al., 2004; Lee and Russell, 2003; Brouwer et al., 2004; Guallar-Castillon et al., 2004; Mitra et al., 2004; Hillsdon et al., 2005). Some studies addressed single factors, for example either smoking or alcohol consumption (Tillmann and Silcock, 1997; Wilson et al., 1999; Van Dijk et al., 2004), and often were unable to examine independent effects or account adequately for confounding from prevalent ill health or social class. Michael et al. (1999) reported the adverse impact of smoking, excess alcohol consumption and physical inactivity
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on physical functional health using the SF-36. However, this study was only in women in one occupational class in the US setting. They did not report on the relationship between these behaviors and mental functional health. Physical functioning generally declines with age (Bond et al., 1993; Adams and White, 2004). However, there is wide variation in the age-related changes that occur with chronological age and the factors that may influence this decline are not well understood or quantified. We examined the independent relationship between smoking, alcohol consumption and physical activity and self-reported physical and mental well being measured by the anglicised version of the short form 36-item questionnaire (UK SF-36) in men and women in the general community. We also quantified the magnitude of relationship of lifestyle factors compared with chronological age on the observed decline in physical functional health. Methods Study population Men and women aged 40–79 were recruited between 1993 and 1997 from general practice registers as part of the European Prospective Investigation into Cancer (EPIC)-Norfolk. The Norwich Local Research Ethics Committee approved the study. Detailed descriptions of the recruitment and methods have been reported previously (Day et al., 1999). Briefly, 30445 men and women aged 40–79 years at the base line consented to participate. A total of 20,921 EPIC-Norfolk participants (73.2% of the eligible sample) completed the HLEQ (Surtees et al., 2004) at 18 months.
Behavioral variables At the baseline assessment, participants completed a self-reported questionnaire. Smoking status was derived from responses to the questions “Have you ever smoked as much as one cigarette a day for as long as a year?” and “Do you smoke cigarettes now?” Individuals were categorised as current smokers, former smokers and never smokers. Alcohol consumption was derived from a food frequency questionnaire (FFQ) collected at the baseline clinic visit. The EPIC-FFQ comprised a list of 130 foods. Under the “drinks” category, nine responses ranging from never to more than six times per day were given for four types of alcoholic drink: half pint of beer, lager or cider, a glass of wine, single unit of spirits (whisky, gin, brandy, vodka, etc.) and a glass of sherry, port, vermouth or liqueurs. Participants were asked to tick each category based on their average alcohol consumption in the previous year. Average alcohol consumption in units/week was calculated (Welch et al., 2005). A four-level physical activity index was derived from the validated EPIC short physical activity questionnaire designed to assess combined work and leisure activity. Participants were categorised into inactive, moderately inactive, moderately active and active categories. The validity and repeatability of this scoring system have been detailed elsewhere (Wareham et al., 2003). Social class obtained in the baseline survey was classified according to the Registrar General's occupation-based classification scheme into 6 social classes; I = professionals, II = managerial, III non-manual and III manual skilled workers, IV = partly skilled workers, V = unskilled manual workers (Elias et al., 1993; Shohaimi et al., 2003). Educational status was based on the highest qualification attained as indicated at the baseline survey, categorised into four groups: degree or equivalent, A level or equivalent, O level or equivalent and less than O level or no qualifications. We recategorised social class and educational level as dichotomous variables. Social classes I, II and III non-manual were classified as “non-manual”, while social
classes III manual, IV and V were classified as “manual”. Educational level was categorised into “at least O level” (O level, A level and degree) and “no qualifications” (lower than O level or no education). At the baseline survey, participants were asked, “Has the doctor ever told you that you have any of the following?”, after which there was a list of various medical conditions. For this study, we defined prevalent illness as self-reported cancer, stroke, myocardial infarction or diabetes mellitus at the baseline. At baseline clinic visit anthropometric measures were obtained using standardised protocols by trained nurses (Lohman et al., 1991). Body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in metres: weight (kg) / {height (m)}2.
Outcome variables Eighteen months after the baseline recruitment, participants (then aged 41– 80 years) were mailed a detailed Health and Life Experiences Questionnaire (HLEQ) that included the SF-36. A total of 20,921 participants (73.2% of the total eligible EPIC sample) responded. The SF-36 measures eight dimensions of health including physical functioning, social functioning, role limitations due to physical problems, role limitations due to emotional problems, mental health, energy/vitality, pain and general health perception. The subscales were scored on a scale from 0 (worst) to 100 (best) health. The Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were derived according to algorithms specified by the original developers (Ware et al., 1993, 1994). Summary scores were created by aggregating across the eight SF-36 subscales, transformed to z-scores and multiplied by their respective factor score coefficients, and standardised as T scores with mean 50 and SD 10 (Surtees et al., 2004). Percentiles from the SF-36 scores were used to categorise Physical Functional Health Status (PFHS) and Mental Functional Health Status (MFHS), using cut points for PCS of 40 (<40 = poor) and 55 (>55 = good) to approximate the bottom and top 20% of the population, respectively. Mental functional health was divided similarly using MCS scores of 46 and 60. This approach allows the potential clinical and population impact of behavioural factors to be quantified. For the current analyses, only participants who had available data on smoking status, alcohol consumption and physical activity level, and had PCS and MCS of the SF-36 questionnaire were included.
Statistical analysis Sex-specific analyses were performed using SPSS for Windows Version 12.0.1 (SPSS Inc., Chicago, Illinois). Mean summary scores for physical and mental components of the SF-36 were tabulated according to smoking habit, alcohol consumption and physical activity. The ANOVA and GLM tests for linearity were used. The trend of association between these lifestyle factors and two main subscales namely physical functioning and mental health, which mainly contributes to PCS and MCS respectively, was examined to assess the validity and reliability of the associations(Taft et al., 2001; Ware and Kosinski, 2001). Using linear regression analysis, the impact of age (per increase in five years age) on physical and mental functioning was calculated for PCS and MCS scores across the sample distribution first unadjusted and then adjusted for BMI, social class (manual and non-manual), education level (no qualifications or at least O level) and prevalent illnesses. The prevalences of those with self-reported poor and good functional health by smoking, alcohol consumption and physical activity status were estimated. Multiple logistic regression analyses were undertaken to assess the odds ratios for having poor or good physical and mental functional health by smoking status, alcohol consumption and physical activity, after adjusting for age, BMI, social class, education and prevalent illness. To minimise the possibility of pre-existing illness reducing physical activity level, the regression analyses were repeated for physical activity after excluding 1802 participants with prevalent illness at baseline, leaving 6494 men and 8382 women in the final analyses. We also repeated the regression analyses after age stratification (<65 and >=65 years). Two sided P values were reported.
P.K. Myint et al. / Preventive Medicine 44 (2007) 109–116 We performed sex-specific analyses to exclude the potential confounding effect of sex as the prevalence of lifestyle behaviors examined and functional health in men and women are different.
Results
Table 1 Sample characteristics of 16,678 men and women in EPIC-Norfolk
Age-SF36 (years) SF-36 PCS SF-36 MCS Smoking Current smoker Ex-smoker Never smoked Number of pack years Alcohol (units/week) Physical activity Active Moderately active Moderately inactive Inactive Occupational social class I II III non-manual III manual IV V Education level No qualification O level A level Degree Prevalent illness
Table 2A Means (SE) of SF-36 Physical Component Score (PCS) firstly age adjusted and secondly age and other covariates including other two lifestyle factors adjusted for different categories of smoking, physical activity and alcohol consumption in 7374 men and 9304 women of EPIC-Norfolk Variable
SF-36 summary scores were available for 19,535 men and women. There were no material differences in terms of age, sex, BMI, systolic blood pressure and cholesterol level compared to non-responders. Excluding those who did not have data on smoking, physical activity and alcohol consumption left 7374 men and 9304 women in the current analyses. The main reason for exclusions was for participants who did not complete FFQs. Table 1 shows the sex-specific distribution of variables. Tables 2A and 2B show SF-36 PCS and MCS mean scores, respectively, adjusted for age at the time of completion of the SF-36, and then adjusted for age and covariates (BMI, social class, education and prevalent illnesses), and other lifestyle factors according to smoking status (never, former and current), alcohol consumption (none or < 1 unit/week, 1–<7 units/week, > = 7 units/week) and physical activity levels (inactive, moderately inactive, moderately active and active). Non-smokers (never smoked) and physically active men and women had significantly higher PCS and MCS scores. Repeating analyses using physical functioning and mental health subscales showed similar trends and associations (data not shown).
Men (N = 7374)
Women (N = 9304)
61.8 (9.1) 47.8 (9.8) 53.0 (9.0)
60.6 (9.1) 47.3 (10.3) 51.8 (9.6)
769 (10.4%) 4023 (54.6%) 2582 (35.0%) 13.2 (17.3) 11.0 (14.0)
923 (9.9%) 2936 (31.6%) 5445 (58.5%) 5.9 (11.2) 6.0 (7.3)
1671 (22.7%) 1777 (24.1%) 1910 (25.9%) 2016 (27.3%)
1615 (17.4%) 2228 (23.9%) 3091 (33.2%) 2370 (25.5%)
607 (8.2%) 2948 (40.0%) 950 (12.9%) 1684 (22.8%) 895 (12.1%) 184 (2.5%)
630 (6.8%) 3360 (36.1%) 1842 (19.8%) 1852 (19.9%) 1125 (12.1%) 324 (3.5%)
2068 (28.0%) 652 (8.8%) 3457 (46.9%) 1197 (16.2%) 880 (11.9%)
4048 (43.5%) 1577 (16.9%) 2530 (27.2%) 1149 (12.3%) 922 (9.9%)
Mean (SD) values or number (%) are presented.
111
Men Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Change in functional score per 5 years increase in age in men Women Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Change in functional score per 5 years increase in age in women
Number Age adjusted Mean (SE)
Age and covariates adjusted Mean (SE)
2582 4023 769
48.90 (0.19) 47.46 (0.15) 46.30 (0.34) <0.0001
48.57 (0.19) 47.48 (0.17) 46.52 (0.36) <0.0001
1389 2458 3527
46.44 (0.25) 47.91 (0.19) 48.35 (0.16) <0.0001
46.62 (0.30) 47.90 (0.24) 48.06 (0.20) <0.0001
2016 1910 1777 1671
45.72 (0.21) 48.32 (0.21) 48.56 (0.22) 49.08 (0.23) <0.0001 β (SE)† − 1.63* (0.06)
45.34 (0.26) 47.64 (0.29) 48.01 (0.28) 49.11 (0.32) <0.0001 β (SE)‡ − 1.38* (0.06)
5445 2936 923
47.90 (0.13) 46.55 (0.18) 46.18 (0.33) <0.0001
48.20 (0.14) 46.87 (0.18) 46.28 (0.33) <0.0001
3276 3748 2280
45.89 (0.17) 47.76 (0.16) 48.58 (0.21) <0.0001
45. 81 (0.24) 47.53 (0.21) 48.02 (0.25) <0.0001
2370 3091 2228 1615
44.97 (0.21) 47.35 (0.18) 48.47 (0.21) 49.01 (0.25) <0.0001 β (SE)† − 1.61* (0.06)
45.37 (0.25) 46.93 (0.23) 47.95 (0.27) 48.23 (0.32) <0.0001 β (SE)‡ − 1.40* (0.06)
7374
9304
Covariates adjusted are body mass index, social class (manual and non-manual), education level (no qualifications or at least O level) and self-reported prevalent illnesses including cancer, stroke, myocardial infarction and diabetes mellitus. † = unadjusted model, ‡ = adjusted for covariates. *p < 0.001.
In Table 2A, we present data for the observed age-related change in physical functional health score for each increase in 5 years of age. The regression slopes β (SE) for men and women were − 1.38 (0.06) and − 1.40 (0.06), respectively controlling for body mass index, social class, education and prevalent illnesses. There was a linear relationship between increasing age and decline in reported physical function. Being a current smoker compared to non-smoker was associated with a difference in mean PCS score of − 2.0 in men and − 1.9 in women, equivalent to being 7 years older in age in both men and women for physical functioning. Similarly, the differences in
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Table 2B Means (SE) of SF-36 Mental Component Score (MCS) firstly age adjusted and secondly age and other covariates adjusted including other two lifestyle factors for different categories of smoking, physical activity and alcohol consumption in 7374 men and 9304 women of EPIC-Norfolk Variable
Men Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Change in functional score per 5 years increase in age in men Women Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Change in functional score per 5 years increase in age in women
Number
Age adjusted Mean (SE)
Age and covariates adjusted Mean (SE)
2582 4023 769
53.31 (0.18) 53.00 (0.14) 52.02 (0.32) 0.002
53.26 (0.19) 52.88 (0.16) 52.14 (0.35) 0.017
1389 2458 3527
52.22 (0.24) 53.31 (0.18) 53.10 (0.15) 0.001
52.13 (0.30) 53.06 (0.24) 53.10 (0.20) 0.017
2016 1910 1777 1671
52.52 (0.20) 52.63 (0.20) 53.13 (0.21) 53.88 (0.22) <0.0001 β (SE)† 0.82* (0.06)
52.02 (0.26) 52.28 (0.29) 53.27 (0.28) 53.47 (0.32) <0.0001 β (SE)‡ 0.87* (0.06)
5445 2936 923
52.24 (0.13) 51.57 (0.17) 49.39 (0.31) <0.0001
52.17 (0.14) 51.69 (0.18) 49.57 (0.33) <0.0001
3276 3748 2280
51.41 (0.17) 52.07 (0.15) 51.71 (0.20) 0.013
50.62 (0.24) 51.40 (0.21) 51.41 (0.25) 0.025
50.85 (0.20) 51.87 (0.17) 52.20 (0.20) 52.21 (0.24) <0.0001 β (SE)† 0.89* (0.05)
50.30 (0.25) 51.13 (0.22) 51.33 (0.27) 51.82 (0.32) 0.001 β (SE)‡ 0.91* (0.06)
7374
2370 3091 2228 1615
9304
Covariates adjusted are body mass index, social class (manual and non-manual), education level (no qualifications or at least O level) and self-reported prevalent illnesses including cancer, stroke, myocardial infarction and diabetes mellitus. † = unadjusted model, ‡ = adjusted for covariates. *p < 0.001.
PCS mean scores of − 3.8 in men and − 2.9 in women between those who were physically inactive compared to active individuals were equivalent to an estimated age-related physical functional decline of 13 years and 10 years for men and women, respectively. Table 2B also shows the age-related change in mental functional health. Older age was associated with better mental functional health. Table 3 shows the prevalence of those reporting poor and good physical and mental functional health categories, as defined by the bottom and top 20th percentile values of SF-36
PCS and MCS scores by individual lifestyle categories. Consistent with the differences in mean scores, the prevalences of those reporting good physical and mental functional health were greater, and the prevalences of those reporting poor physical and mental functional health were lower, in nonsmokers and those who were physically active. For alcohol consumption, trends were less consistent for mental functional health compared to physical functional health. Table 4 shows multiple logistic regression models examining the independent effects of lifestyle behaviors on the likelihood of being in poor or good functional health status, adjusting for age, BMI, social class, education and prevalent illness. All of the lifestyle behaviors examined in this study were simultaneously entered in the model. Current smokers and those who were physically inactive were more likely to report poor physical and mental functional health. Alcohol consumption was associated with a lower likelihood of reporting poor physical functional health and higher likelihood of reporting good physical functional health in both sexes and increased likelihood of poor mental functional health in men. Smokers and those who are physically less active were, conversely, less likely to report good physical functional health, although no significant relationship was observed for good mental functional health with all three variables examined. Repeating the analyses after excluding those who reported prevalent illness at the baseline survey or by age stratification (< 65 and > =65 years) showed consistent relationships between lifestyle factors and functional measures to those for the whole group (data not shown). Discussion In this study, we examined the independent relationships between smoking, alcohol consumption and physical activity, and functional health measured by SF-36 summary scores in a free-living general community. We also quantified the potential magnitude of impact of these behavioral factors in terms of the physical functional decline reported with increasing age and the differences in prevalences of those in good and poor functional health. Those who had never smoked, those who consumed alcohol in moderate amounts and those who were physically active had better self-reported physical and mental functional health independently of age, BMI, social class, education and prevalent illnesses and also independent of each other. This concurs with a previous report in women which examined the relationship of lifestyle behaviours and self-reported health measured by four physical subscales of the SF-36 (Michael et al., 1999). Life expectancy in populations across the world has risen considerably over the last century particularly in developed nations. This increase in life expectancy may not necessarily equate with the prolongation of the healthy life expectancy. There is increasing need to incorporated health measures which cover quality of life or functional health aspects in addition to mortality or disease measures. A major concern in ageing
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Table 3 Prevalence of “poor” and “good” functional health (FH) using bottom and top 20th percentiles values of PCS and MCS for different categories of smoking, physical activity and alcohol consumption in 7374 men and 9304 women of EPIC-Norfolk Variable
Physical functional health Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Mental functional health Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value
Men
Women
Number
Poor FH N (%)
Good FH N (%)
Number
Poor FH N (%)
Good FH N (%)
2582 4023 769
352 (13.6) 887 (22.0) 174 (22.6) <0.0001
751 (29.1) 866 (21.5) 183 (23.8) <0.0001
5445 2936 923
1063 (19.5) 742 (25.3) 209 (22.6) <0.0001
1426 (26.2) 673 (22.9) 226 (24.5) 0.004
1389 2458 3527
350 (25.2) 478 (19.4) 585 (16.6) <0.0001
273 (19.7) 605 (24.6) 922 (26.1) <0.0001
3276 3748 2280
908 (27.7) 731 (19.5) 375 (16.4) <0.0001
693 (21.2) 959 (25.6) 673 (29.5) <0.0001
2016 1910 1777 1671
576 (28.6) 340 (17.8) 272 (15.3) 225 (13.5) <0.0001
335 (16.6) 475 (24.9) 477 (26.8) 513 (30.7) <0.0001
2370 3091 2228 1615
769 (32.4) 642 (20.8) 366 (16.4) 237 (14.7) <0.0001
402 (17.0) 754 (24.4) 642 (28.8) 527 (32.6) <0.0001
2582 4023 769
415 (16.1) 693 (17.2) 169 (22.0) 0.001
372 (14.4) 774 (19.2) 132 (17.2) <0.0001
5445 2936 923
1075 (19.7) 642 (21.9) 292 (31.6) <0.0001
827 (15.2) 458 (15.6) 101 (10.9) 0.002
1389 2458 3527
291 (21.0) 395 (16.1) 591 (16.8) <0.0001
262 (18.9) 435 (17.7) 581 (16.5) 0.116
3276 3748 2280
748 (22.8) 756 (20.2) 505 (22.1) 0.02
549 (16.8) 539 (14.4) 298 (13.1) <0.0001
2016 1910 1777 1671
368 (18.3) 354 (18.5) 302 (17.0) 253 (15.1) 0.031
401 (19.9) 337 (17.6) 273 (15.4) 267 (16.0) 0.001
2370 3091 2228 1615
570 (24.1) 666 (21.5) 454 (20.4) 319 (19.8) 0.003
433 (18.3) 482 (15.6) 271 (12.2) 200 (12.4) <0.0001
Cut points for poor and good physical functional health are PCS scores of 40 and 55 and cut points for poor and good mental functional health are MCS scores of 46 and 60 in both men and women.
populations is the decline in functional health. However, heterogeneity in the decline in physical function with ageing is well recognised (Beckett et al., 1996). LaCroix and colleagues reported that after adjustment for age, risk of losing mobility was significantly associated with current smoking, not consuming alcohol compared with small-to-moderate amounts of alcohol consumption, and low physical activity levels in older people aged 65 and over. They suggested that health behaviours were not only associated with greater longevity but also different risk of losing mobility and independence in later life (LaCroix et al., 1993). Lifestyle behaviours such as non-smoking and physical activity were associated with better mental functional health in our study. A low MCS score has been reported to correlate significantly with depressive symptoms in people with asthma (Mancuso et al., 2000), and pharmacological treatment improves MCS scores in depressed patients (Taylor et al., 2001). Lower levels of MCS scores have been observed with
greater severity of depression (Nease et al., 2002), and prevalent mood disorders (Surtees et al., 2003). Moreover, non-smoking, physical activity and moderate alcohol consumption were associated with significantly lower likelihood of depressive symptoms (Cassidy et al., 2004). In this current older population, self-reported physical functional health declined with age. Unlike physical functioning, older age is not associated with marked decline in mental functional health (Walters et al., 2001). This is consistent with previous reports. In 1998 the Health Survey for England used the General Health Questionnaire (GHQ12) to assess levels of depression, anxiety, sleep disturbance and happiness in the population. With the exception of men and women in the oldest category (> 75 years), the general trend suggested that older age was associated with lower prevalence of high scores (Health Survey for England, 1998). The negative impact of smoking and low levels of physical activity on various health outcomes has been reported
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Table 4 Odds ratios for being in poor or good physical and mental functional health status defined by bottom and top 20th percentiles values for PCS and MCS scores of SF-36 for 7243 men and 9101 women of EPIC-Norfolk using multiple logistic regression controlling for age, body mass index, social class, education level, prevalent illness and other lifestyle variables Odds ratios (95% confidence interval) Physical functional health
Men Smoking Never smoked Former smoker Current smoker Alcohol/week 0–<1 unit 1–<7 units >=7 units Physical activity Inactive Moderately inactive Moderately active Active Women Smoking Never smoked Former smoker Current smoker Alcohol/week 0–<1 unit 1–<7 units >=7 units Physical activity Inactive Moderately inactive Moderately active Active
Mental functional health
Poor (score < 40)
Good (score > 55)
Poor (score < 46)
Good (score > 60)
1.00 1.39 (1.20, 1.62) 1.85 (1.49, 2.30)
1.00 0.88 (0.78, 1.00) 0.81 (0.66, 0.99)
1.00 1.18 (1.02, 1.35) 1.38 (1.12, 1.70)
1.00 1.13 (0.98, 1.30) 1.23 (0.98, 1.54)
1.00 0.82 (0.69, 0.97) 0.72 (0.61, 0.85)
1.00 1.21 (1.02, 1.43) 1.21 (1.02, 1.42)
1.00 0.71 (0.60, 0.85) 0.74 (0.63, 0.87)
1.00 1.03 (0.87, 1.23) 1.01 (0.85, 1.20)
1.00 0.68 (0.58, 0.80) 0.58 (0.49, 0.69) 0.52 (0.43, 0.63)
1.00 1.29 (1.10, 1.53) 1.44 (1.22, 1.70) 1.67 (1.41, 1.97)
1.00 1.05 (0.89, 1.24) 0.85 (0.71, 1.01) 0.71 (0.59, 0.86)
1.00 1.05 (0.89, 1.24) 0.93 (0.78, 1.12) 1.10 (0.92, 1.32)
1.00 1.34 (1.19, 1.50) 1.56 (1.30, 1.87)
1.00 0.89 (0.80, 1.00) 0.74 (0.62, 0.88)
1.00 1.16 (1.03, 1.30) 1.77 (1.51, 2.07)
1.00 0.96 (0.84, 1.10) 0.85 (0.68, 1.07)
1.00 0.73 (0.65, 0.82) 0.62 (0.54, 0.72)
1.00 1.12 (1.00, 1.26) 1.31 (1.15, 1.50)
1.00 0.84 (0.75, 0.95) 0.92 (0.80, 1.05)
1.00 0.94 (0.82, 1.08) 0.89 (0.75, 1.05)
1.00 0.71 (0.62, 0.80) 0.60 (0.51, 0.69) 0.56 (0.47, 0.67)
1.00 1.26 (1.09, 1.45) 1.43 (1.23, 1.66) 1.63 (1.39, 1.91)
1.00 0.83 (0.73, 0.95) 0.74 (0.64, 0.86) 0.70 (0.60, 0.82)
1.00 1.05 (0.91, 1.22) 0.89 (0.75, 1.06) 0.99 (0.82, 1.20)
extensively (Mallampalli and Guntupalli, 2004; Wen et al., 2004; Richardson et al., 2004; Knoops et al., 2004). Additionally, the associations appeared to be dose related across the normal distribution in healthy people, and not just driven by those with illnesses; relationships were also consistent after excluding those with known prevalent illnesses. Moderate alcohol consumption is associated with reduced cardiovascular disease risk, and excessive alcohol consumption is adversely associated with health (Strandberg et al., 2004; Knoops et al., 2004; Hart et al., 1999). In this cohort, although alcohol drinking was associated with better physical and mental functional health compared to non-drinkers, the median alcohol consumption was 14 units/week for men and 11 units/week for women in those who consumed seven unit or more and was well within recommended limits (Joint Working Party Reprt, 1995). Though health behaviours and outcomes vary according to socioeconomic status (Townsend et al., 1994; Graham and Der, 1999), the associations found here were independent of participants' occupational social class and educational level. People who are not well physically and mentally may have reduced their physical activity and therefore there may be an
issue of reverse causality. Exclusion of people with known prevalent illnesses did not alter the results. However, we cannot completely exclude this possibility as we only used self-reported major prevalent illnesses, which may have some inaccuracy, and may not have included conditions such as arthritis. This study has limitations. Because participants had to be willing to provide detailed information and participate in a longterm follow-up study, the response rate was only 40–45% for the baseline and follow-up. Nevertheless, the characteristics of this population were comparable to national samples except for a slightly lower prevalence of smokers (Day et al., 1999). There is an issue of selection bias with healthy responders. Truncation of the distribution would result only in attenuation of the relationships. Moreover, the internal relationship between lifestyle factors and functional health is unlikely to differ significantly between participants and non-participants. Furthermore, mean SF-36 PCS and MCS scores of this cohort are comparable with the UK population norms (Surtees et al., 2004). Although ageing may be nonlinearly related with quality of life, there are many components to quality of life and at least in this population with an older age range of 40–79 years physical function appears to be inversely linearly associated with age.
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Conclusion Our study confirms a strong independent relationship between modifiable behavioral factors and functional health. Current smoking and physical inactivity were associated with poorer physical functioning equivalent to being over 7 years and 10–13 years older, respectively. The combined effect of smoking and physical inactivity on level of physical functioning (mean PCS score of − 5.8 in men and − 4.8 in women) is comparable to having a chronic medical condition such as stroke (Surtees et al., 2003). In agreement with other studies, we found that moderate alcohol consumption is associated with better physical and mental functional health. This study may contribute additional support for existing recommendations to stop smoking and increase their level of physical activity. Acknowledgments We would like to thank participants and general practitioners who took part in the study. We also thank the staff of EPICNorfolk and our funders. Funding: PIC-Norfolk is supported by research programme frant funding frm Center Research UK and the Medical Research Council with additional support from the Stroke Association, British Heart Foundation, Department of Health, Europe Against Cancer Programme Commission of the European Union, Food Standards Agency and Wellcome Trust. The PIC-Norfolk HLEQ research programmeis supported by aprogramme grant from the Medical Research Council UK (G0300128). Ethics approval: Norwich Local Research Ethics Committee approved the study. The corresponding address for the LREC is Clinical Governance Department, Aldwych House, 57 Bethel Street, Norwich. Contributors: KTK, SAB and NJW are principal investigators in EPIC-Norfolk population study. PGS is the principal investigator of EPIC-Norfolk HLEQ programme. RNL is responsible for data management, computing and data linkages. PKM conducted the analysis and wrote the paper with KTK. All co-authors contributed to the writing of this paper. KTK is the guarantor. References Adams, J.M., White, M., 2004. Biological ageing. A fundamental, biological link between socio-economic status and health? Eur. J. Public Health 14, 331–334. Arday, D.R., Milton, M.H., Husten, C.G., et al., 2003. Smoking and functional status among Medicare managed care enrolees. Am. J. Prev. Med. 24, 234–241. Beckett, L.A., Brock, D.B., Lemke, J.H., et al., 1996. Analysis of change in selfreported physical function among older persons in four population studies. Am. J. Epidemiol. 143, 766–778. Bond, J., Briggs, R., Coleman, P., 1993. The study of ageing. In: Bond, J., Coleman, P., Peace, S. (Eds.), Ageing in Society: An Introduction to Social Gerontology. Sage Publications, Wiltshire. Brouwer, B., Musselman, K., Culham, E., 2004. Physical functioning and health status among seniors with and without fear of falling. Gerontology 50, 135–141.
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Modifiable lifestyle behaviors and functional health in the European Prospective Investigation into Cancer (EPIC)-Norfolk population study Phyo K. Myint a,d,⁎, Paul G. Surtees a , Nicholas W.J. Wainwright a , Nicholas J. Wareham b , Sheila A. Bingham c , Robert N. Luben a , Ailsa A. Welch a , Richard D. Smith d , Ian M. Harvey d , Kay-Tee Khaw a a
Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK b MRC Epidemiology Unit, Elsie Widdowson Laboratories, Cambridge, UK c MRC Dunn Human Nutrition Unit, Cambridge, UK d School of Medicine, Health Policy and Practice, University of East Anglia, Norwich, UK Available online 27 October 2006
Abstract Objective. To examine the association between modifiable lifestyle behaviors and functional health. Method. Population-based cross-sectional study in 16,678 men and women aged 40–79 years at baseline in 1993–1997 participating in the European Prospective Investigation into Cancer (EPIC)-Norfolk cohort. Results. Smoking and physical inactivity were associated with poorer physical functional health, equivalent to being 7 years and 10–13 years older, respectively, and poorer mental functional health compared to non-smoking or being physically active. After adjusting for age, body mass index, social class, education, prevalent illness, and other lifestyles; men and women who currently smoke were more likely to report poor physical functional health compared to non-smokers {Odds Ratio (OR) = 1.85 (95% confidence interval (CI): 1.49, 2.30) and 1.56 (1.30, 1.87)} and poor mental functional health {1.38 (1.12, 1.70); 1.77 (1.51, 2.07)}, respectively. The OR for good physical function in those who were physically active compared to inactive was 1.67 (1.41, 1.97) in men and 1.63 (1.39, 1.91) in women. Moderate alcohol consumption was positively associated with good physical and mental functional health. Conclusion. Modifiable behavioral factors are associated with substantial differences in the observed age-related decline in physical functional health and the prevalence of those in good and poor functional health in the community. © 2006 Elsevier Inc. All rights reserved. Keywords: Smoking; Alcohol consumption; Physical activity; SF-36; Physical functional health; Mental functional health
Introduction Most studies examining the relationship between behavioral factors, including smoking, alcohol consumption and physical activity and health have focused on clinical end points, such as death and/or cardiovascular disease. Many studies examining the relationship between behavioral factors and subjective functional health are limited by sample size or ⁎ Corresponding author. Clinical Gerontology Unit, Level 2 F and G Block, Box-251, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 2QQ, UK. Fax: +44 0 1223 336928. E-mail address: [email protected] (P.K. Myint). 0091-7435/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ypmed.2006.09.007
composition (e.g. restricted age, sex or patient groups) (Stewart et al., 2003; Woolf et al., 1999; Painter et al., 2000; Kraemer et al., 2002; Arday et al., 2003; Strandberg et al., 2004; Leino-Arjas et al., 2004; Cassidy et al., 2004; Lee and Russell, 2003; Brouwer et al., 2004; Guallar-Castillon et al., 2004; Mitra et al., 2004; Hillsdon et al., 2005). Some studies addressed single factors, for example either smoking or alcohol consumption (Tillmann and Silcock, 1997; Wilson et al., 1999; Van Dijk et al., 2004), and often were unable to examine independent effects or account adequately for confounding from prevalent ill health or social class. Michael et al. (1999) reported the adverse impact of smoking, excess alcohol consumption and physical inactivity
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on physical functional health using the SF-36. However, this study was only in women in one occupational class in the US setting. They did not report on the relationship between these behaviors and mental functional health. Physical functioning generally declines with age (Bond et al., 1993; Adams and White, 2004). However, there is wide variation in the age-related changes that occur with chronological age and the factors that may influence this decline are not well understood or quantified. We examined the independent relationship between smoking, alcohol consumption and physical activity and self-reported physical and mental well being measured by the anglicised version of the short form 36-item questionnaire (UK SF-36) in men and women in the general community. We also quantified the magnitude of relationship of lifestyle factors compared with chronological age on the observed decline in physical functional health. Methods Study population Men and women aged 40–79 were recruited between 1993 and 1997 from general practice registers as part of the European Prospective Investigation into Cancer (EPIC)-Norfolk. The Norwich Local Research Ethics Committee approved the study. Detailed descriptions of the recruitment and methods have been reported previously (Day et al., 1999). Briefly, 30445 men and women aged 40–79 years at the base line consented to participate. A total of 20,921 EPIC-Norfolk participants (73.2% of the eligible sample) completed the HLEQ (Surtees et al., 2004) at 18 months.
Behavioral variables At the baseline assessment, participants completed a self-reported questionnaire. Smoking status was derived from responses to the questions “Have you ever smoked as much as one cigarette a day for as long as a year?” and “Do you smoke cigarettes now?” Individuals were categorised as current smokers, former smokers and never smokers. Alcohol consumption was derived from a food frequency questionnaire (FFQ) collected at the baseline clinic visit. The EPIC-FFQ comprised a list of 130 foods. Under the “drinks” category, nine responses ranging from never to more than six times per day were given for four types of alcoholic drink: half pint of beer, lager or cider, a glass of wine, single unit of spirits (whisky, gin, brandy, vodka, etc.) and a glass of sherry, port, vermouth or liqueurs. Participants were asked to tick each category based on their average alcohol consumption in the previous year. Average alcohol consumption in units/week was calculated (Welch et al., 2005). A four-level physical activity index was derived from the validated EPIC short physical activity questionnaire designed to assess combined work and leisure activity. Participants were categorised into inactive, moderately inactive, moderately active and active categories. The validity and repeatability of this scoring system have been detailed elsewhere (Wareham et al., 2003). Social class obtained in the baseline survey was classified according to the Registrar General's occupation-based classification scheme into 6 social classes; I = professionals, II = managerial, III non-manual and III manual skilled workers, IV = partly skilled workers, V = unskilled manual workers (Elias et al., 1993; Shohaimi et al., 2003). Educational status was based on the highest qualification attained as indicated at the baseline survey, categorised into four groups: degree or equivalent, A level or equivalent, O level or equivalent and less than O level or no qualifications. We recategorised social class and educational level as dichotomous variables. Social classes I, II and III non-manual were classified as “non-manual”, while social
classes III manual, IV and V were classified as “manual”. Educational level was categorised into “at least O level” (O level, A level and degree) and “no qualifications” (lower than O level or no education). At the baseline survey, participants were asked, “Has the doctor ever told you that you have any of the following?”, after which there was a list of various medical conditions. For this study, we defined prevalent illness as self-reported cancer, stroke, myocardial infarction or diabetes mellitus at the baseline. At baseline clinic visit anthropometric measures were obtained using standardised protocols by trained nurses (Lohman et al., 1991). Body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in metres: weight (kg) / {height (m)}2.
Outcome variables Eighteen months after the baseline recruitment, participants (then aged 41– 80 years) were mailed a detailed Health and Life Experiences Questionnaire (HLEQ) that included the SF-36. A total of 20,921 participants (73.2% of the total eligible EPIC sample) responded. The SF-36 measures eight dimensions of health including physical functioning, social functioning, role limitations due to physical problems, role limitations due to emotional problems, mental health, energy/vitality, pain and general health perception. The subscales were scored on a scale from 0 (worst) to 100 (best) health. The Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were derived according to algorithms specified by the original developers (Ware et al., 1993, 1994). Summary scores were created by aggregating across the eight SF-36 subscales, transformed to z-scores and multiplied by their respective factor score coefficients, and standardised as T scores with mean 50 and SD 10 (Surtees et al., 2004). Percentiles from the SF-36 scores were used to categorise Physical Functional Health Status (PFHS) and Mental Functional Health Status (MFHS), using cut points for PCS of 40 (<40 = poor) and 55 (>55 = good) to approximate the bottom and top 20% of the population, respectively. Mental functional health was divided similarly using MCS scores of 46 and 60. This approach allows the potential clinical and population impact of behavioural factors to be quantified. For the current analyses, only participants who had available data on smoking status, alcohol consumption and physical activity level, and had PCS and MCS of the SF-36 questionnaire were included.
Statistical analysis Sex-specific analyses were performed using SPSS for Windows Version 12.0.1 (SPSS Inc., Chicago, Illinois). Mean summary scores for physical and mental components of the SF-36 were tabulated according to smoking habit, alcohol consumption and physical activity. The ANOVA and GLM tests for linearity were used. The trend of association between these lifestyle factors and two main subscales namely physical functioning and mental health, which mainly contributes to PCS and MCS respectively, was examined to assess the validity and reliability of the associations(Taft et al., 2001; Ware and Kosinski, 2001). Using linear regression analysis, the impact of age (per increase in five years age) on physical and mental functioning was calculated for PCS and MCS scores across the sample distribution first unadjusted and then adjusted for BMI, social class (manual and non-manual), education level (no qualifications or at least O level) and prevalent illnesses. The prevalences of those with self-reported poor and good functional health by smoking, alcohol consumption and physical activity status were estimated. Multiple logistic regression analyses were undertaken to assess the odds ratios for having poor or good physical and mental functional health by smoking status, alcohol consumption and physical activity, after adjusting for age, BMI, social class, education and prevalent illness. To minimise the possibility of pre-existing illness reducing physical activity level, the regression analyses were repeated for physical activity after excluding 1802 participants with prevalent illness at baseline, leaving 6494 men and 8382 women in the final analyses. We also repeated the regression analyses after age stratification (<65 and >=65 years). Two sided P values were reported.
P.K. Myint et al. / Preventive Medicine 44 (2007) 109–116 We performed sex-specific analyses to exclude the potential confounding effect of sex as the prevalence of lifestyle behaviors examined and functional health in men and women are different.
Results
Table 1 Sample characteristics of 16,678 men and women in EPIC-Norfolk
Age-SF36 (years) SF-36 PCS SF-36 MCS Smoking Current smoker Ex-smoker Never smoked Number of pack years Alcohol (units/week) Physical activity Active Moderately active Moderately inactive Inactive Occupational social class I II III non-manual III manual IV V Education level No qualification O level A level Degree Prevalent illness
Table 2A Means (SE) of SF-36 Physical Component Score (PCS) firstly age adjusted and secondly age and other covariates including other two lifestyle factors adjusted for different categories of smoking, physical activity and alcohol consumption in 7374 men and 9304 women of EPIC-Norfolk Variable
SF-36 summary scores were available for 19,535 men and women. There were no material differences in terms of age, sex, BMI, systolic blood pressure and cholesterol level compared to non-responders. Excluding those who did not have data on smoking, physical activity and alcohol consumption left 7374 men and 9304 women in the current analyses. The main reason for exclusions was for participants who did not complete FFQs. Table 1 shows the sex-specific distribution of variables. Tables 2A and 2B show SF-36 PCS and MCS mean scores, respectively, adjusted for age at the time of completion of the SF-36, and then adjusted for age and covariates (BMI, social class, education and prevalent illnesses), and other lifestyle factors according to smoking status (never, former and current), alcohol consumption (none or < 1 unit/week, 1–<7 units/week, > = 7 units/week) and physical activity levels (inactive, moderately inactive, moderately active and active). Non-smokers (never smoked) and physically active men and women had significantly higher PCS and MCS scores. Repeating analyses using physical functioning and mental health subscales showed similar trends and associations (data not shown).
Men (N = 7374)
Women (N = 9304)
61.8 (9.1) 47.8 (9.8) 53.0 (9.0)
60.6 (9.1) 47.3 (10.3) 51.8 (9.6)
769 (10.4%) 4023 (54.6%) 2582 (35.0%) 13.2 (17.3) 11.0 (14.0)
923 (9.9%) 2936 (31.6%) 5445 (58.5%) 5.9 (11.2) 6.0 (7.3)
1671 (22.7%) 1777 (24.1%) 1910 (25.9%) 2016 (27.3%)
1615 (17.4%) 2228 (23.9%) 3091 (33.2%) 2370 (25.5%)
607 (8.2%) 2948 (40.0%) 950 (12.9%) 1684 (22.8%) 895 (12.1%) 184 (2.5%)
630 (6.8%) 3360 (36.1%) 1842 (19.8%) 1852 (19.9%) 1125 (12.1%) 324 (3.5%)
2068 (28.0%) 652 (8.8%) 3457 (46.9%) 1197 (16.2%) 880 (11.9%)
4048 (43.5%) 1577 (16.9%) 2530 (27.2%) 1149 (12.3%) 922 (9.9%)
Mean (SD) values or number (%) are presented.
111
Men Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Change in functional score per 5 years increase in age in men Women Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Change in functional score per 5 years increase in age in women
Number Age adjusted Mean (SE)
Age and covariates adjusted Mean (SE)
2582 4023 769
48.90 (0.19) 47.46 (0.15) 46.30 (0.34) <0.0001
48.57 (0.19) 47.48 (0.17) 46.52 (0.36) <0.0001
1389 2458 3527
46.44 (0.25) 47.91 (0.19) 48.35 (0.16) <0.0001
46.62 (0.30) 47.90 (0.24) 48.06 (0.20) <0.0001
2016 1910 1777 1671
45.72 (0.21) 48.32 (0.21) 48.56 (0.22) 49.08 (0.23) <0.0001 β (SE)† − 1.63* (0.06)
45.34 (0.26) 47.64 (0.29) 48.01 (0.28) 49.11 (0.32) <0.0001 β (SE)‡ − 1.38* (0.06)
5445 2936 923
47.90 (0.13) 46.55 (0.18) 46.18 (0.33) <0.0001
48.20 (0.14) 46.87 (0.18) 46.28 (0.33) <0.0001
3276 3748 2280
45.89 (0.17) 47.76 (0.16) 48.58 (0.21) <0.0001
45. 81 (0.24) 47.53 (0.21) 48.02 (0.25) <0.0001
2370 3091 2228 1615
44.97 (0.21) 47.35 (0.18) 48.47 (0.21) 49.01 (0.25) <0.0001 β (SE)† − 1.61* (0.06)
45.37 (0.25) 46.93 (0.23) 47.95 (0.27) 48.23 (0.32) <0.0001 β (SE)‡ − 1.40* (0.06)
7374
9304
Covariates adjusted are body mass index, social class (manual and non-manual), education level (no qualifications or at least O level) and self-reported prevalent illnesses including cancer, stroke, myocardial infarction and diabetes mellitus. † = unadjusted model, ‡ = adjusted for covariates. *p < 0.001.
In Table 2A, we present data for the observed age-related change in physical functional health score for each increase in 5 years of age. The regression slopes β (SE) for men and women were − 1.38 (0.06) and − 1.40 (0.06), respectively controlling for body mass index, social class, education and prevalent illnesses. There was a linear relationship between increasing age and decline in reported physical function. Being a current smoker compared to non-smoker was associated with a difference in mean PCS score of − 2.0 in men and − 1.9 in women, equivalent to being 7 years older in age in both men and women for physical functioning. Similarly, the differences in
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Table 2B Means (SE) of SF-36 Mental Component Score (MCS) firstly age adjusted and secondly age and other covariates adjusted including other two lifestyle factors for different categories of smoking, physical activity and alcohol consumption in 7374 men and 9304 women of EPIC-Norfolk Variable
Men Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Change in functional score per 5 years increase in age in men Women Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Change in functional score per 5 years increase in age in women
Number
Age adjusted Mean (SE)
Age and covariates adjusted Mean (SE)
2582 4023 769
53.31 (0.18) 53.00 (0.14) 52.02 (0.32) 0.002
53.26 (0.19) 52.88 (0.16) 52.14 (0.35) 0.017
1389 2458 3527
52.22 (0.24) 53.31 (0.18) 53.10 (0.15) 0.001
52.13 (0.30) 53.06 (0.24) 53.10 (0.20) 0.017
2016 1910 1777 1671
52.52 (0.20) 52.63 (0.20) 53.13 (0.21) 53.88 (0.22) <0.0001 β (SE)† 0.82* (0.06)
52.02 (0.26) 52.28 (0.29) 53.27 (0.28) 53.47 (0.32) <0.0001 β (SE)‡ 0.87* (0.06)
5445 2936 923
52.24 (0.13) 51.57 (0.17) 49.39 (0.31) <0.0001
52.17 (0.14) 51.69 (0.18) 49.57 (0.33) <0.0001
3276 3748 2280
51.41 (0.17) 52.07 (0.15) 51.71 (0.20) 0.013
50.62 (0.24) 51.40 (0.21) 51.41 (0.25) 0.025
50.85 (0.20) 51.87 (0.17) 52.20 (0.20) 52.21 (0.24) <0.0001 β (SE)† 0.89* (0.05)
50.30 (0.25) 51.13 (0.22) 51.33 (0.27) 51.82 (0.32) 0.001 β (SE)‡ 0.91* (0.06)
7374
2370 3091 2228 1615
9304
Covariates adjusted are body mass index, social class (manual and non-manual), education level (no qualifications or at least O level) and self-reported prevalent illnesses including cancer, stroke, myocardial infarction and diabetes mellitus. † = unadjusted model, ‡ = adjusted for covariates. *p < 0.001.
PCS mean scores of − 3.8 in men and − 2.9 in women between those who were physically inactive compared to active individuals were equivalent to an estimated age-related physical functional decline of 13 years and 10 years for men and women, respectively. Table 2B also shows the age-related change in mental functional health. Older age was associated with better mental functional health. Table 3 shows the prevalence of those reporting poor and good physical and mental functional health categories, as defined by the bottom and top 20th percentile values of SF-36
PCS and MCS scores by individual lifestyle categories. Consistent with the differences in mean scores, the prevalences of those reporting good physical and mental functional health were greater, and the prevalences of those reporting poor physical and mental functional health were lower, in nonsmokers and those who were physically active. For alcohol consumption, trends were less consistent for mental functional health compared to physical functional health. Table 4 shows multiple logistic regression models examining the independent effects of lifestyle behaviors on the likelihood of being in poor or good functional health status, adjusting for age, BMI, social class, education and prevalent illness. All of the lifestyle behaviors examined in this study were simultaneously entered in the model. Current smokers and those who were physically inactive were more likely to report poor physical and mental functional health. Alcohol consumption was associated with a lower likelihood of reporting poor physical functional health and higher likelihood of reporting good physical functional health in both sexes and increased likelihood of poor mental functional health in men. Smokers and those who are physically less active were, conversely, less likely to report good physical functional health, although no significant relationship was observed for good mental functional health with all three variables examined. Repeating the analyses after excluding those who reported prevalent illness at the baseline survey or by age stratification (< 65 and > =65 years) showed consistent relationships between lifestyle factors and functional measures to those for the whole group (data not shown). Discussion In this study, we examined the independent relationships between smoking, alcohol consumption and physical activity, and functional health measured by SF-36 summary scores in a free-living general community. We also quantified the potential magnitude of impact of these behavioral factors in terms of the physical functional decline reported with increasing age and the differences in prevalences of those in good and poor functional health. Those who had never smoked, those who consumed alcohol in moderate amounts and those who were physically active had better self-reported physical and mental functional health independently of age, BMI, social class, education and prevalent illnesses and also independent of each other. This concurs with a previous report in women which examined the relationship of lifestyle behaviours and self-reported health measured by four physical subscales of the SF-36 (Michael et al., 1999). Life expectancy in populations across the world has risen considerably over the last century particularly in developed nations. This increase in life expectancy may not necessarily equate with the prolongation of the healthy life expectancy. There is increasing need to incorporated health measures which cover quality of life or functional health aspects in addition to mortality or disease measures. A major concern in ageing
P.K. Myint et al. / Preventive Medicine 44 (2007) 109–116
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Table 3 Prevalence of “poor” and “good” functional health (FH) using bottom and top 20th percentiles values of PCS and MCS for different categories of smoking, physical activity and alcohol consumption in 7374 men and 9304 women of EPIC-Norfolk Variable
Physical functional health Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value Mental functional health Smoking Never Former Current P value Alcohol (units/week) None or <1 1–<7 >=7 P value Physical activity Inactive Moderately inactive Moderately active Active P value
Men
Women
Number
Poor FH N (%)
Good FH N (%)
Number
Poor FH N (%)
Good FH N (%)
2582 4023 769
352 (13.6) 887 (22.0) 174 (22.6) <0.0001
751 (29.1) 866 (21.5) 183 (23.8) <0.0001
5445 2936 923
1063 (19.5) 742 (25.3) 209 (22.6) <0.0001
1426 (26.2) 673 (22.9) 226 (24.5) 0.004
1389 2458 3527
350 (25.2) 478 (19.4) 585 (16.6) <0.0001
273 (19.7) 605 (24.6) 922 (26.1) <0.0001
3276 3748 2280
908 (27.7) 731 (19.5) 375 (16.4) <0.0001
693 (21.2) 959 (25.6) 673 (29.5) <0.0001
2016 1910 1777 1671
576 (28.6) 340 (17.8) 272 (15.3) 225 (13.5) <0.0001
335 (16.6) 475 (24.9) 477 (26.8) 513 (30.7) <0.0001
2370 3091 2228 1615
769 (32.4) 642 (20.8) 366 (16.4) 237 (14.7) <0.0001
402 (17.0) 754 (24.4) 642 (28.8) 527 (32.6) <0.0001
2582 4023 769
415 (16.1) 693 (17.2) 169 (22.0) 0.001
372 (14.4) 774 (19.2) 132 (17.2) <0.0001
5445 2936 923
1075 (19.7) 642 (21.9) 292 (31.6) <0.0001
827 (15.2) 458 (15.6) 101 (10.9) 0.002
1389 2458 3527
291 (21.0) 395 (16.1) 591 (16.8) <0.0001
262 (18.9) 435 (17.7) 581 (16.5) 0.116
3276 3748 2280
748 (22.8) 756 (20.2) 505 (22.1) 0.02
549 (16.8) 539 (14.4) 298 (13.1) <0.0001
2016 1910 1777 1671
368 (18.3) 354 (18.5) 302 (17.0) 253 (15.1) 0.031
401 (19.9) 337 (17.6) 273 (15.4) 267 (16.0) 0.001
2370 3091 2228 1615
570 (24.1) 666 (21.5) 454 (20.4) 319 (19.8) 0.003
433 (18.3) 482 (15.6) 271 (12.2) 200 (12.4) <0.0001
Cut points for poor and good physical functional health are PCS scores of 40 and 55 and cut points for poor and good mental functional health are MCS scores of 46 and 60 in both men and women.
populations is the decline in functional health. However, heterogeneity in the decline in physical function with ageing is well recognised (Beckett et al., 1996). LaCroix and colleagues reported that after adjustment for age, risk of losing mobility was significantly associated with current smoking, not consuming alcohol compared with small-to-moderate amounts of alcohol consumption, and low physical activity levels in older people aged 65 and over. They suggested that health behaviours were not only associated with greater longevity but also different risk of losing mobility and independence in later life (LaCroix et al., 1993). Lifestyle behaviours such as non-smoking and physical activity were associated with better mental functional health in our study. A low MCS score has been reported to correlate significantly with depressive symptoms in people with asthma (Mancuso et al., 2000), and pharmacological treatment improves MCS scores in depressed patients (Taylor et al., 2001). Lower levels of MCS scores have been observed with
greater severity of depression (Nease et al., 2002), and prevalent mood disorders (Surtees et al., 2003). Moreover, non-smoking, physical activity and moderate alcohol consumption were associated with significantly lower likelihood of depressive symptoms (Cassidy et al., 2004). In this current older population, self-reported physical functional health declined with age. Unlike physical functioning, older age is not associated with marked decline in mental functional health (Walters et al., 2001). This is consistent with previous reports. In 1998 the Health Survey for England used the General Health Questionnaire (GHQ12) to assess levels of depression, anxiety, sleep disturbance and happiness in the population. With the exception of men and women in the oldest category (> 75 years), the general trend suggested that older age was associated with lower prevalence of high scores (Health Survey for England, 1998). The negative impact of smoking and low levels of physical activity on various health outcomes has been reported
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Table 4 Odds ratios for being in poor or good physical and mental functional health status defined by bottom and top 20th percentiles values for PCS and MCS scores of SF-36 for 7243 men and 9101 women of EPIC-Norfolk using multiple logistic regression controlling for age, body mass index, social class, education level, prevalent illness and other lifestyle variables Odds ratios (95% confidence interval) Physical functional health
Men Smoking Never smoked Former smoker Current smoker Alcohol/week 0–<1 unit 1–<7 units >=7 units Physical activity Inactive Moderately inactive Moderately active Active Women Smoking Never smoked Former smoker Current smoker Alcohol/week 0–<1 unit 1–<7 units >=7 units Physical activity Inactive Moderately inactive Moderately active Active
Mental functional health
Poor (score < 40)
Good (score > 55)
Poor (score < 46)
Good (score > 60)
1.00 1.39 (1.20, 1.62) 1.85 (1.49, 2.30)
1.00 0.88 (0.78, 1.00) 0.81 (0.66, 0.99)
1.00 1.18 (1.02, 1.35) 1.38 (1.12, 1.70)
1.00 1.13 (0.98, 1.30) 1.23 (0.98, 1.54)
1.00 0.82 (0.69, 0.97) 0.72 (0.61, 0.85)
1.00 1.21 (1.02, 1.43) 1.21 (1.02, 1.42)
1.00 0.71 (0.60, 0.85) 0.74 (0.63, 0.87)
1.00 1.03 (0.87, 1.23) 1.01 (0.85, 1.20)
1.00 0.68 (0.58, 0.80) 0.58 (0.49, 0.69) 0.52 (0.43, 0.63)
1.00 1.29 (1.10, 1.53) 1.44 (1.22, 1.70) 1.67 (1.41, 1.97)
1.00 1.05 (0.89, 1.24) 0.85 (0.71, 1.01) 0.71 (0.59, 0.86)
1.00 1.05 (0.89, 1.24) 0.93 (0.78, 1.12) 1.10 (0.92, 1.32)
1.00 1.34 (1.19, 1.50) 1.56 (1.30, 1.87)
1.00 0.89 (0.80, 1.00) 0.74 (0.62, 0.88)
1.00 1.16 (1.03, 1.30) 1.77 (1.51, 2.07)
1.00 0.96 (0.84, 1.10) 0.85 (0.68, 1.07)
1.00 0.73 (0.65, 0.82) 0.62 (0.54, 0.72)
1.00 1.12 (1.00, 1.26) 1.31 (1.15, 1.50)
1.00 0.84 (0.75, 0.95) 0.92 (0.80, 1.05)
1.00 0.94 (0.82, 1.08) 0.89 (0.75, 1.05)
1.00 0.71 (0.62, 0.80) 0.60 (0.51, 0.69) 0.56 (0.47, 0.67)
1.00 1.26 (1.09, 1.45) 1.43 (1.23, 1.66) 1.63 (1.39, 1.91)
1.00 0.83 (0.73, 0.95) 0.74 (0.64, 0.86) 0.70 (0.60, 0.82)
1.00 1.05 (0.91, 1.22) 0.89 (0.75, 1.06) 0.99 (0.82, 1.20)
extensively (Mallampalli and Guntupalli, 2004; Wen et al., 2004; Richardson et al., 2004; Knoops et al., 2004). Additionally, the associations appeared to be dose related across the normal distribution in healthy people, and not just driven by those with illnesses; relationships were also consistent after excluding those with known prevalent illnesses. Moderate alcohol consumption is associated with reduced cardiovascular disease risk, and excessive alcohol consumption is adversely associated with health (Strandberg et al., 2004; Knoops et al., 2004; Hart et al., 1999). In this cohort, although alcohol drinking was associated with better physical and mental functional health compared to non-drinkers, the median alcohol consumption was 14 units/week for men and 11 units/week for women in those who consumed seven unit or more and was well within recommended limits (Joint Working Party Reprt, 1995). Though health behaviours and outcomes vary according to socioeconomic status (Townsend et al., 1994; Graham and Der, 1999), the associations found here were independent of participants' occupational social class and educational level. People who are not well physically and mentally may have reduced their physical activity and therefore there may be an
issue of reverse causality. Exclusion of people with known prevalent illnesses did not alter the results. However, we cannot completely exclude this possibility as we only used self-reported major prevalent illnesses, which may have some inaccuracy, and may not have included conditions such as arthritis. This study has limitations. Because participants had to be willing to provide detailed information and participate in a longterm follow-up study, the response rate was only 40–45% for the baseline and follow-up. Nevertheless, the characteristics of this population were comparable to national samples except for a slightly lower prevalence of smokers (Day et al., 1999). There is an issue of selection bias with healthy responders. Truncation of the distribution would result only in attenuation of the relationships. Moreover, the internal relationship between lifestyle factors and functional health is unlikely to differ significantly between participants and non-participants. Furthermore, mean SF-36 PCS and MCS scores of this cohort are comparable with the UK population norms (Surtees et al., 2004). Although ageing may be nonlinearly related with quality of life, there are many components to quality of life and at least in this population with an older age range of 40–79 years physical function appears to be inversely linearly associated with age.
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Conclusion Our study confirms a strong independent relationship between modifiable behavioral factors and functional health. Current smoking and physical inactivity were associated with poorer physical functioning equivalent to being over 7 years and 10–13 years older, respectively. The combined effect of smoking and physical inactivity on level of physical functioning (mean PCS score of − 5.8 in men and − 4.8 in women) is comparable to having a chronic medical condition such as stroke (Surtees et al., 2003). In agreement with other studies, we found that moderate alcohol consumption is associated with better physical and mental functional health. This study may contribute additional support for existing recommendations to stop smoking and increase their level of physical activity. Acknowledgments We would like to thank participants and general practitioners who took part in the study. We also thank the staff of EPICNorfolk and our funders. Funding: PIC-Norfolk is supported by research programme frant funding frm Center Research UK and the Medical Research Council with additional support from the Stroke Association, British Heart Foundation, Department of Health, Europe Against Cancer Programme Commission of the European Union, Food Standards Agency and Wellcome Trust. The PIC-Norfolk HLEQ research programmeis supported by aprogramme grant from the Medical Research Council UK (G0300128). Ethics approval: Norwich Local Research Ethics Committee approved the study. The corresponding address for the LREC is Clinical Governance Department, Aldwych House, 57 Bethel Street, Norwich. Contributors: KTK, SAB and NJW are principal investigators in EPIC-Norfolk population study. PGS is the principal investigator of EPIC-Norfolk HLEQ programme. RNL is responsible for data management, computing and data linkages. PKM conducted the analysis and wrote the paper with KTK. All co-authors contributed to the writing of this paper. KTK is the guarantor. References Adams, J.M., White, M., 2004. Biological ageing. A fundamental, biological link between socio-economic status and health? Eur. J. Public Health 14, 331–334. Arday, D.R., Milton, M.H., Husten, C.G., et al., 2003. Smoking and functional status among Medicare managed care enrolees. Am. J. Prev. Med. 24, 234–241. Beckett, L.A., Brock, D.B., Lemke, J.H., et al., 1996. Analysis of change in selfreported physical function among older persons in four population studies. Am. J. Epidemiol. 143, 766–778. Bond, J., Briggs, R., Coleman, P., 1993. The study of ageing. In: Bond, J., Coleman, P., Peace, S. (Eds.), Ageing in Society: An Introduction to Social Gerontology. Sage Publications, Wiltshire. Brouwer, B., Musselman, K., Culham, E., 2004. Physical functioning and health status among seniors with and without fear of falling. Gerontology 50, 135–141.
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