Neighborhood Environments and Socioeconomic Inequalities in Mental Well-Being Richard J. Mitchell, PhD, Elizabeth A. Richardson, PhD, Niamh K. Shortt, PhD, Jamie R. Pearce, PhD Introduction: It has been suggested that socioeconomic inequalities in health might be reduced among populations with good access to green space. However, the potential for other neighborhood characteristics to reduce socioeconomic health inequalities, or to confound the effects of green space, has not been well explored. Therefore, this study investigates which, if any, neighborhood characteristics are associated with narrower socioeconomic inequalities in mental well-being in a large, international sample of urban residents. Methods: The 2012 European Quality of Life Survey provided data on 21,294 urban residents from 34 European nations. Associations between mental well-being (captured by the WHO-5 scale) and level of financial strain were assessed for interaction with five different neighborhood characteristics, including reported access to recreational/green areas, financial services, transport, and cultural facilities. Multilevel regression models allowed for clustering of individuals within region and country in this cross-sectional, observational study. Data were analyzed in 2014. Results: Socioeconomic inequality in mental well-being was 40% (8.1 WHO-5 points) narrower among respondents reporting good access to green/recreational areas, compared with those with poorer access. None of the other neighborhood characteristics or services were associated with narrower inequality. Conclusions: If societies cannot, or will not, narrow socioeconomic inequality, research should explore the so-called equigenic environments—those that can disrupt the usual conversion of socioeconomic inequality to health inequality. This large, international, observational study suggests that access to recreational/green areas may offer such a disruption. (Am J Prev Med 2015;49(1):80–84) & 2015 American Journal of Preventive Medicine
Introduction
M
ost research on environment and health has focused on the threats to health that some environments make and those threats’ inequitable distribution.1 However, an alternative perspective is emerging, asking whether some environments might protect health and limit socioeconomic health inequalities.2–6 Access to green space is one factor that has received attention, with studies7–14 showing benefits From the Centre for Research on Environment, Society and Health, University of Glasgow, Glasgow (Mitchell); and the Centre for Research on Environment, Society and Health, University of Edinburgh, Edinburgh (Richardson, Shortt, Pearce), United Kingdom Address correspondence to: Richard J. Mitchell, PhD, Centre for Research on Environment, Society and Health, Institute of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, 1 Lilybank Gardens, Glasgow G12 8RZ. E-mail: richard.mitchell@ glasgow.ac.uk. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2015.01.017
80 Am J Prev Med 2015;49(1):80–84
to the physiologic and psychological health of individuals and populations. At least three studies15–17 suggest particular benefit for socioeconomically deprived populations and that green space may therefore narrow health inequalities. However, these did not compare green space with other features of the environment. It is plausible, indeed probable, that neighborhoods with better access to green space also possess other characteristics or services that may promote health. This study is, therefore, the first to ask which neighborhood characteristics or services are associated with narrower socioeconomic health inequalities in a large, international sample of urban residents.
Methods Study Sample The 2012 European Quality of Life Survey (EQLS) was designed to assess influences on quality of life among those aged Z18 years in
& 2015 American Journal of Preventive Medicine
Published by Elsevier Inc.
81
Mitchell et al / Am J Prev Med 2015;49(1):80–84 18
34 European nations. Full sampling and survey details are available elsewhere.19 This analysis, run in 2014, only included respondents living in urban areas (Appendix Table 1).
Table 1. Character and Distribution of Categorical Covariates Included in Regression Analysis Variable/Categories
n (%)
Sex
Measures
Male
Mental well-being was measured using the robust and validated WHO-5,20–22 assessing respondents’ attitude toward life in the 2 weeks prior to interview (Appendix Table 2). Scores range from 0 to 100, where 100 denotes the maximum possible well-being. The primary measure of socioeconomic position (SEP) was perceived financial strain, distinguishing between those able to make ends meet at the end of the month very easily or easily, fairly easily or with some difficulty, and with difficulty or great difficulty. Sensitivity to the use of financial strain was tested by repeating analyses using education level as the primary measure. There were no substantive differences in results. Respondents reported access to five different neighborhood characteristics or services: recreational/green areas, postal services, banking services, public transport, and cultural services (including cinema, theatre, or a cultural center). For each, respondents were asked: Thinking of physical access, distance, opening hours and the like, how would you describe your access to the following service? They chose a response from with great difficulty to very easily, but, importantly, were also able to report not using this particular service (Table 1). Models adjusted for respondents’ reported sex, age, illness limiting daily activities, education level, and employment status. Because better access to neighborhood services might be a proxy for the absence of neighborhood incivilities, models adjusted for reported problems with: noise; air quality; crime, violence, or vandalism; litter or rubbish; and traffic congestion. Models also adjusted for national median household income (equivalized for differences in purchasing power between nations and currencies) in case perception of financial strain was affected by the wealth of wider society.23 Levels of missing data were low, with 1.5% of respondents missing a WHO-5 score, 1.3% missing perceived financial strain, and 5.4% missing any other independent variable. The complete case analysis used a sample size of 21,294 (Table 1). EQLS does not provide weights at both regional and national levels; therefore, analyses were unweighted.24
Female
Statistical Analysis A multilevel linear regression model assessed variation in the relationship between perceived financial strain and mental wellbeing, by reported access to neighborhood services. This was achieved by fitting interaction terms between each service access variable and financial strain, assessing significance using Wald tests. Models were run in Stata, version 12.1, and allowed for clustering of individuals within subnational region and within nation. National median income was modeled as a fixed effect at the national level. For significant interactions, the “margins” command was used to estimate the mean WHO-5 score for each combination of financial strain and reported access to a neighborhood service. This approach accounts for the different distribution of confounders in each group. July 2015
9,159 (43.01) 12,135 (56.99)
Education Primary or less
2,077 (9.75)
Secondary
12,990 (61.0)
Tertiary
6,227 (29.24)
Employment status Employed
10,057 (47.23)
Unemployed
1,632 (7.66)
Unable to work
431 (2.02)
Retired
5,704 (26.79)
Homemaker
1,713 (8.04)
Student
1,522 (7.15)
Other
235 (1.10)
Limiting illness Yes, severely limited
1,379 (6.48)
Yes, limited to some extent
3,072 (14.43)
Yes, but not limited
1,537 (7.22)
No illness
15,306 (71.88)
Financial strain (ability to make ends meet) Very easily/easily Fairly easily / some difficulty With difficulty / great difficulty
5,295 (24.87) 12,280 (57.67) 3,719 (17.47)
Problems in the immediate neighborhood Noise Major problems
2,043 (9.59)
Moderate problems
6,219 (29.21)
No problems
13,032 (61.2)
Air quality Major problems
1,892 (8.89)
Moderate problems
5,553 (26.08)
No problems
13,849 (65.04)
Crime, violence, or vandalism Major problems Moderate problems
1,797 (8.44) 6,424 (30.17) (continued on next page)
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Table 1. Character and Distribution of Categorical Covariates Included in Regression Analysis (continued) Variable/Categories
n (%)
No problems
13,073 (61.39)
Litter or rubbish on the street
Table 1. (continued) Variable/Categories
n (%)
With some difficulty
2,004 (9.41)
Easily
9,352 (43.92)
Major problems
2,258 (10.6)
Very easily
7,794 (36.60)
Moderate problems
6,338 (29.76)
Service not used
1,525 (7.16)
No problems
12,698 (59.63)
Traffic congestion Major problems
2,706 (12.71)
Results
Moderate problems
5,791 (27.20)
Reported access to recreational/green areas was the only neighborhood service to have a significant interaction with financial strain in its relationship with mental wellbeing. Inequality in mental well-being was narrower among those reporting better access to recreational/green areas (chi-squared¼16.08, p¼0.041) (Appendix Table 3). Figure 1 illustrates this in two ways. First, it shows that the difference in estimated WHO-5 scores between those under most and least financial strain fell successively as reported access to green/recreational areas improved. The gap was 8.1 points, or 40%, smaller among those reporting best access than among those reporting worst access. Second, sloped lines compare gradients in mental well-being among different levels of reported access to recreational/green areas; the gradient is shallower where reported access is better. Inequality in mental well-being among those who did not use their recreational/green space was at about the same level as those who reported some difficulty with access.
No problems
12,797 (60.10)
Access to neighborhood services/facilities Postal service With great difficulty
596 (2.80)
With some difficulty
2,332 (10.95)
Easily
10,003 (46.98)
Very easily
7,359 (34.56)
Service not used
1,004 (4.71)
Banking service With great difficulty
501 (2.35)
With some difficulty
2,150 (10.10)
Easily Very easily Service not used
10,155 (47.69) 7,756 (36.42) 732 (3.44)
Public transport With great difficulty
744 (3.49)
With some difficulty
1,969 (9.25)
Easily
8,653 (40.64)
Very easily
7,258 (34.08)
Service not used
2,670 (12.54)
Cinema, theatre, or cultural center With great difficulty
958 (4.50)
With some difficulty
2,732 (12.83)
Easily
8,932 (41.95)
Very easily
5,389 (25.31)
Service not used
3,283 (15.42)
Recreational/green areas With great difficulty
619 (2.91) (continued)
Discussion Socioeconomic inequalities in mental well-being were smaller among urban dwellers reporting good access to recreational/green areas, compared with those reporting difficulty with access. There was no such difference for the other tested neighborhood services. The frequency or type of use of recreational/green areas was not reported. Although it is plausible that those experiencing financial strain who reported easier access use their areas more, or in ways that have greater benefits for well-being, the existing literature25–27 does not support this hypothesis. Alternatively, perhaps the beneficial effects of using recreational/green areas are stronger for those under greater levels of stress. Many experiments have identified that contact with nature can offer psychological restoration to those who are stressed or fatigued.28–32 Perhaps those with no financial strain are less in need of restoration, or have already maximized www.ajpmonline.org
Mitchell et al / Am J Prev Med 2015;49(1):80–84
83
Figure 1. Predicted mean mental well-being by perceived financial strain and ease of access to recreational/green areas.
their well-being. Experimental studies14 have not explored variation in restorative effect by SEP, and this requires further research. Strengths of this study included individual-level data; control for important influences on mental well-being; a large, international sample; a robust measure of mental well-being; control for other neighborhood incivilities; and, importantly, comparison of several neighborhood services, with identification of those not using each service. Limitations included self-reported neighborhood characteristics and mental well-being outcome, raising the possibility of same-source bias.33,34 Objectively measured neighborhood characteristics may show different associations with health.35 The term recreational/green areas was non-specific, though it is likely the word areas would discourage respondents from thinking of indoor facilities, such as gyms. The interpretation of results assumes these areas provided access to nature, but recreational areas could include those in which contact with nature is minimal. Although the cross-sectional design of this large, international study cannot prove causality, there are at least two reasons why a causal effect is plausible: the narrowing of inequality did not occur among those not using their recreational/green areas, and experimental studies5,10,14,36 have proven that contact with nature can cause improved mental well-being.
July 2015
Conclusions Public health professionals have campaigned for more equal distribution of power and resources to reduce health inequalities. Yet, a radically more equal world currently looks unlikely. While this campaign continues, additional ideas are needed. Environments that act to disrupt the usual conversion of socioeconomic adversity to a greater risk of poor health have been labeled “equigenic,” which may include recreational/green areas.6 Identifying and testing other equigenic characteristics should be a priority. We are grateful to Frank Popham for comments on an earlier draft. We are indebted to the Eurofound team for collecting and curating the European Quality of Life survey. This work was supported by the European Research Council (ERC-2010StG Grant 263501). The funder had no role at all in study design, analysis, interpretation, writing, or the decision to submit. RM designed the study, carried out the analyses, and wrote the first draft. JP, NS, and ER contributed to the concept and design of the study, and to writing subsequent and final drafts. RM is the guarantor. No financial disclosures were reported by the authors of this paper.
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References 1. Brulle RJ, Pellow DN. Environmental justice: human health and environmental inequalities. Annu Rev Public Health. 2006;27:103– 124. http://dx.doi.org/10.1146/annurev.publhealth.27.021405.102124. 2. Morgan A, Ziglio E. Revitalising the evidence base for public health: an assets model. Glob Health Promot. 2007;14(2 suppl):17–22. 3. Scottish Government. Good Places, Better Health: A New Approach to Environment and Health in Scotland. Edinburgh: Scottish Government; 2009. 4. Frumkin H. Healthy places: exploring the evidence. Am J Public Health. 2003;93(9):1451–1456. http://dx.doi.org/10.2105/AJPH.93.9. 1451. 5. Africa J, Logan A, Mitchell R, et al. The Natural Environments Initiative: Illustrative Review and Workshop Statement. Boston: Center for Health and the Global Environment at the Harvard School of Public Health; 2014. 6. Mitchell R. What is equigenesis and how might it help narrow health inequalities? CRESH Blog Series. 2013. http://cresh.org.uk/ 2013/11/08/what-is-equigenesis-and-how-might-it-help-narrow-healthinequalities. 7. Park B, Tsunetsugu Y, Kasetani T, Kagawa T, Miyazaki Y. The physiological effects of Shinrin-yoku (taking in the forest atmosphere or forest bathing): evidence from field experiments in 24 forests across Japan. Environ Health Prev Med. 2010;15(1):18–26. http://dx.doi.org/ 10.1007/s12199-009-0086-9. 8. Lee J, Park BJ, Tsunetsugu Y, Ohira T, Kagawa T, Miyazaki Y. Effect of forest bathing on physiological and psychological responses in young Japanese male subjects. Public Health. 2011;125(2):93–100. http://dx. doi.org/10.1016/j.puhe.2010.09.005. 9. Tsunetsugu Y, Park B, Miyazaki Y. Trends in research related to “Shinrin-yoku” (taking in the forest atmosphere or forest bathing) in Japan. Environ Health Prev Med. 2010;15(1):27–37. http://dx.doi.org/ 10.1007/s12199-009-0091-z. 10. Selhub EM, Logan AC. Your Brain on Nature: The Science of Nature's Influence on Your Health, Happiness and Vitality. Mississauga, Ontario, Canada: John Wiley & Sons; 2012. 11. Barton J, Pretty J. What is the best dose of nature and green exercise for improving mental health? A Multi-Study Analysis. Environ Sci Technol. 2010;44(10):3947–3955. http://dx.doi.org/10.1021/es903183r. 12. Pretty J, Peacock J, Sellens M, Griffin M. The mental and physical health outcomes of green exercise. Int J Environ Health Res. 2005; 15(5):319–337. http://dx.doi.org/10.1080/09603120500155963. 13. Nilsson K, Sangster M, Gallis C, et al. Forests, Trees and Human Health. New York: Springer Science; 2011. http://dx.doi.org/10.1007/ 978-90-481-9806-1. 14. Hartig T, Mitchell R, De Vries S, Frumkin H. Nature and health. Annu Rev Public Health. 2014;35:207–228. http://dx.doi.org/10.1146/ annurev-publhealth-032013-182443. 15. Maas J, Verheij RA, Groenewegen PP, de Vries S, Spreeuwenberg P. Green space, urbanity, and health: how strong is the relation? J. Epidemiol Community Health. 2006;60(7):587–592. http://dx.doi. org/10.1136/jech.2005.043125. 16. Lachowycz K, Jones AP. Does walking explain associations between access to greenspace and lower mortality? Soc Sci Med. 2014;107:9–17. http://dx.doi.org/10.1016/j.socscimed.2014.02.023. 17. Mitchell R, Popham F. Effect of exposure to natural environment on health inequalities: an observational population study. Lancet. 2008;372(9650):1655–1660. http://dx.doi.org/10.1016/S0140-6736(08) 61689-X. 18. Eurofound. Third European Quality of Life Survey—Quality of Life in Europe: Impacts of the Crisis. Luxembourg: Publications Office of the European Union; 2012. 19. Eurofound. 3rd European Quality of Life Survey—Technical Report. Luxembourg: Eurofound; 2012.
20. WHO. Wellbeing Measures in Primary Health Care/The DEPCARE Project. Regional Office for Europe. Copenhagen: WHO; 1998. 21. Bech P, Olsen LR, Kjoller M, Rasmussen NK. Measuring well-being rather than the absence of distress symptoms: a comparison of the SF36 Mental Health subscale and the WHO-Five well-being scale. Int J Methods Psychiatr Res. 2003;12(2):85–91. http://dx.doi.org/10.1002/ mpr.145. 22. Primack BA. The WHO-5 Wellbeing Index performed the best in screening for depression in primary care. Evid Based Med. 2003;8:155. http://dx.doi.org/10.1136/ebm.8.5.155. 23. EUROSTAT OECD. Eurostat-OECD Methodological Manual on Purchasing Power Parities. Luxembourg: Organisation for Economic Co-operation and Development; 2007. 24. Carle AC. Fitting multilevel models in complex survey data with design weights: Recommendations. BMC Med Res Methodol. 2009;9(1):49. http://dx.doi.org/10.1186/1471-2288-9-49. 25. Ord K, Mitchell R, Pearce J. Is level of neighbourhood green space associated with physical activity in green space? Int J Behav Nutr Phys Act. 2013;10(1):127. http://dx.doi.org/10.1186/1479-5868-10-127. 26. Jones A, Hillsdon M, Coombes E. Greenspace access, use, and physical activity: Understanding the effects of area deprivation. Prev Med. 2009;49(6):500–505. http://dx.doi.org/10.1016/j.ypmed.2009.10.012. 27. Cohen DA, Lapham S, Evenson KR, et al. Use of neighbourhood parks: does socio-economic status matter? A four-city study. Public Health. 2013;127(4):325–332. http://dx.doi.org/10.1016/j.puhe.2013.01.003. 28. Hartig T, Berg AE, Hagerhall CM, et al. Health Benefits of Nature Experience: Psychological, Social and Cultural Processes. In: Nilsson K, Sangster M, Gallis C, et al., eds. Forests, Trees and Human Health. Doredrecht. Springer; 2011:127–168. http://dx.doi.org/10.1007/978-90481-9806-1_5. 29. Stigsdotter UK, Palsdottir AM, Burls A, Chermaz A, Ferrini F, Grahn P. Nature-Based Therapeutic Interventions. In: Nilsson K, Sangster M, Gallis C, et al., eds. Forests, Trees and Human Health. Doredrecht. Springer; 2011:309–342. http://dx.doi.org/10.1007/978-90-481-9806-1_11. 30. Hartig T, Evans GW, Jamner LD, Davis DS, Garling T. Tracking restoration in natural and urban field settings. J Environ Pyschol. 2003;23(2):109–123. http://dx.doi.org/10.1016/S0272-4944(02) 00109-3. 31. Staats H, Hartig T, Kieviets A. Restorative effects of outdoor environments. Int J Psychol. 2000;35(3–4):126. 32. Hartig T, Mang M. Restorative effects of natural environment experiences. Environ Behav. 1991;23(1):3–26. http://dx.doi.org/10. 1177/0013916591231001. 33. Gullón P, Bilal U, Franco M. Physical activity environment measurement and same source bias. Gac Sanit. 2014;28(4):344–345. http://dx. doi.org/10.1016/j.gaceta.2013.12.011. 34. Diez Roux AV. Neighborhoods and health: where are we and were do we go from here? Rev Epidemiol Sante Publique. 2007;55(1):13–21. http://dx.doi.org/10.1016/j.respe.2006.12.003. 35. McGinn A, Evenson K, Herring A, Huston S, Rodriguez D. Exploring associations between physical activity and perceived and objective measures of the built environment. J Urban Health. 2007;84(2):162– 184. http://dx.doi.org/10.1007/s11524-006-9136-4. 36. Bowler DE, Buyung-Ali LM, Knight TM, Pullin AS. A systematic review of evidence for the added benefits to health of exposure to natural environments. BMC Public Health. 2010;10:456. http://dx.doi. org/10.1186/1471-2458-10-456.
Appendix Supplementary data Supplementary data associated with this article can be found at http://dx.doi.org/10.1016/j.amepre.2015.01.017.
www.ajpmonline.org
Introduction
M
ost research on environment and health has focused on the threats to health that some environments make and those threats’ inequitable distribution.1 However, an alternative perspective is emerging, asking whether some environments might protect health and limit socioeconomic health inequalities.2–6 Access to green space is one factor that has received attention, with studies7–14 showing benefits From the Centre for Research on Environment, Society and Health, University of Glasgow, Glasgow (Mitchell); and the Centre for Research on Environment, Society and Health, University of Edinburgh, Edinburgh (Richardson, Shortt, Pearce), United Kingdom Address correspondence to: Richard J. Mitchell, PhD, Centre for Research on Environment, Society and Health, Institute of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, 1 Lilybank Gardens, Glasgow G12 8RZ. E-mail: richard.mitchell@ glasgow.ac.uk. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2015.01.017
80 Am J Prev Med 2015;49(1):80–84
to the physiologic and psychological health of individuals and populations. At least three studies15–17 suggest particular benefit for socioeconomically deprived populations and that green space may therefore narrow health inequalities. However, these did not compare green space with other features of the environment. It is plausible, indeed probable, that neighborhoods with better access to green space also possess other characteristics or services that may promote health. This study is, therefore, the first to ask which neighborhood characteristics or services are associated with narrower socioeconomic health inequalities in a large, international sample of urban residents.
Methods Study Sample The 2012 European Quality of Life Survey (EQLS) was designed to assess influences on quality of life among those aged Z18 years in
& 2015 American Journal of Preventive Medicine
Published by Elsevier Inc.
81
Mitchell et al / Am J Prev Med 2015;49(1):80–84 18
34 European nations. Full sampling and survey details are available elsewhere.19 This analysis, run in 2014, only included respondents living in urban areas (Appendix Table 1).
Table 1. Character and Distribution of Categorical Covariates Included in Regression Analysis Variable/Categories
n (%)
Sex
Measures
Male
Mental well-being was measured using the robust and validated WHO-5,20–22 assessing respondents’ attitude toward life in the 2 weeks prior to interview (Appendix Table 2). Scores range from 0 to 100, where 100 denotes the maximum possible well-being. The primary measure of socioeconomic position (SEP) was perceived financial strain, distinguishing between those able to make ends meet at the end of the month very easily or easily, fairly easily or with some difficulty, and with difficulty or great difficulty. Sensitivity to the use of financial strain was tested by repeating analyses using education level as the primary measure. There were no substantive differences in results. Respondents reported access to five different neighborhood characteristics or services: recreational/green areas, postal services, banking services, public transport, and cultural services (including cinema, theatre, or a cultural center). For each, respondents were asked: Thinking of physical access, distance, opening hours and the like, how would you describe your access to the following service? They chose a response from with great difficulty to very easily, but, importantly, were also able to report not using this particular service (Table 1). Models adjusted for respondents’ reported sex, age, illness limiting daily activities, education level, and employment status. Because better access to neighborhood services might be a proxy for the absence of neighborhood incivilities, models adjusted for reported problems with: noise; air quality; crime, violence, or vandalism; litter or rubbish; and traffic congestion. Models also adjusted for national median household income (equivalized for differences in purchasing power between nations and currencies) in case perception of financial strain was affected by the wealth of wider society.23 Levels of missing data were low, with 1.5% of respondents missing a WHO-5 score, 1.3% missing perceived financial strain, and 5.4% missing any other independent variable. The complete case analysis used a sample size of 21,294 (Table 1). EQLS does not provide weights at both regional and national levels; therefore, analyses were unweighted.24
Female
Statistical Analysis A multilevel linear regression model assessed variation in the relationship between perceived financial strain and mental wellbeing, by reported access to neighborhood services. This was achieved by fitting interaction terms between each service access variable and financial strain, assessing significance using Wald tests. Models were run in Stata, version 12.1, and allowed for clustering of individuals within subnational region and within nation. National median income was modeled as a fixed effect at the national level. For significant interactions, the “margins” command was used to estimate the mean WHO-5 score for each combination of financial strain and reported access to a neighborhood service. This approach accounts for the different distribution of confounders in each group. July 2015
9,159 (43.01) 12,135 (56.99)
Education Primary or less
2,077 (9.75)
Secondary
12,990 (61.0)
Tertiary
6,227 (29.24)
Employment status Employed
10,057 (47.23)
Unemployed
1,632 (7.66)
Unable to work
431 (2.02)
Retired
5,704 (26.79)
Homemaker
1,713 (8.04)
Student
1,522 (7.15)
Other
235 (1.10)
Limiting illness Yes, severely limited
1,379 (6.48)
Yes, limited to some extent
3,072 (14.43)
Yes, but not limited
1,537 (7.22)
No illness
15,306 (71.88)
Financial strain (ability to make ends meet) Very easily/easily Fairly easily / some difficulty With difficulty / great difficulty
5,295 (24.87) 12,280 (57.67) 3,719 (17.47)
Problems in the immediate neighborhood Noise Major problems
2,043 (9.59)
Moderate problems
6,219 (29.21)
No problems
13,032 (61.2)
Air quality Major problems
1,892 (8.89)
Moderate problems
5,553 (26.08)
No problems
13,849 (65.04)
Crime, violence, or vandalism Major problems Moderate problems
1,797 (8.44) 6,424 (30.17) (continued on next page)
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Mitchell et al / Am J Prev Med 2015;49(1):80–84
Table 1. Character and Distribution of Categorical Covariates Included in Regression Analysis (continued) Variable/Categories
n (%)
No problems
13,073 (61.39)
Litter or rubbish on the street
Table 1. (continued) Variable/Categories
n (%)
With some difficulty
2,004 (9.41)
Easily
9,352 (43.92)
Major problems
2,258 (10.6)
Very easily
7,794 (36.60)
Moderate problems
6,338 (29.76)
Service not used
1,525 (7.16)
No problems
12,698 (59.63)
Traffic congestion Major problems
2,706 (12.71)
Results
Moderate problems
5,791 (27.20)
Reported access to recreational/green areas was the only neighborhood service to have a significant interaction with financial strain in its relationship with mental wellbeing. Inequality in mental well-being was narrower among those reporting better access to recreational/green areas (chi-squared¼16.08, p¼0.041) (Appendix Table 3). Figure 1 illustrates this in two ways. First, it shows that the difference in estimated WHO-5 scores between those under most and least financial strain fell successively as reported access to green/recreational areas improved. The gap was 8.1 points, or 40%, smaller among those reporting best access than among those reporting worst access. Second, sloped lines compare gradients in mental well-being among different levels of reported access to recreational/green areas; the gradient is shallower where reported access is better. Inequality in mental well-being among those who did not use their recreational/green space was at about the same level as those who reported some difficulty with access.
No problems
12,797 (60.10)
Access to neighborhood services/facilities Postal service With great difficulty
596 (2.80)
With some difficulty
2,332 (10.95)
Easily
10,003 (46.98)
Very easily
7,359 (34.56)
Service not used
1,004 (4.71)
Banking service With great difficulty
501 (2.35)
With some difficulty
2,150 (10.10)
Easily Very easily Service not used
10,155 (47.69) 7,756 (36.42) 732 (3.44)
Public transport With great difficulty
744 (3.49)
With some difficulty
1,969 (9.25)
Easily
8,653 (40.64)
Very easily
7,258 (34.08)
Service not used
2,670 (12.54)
Cinema, theatre, or cultural center With great difficulty
958 (4.50)
With some difficulty
2,732 (12.83)
Easily
8,932 (41.95)
Very easily
5,389 (25.31)
Service not used
3,283 (15.42)
Recreational/green areas With great difficulty
619 (2.91) (continued)
Discussion Socioeconomic inequalities in mental well-being were smaller among urban dwellers reporting good access to recreational/green areas, compared with those reporting difficulty with access. There was no such difference for the other tested neighborhood services. The frequency or type of use of recreational/green areas was not reported. Although it is plausible that those experiencing financial strain who reported easier access use their areas more, or in ways that have greater benefits for well-being, the existing literature25–27 does not support this hypothesis. Alternatively, perhaps the beneficial effects of using recreational/green areas are stronger for those under greater levels of stress. Many experiments have identified that contact with nature can offer psychological restoration to those who are stressed or fatigued.28–32 Perhaps those with no financial strain are less in need of restoration, or have already maximized www.ajpmonline.org
Mitchell et al / Am J Prev Med 2015;49(1):80–84
83
Figure 1. Predicted mean mental well-being by perceived financial strain and ease of access to recreational/green areas.
their well-being. Experimental studies14 have not explored variation in restorative effect by SEP, and this requires further research. Strengths of this study included individual-level data; control for important influences on mental well-being; a large, international sample; a robust measure of mental well-being; control for other neighborhood incivilities; and, importantly, comparison of several neighborhood services, with identification of those not using each service. Limitations included self-reported neighborhood characteristics and mental well-being outcome, raising the possibility of same-source bias.33,34 Objectively measured neighborhood characteristics may show different associations with health.35 The term recreational/green areas was non-specific, though it is likely the word areas would discourage respondents from thinking of indoor facilities, such as gyms. The interpretation of results assumes these areas provided access to nature, but recreational areas could include those in which contact with nature is minimal. Although the cross-sectional design of this large, international study cannot prove causality, there are at least two reasons why a causal effect is plausible: the narrowing of inequality did not occur among those not using their recreational/green areas, and experimental studies5,10,14,36 have proven that contact with nature can cause improved mental well-being.
July 2015
Conclusions Public health professionals have campaigned for more equal distribution of power and resources to reduce health inequalities. Yet, a radically more equal world currently looks unlikely. While this campaign continues, additional ideas are needed. Environments that act to disrupt the usual conversion of socioeconomic adversity to a greater risk of poor health have been labeled “equigenic,” which may include recreational/green areas.6 Identifying and testing other equigenic characteristics should be a priority. We are grateful to Frank Popham for comments on an earlier draft. We are indebted to the Eurofound team for collecting and curating the European Quality of Life survey. This work was supported by the European Research Council (ERC-2010StG Grant 263501). The funder had no role at all in study design, analysis, interpretation, writing, or the decision to submit. RM designed the study, carried out the analyses, and wrote the first draft. JP, NS, and ER contributed to the concept and design of the study, and to writing subsequent and final drafts. RM is the guarantor. No financial disclosures were reported by the authors of this paper.
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Appendix Supplementary data Supplementary data associated with this article can be found at http://dx.doi.org/10.1016/j.amepre.2015.01.017.
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