Introduction

In 2008, the World Health Organization’s pioneering Commission on Social Determinants of Health measured the extent of health inequalities in the city of Glasgow, Scotland, as an example. The final report revealed that a boy in the deprived area of Calton had an average life expectancy of 54 years compared with a boy in the affluent suburb of Lenzie, only 12 km away, who could expect to live to an age of 82 years (CSDH, 2008): a gap of 28 years. Michael Marmot, chair of the World Health Organization Commission, later reflected in his book The health gap that this “was a tale of two cities ... and they are both in Glasgow” (Marmot, 2015). This is a most basic, fundamental, and stark example of measuring health inequalities. The authors of this chapter all work in Glasgow, and are driven by the aim of tackling this inequality.

Measures of health inequality, which are determined by inequalities in income, wealth, and power (WHO, 2010), are a reflection of the levels of justice and fairness in society. The measurement of inequalities in health is essential to define, describe, and understand the nature of the public health problem; it is a crucial first step in the development of strategies and policies to tackle health inequalities, and in the monitoring and evaluation of the effectiveness of approaches.

Socioeconomic inequalities can exist both within and between countries (as detailed in Chapters 5 and 6), and they have an impact across the cancer continuum, from burden and risk, to early detection, diagnosis, and treatment (see Chapter 7), and to outcomes including quality of life, mortality, and survival. Inequalities also exist across other population groups and communities (defined by sex, age, race or ethnicity, geographical area, time periods, and health status, for example), although socioeconomic status (SES) is often a major factor in these differences (United Kingdom Government, 2010; Krieger, 2014). This chapter describes two key aspects of measurement and analysis of SES in relation to cancer: indicators of SES, and metrics of health inequality between socioeconomic strata.

Indicators of SES

SES or socioeconomic position is a theoretical construct of socioeconomic hierarchies with roots in the social theories of Weber and Marx (Lynch and Kaplan, 2000). SES is conceptualized through indicators or measures collected at the individual or area-based level. In social epidemiology, these indicators are used to capture and analyse the impacts of social determinants of health (Glymour et al., 2014). Several comprehensive reviews of SES indicators have discussed in detail the strengths and weaknesses of the different approaches to measurement (e.g. Liberatos et al., 1988; Berkman and Macintyre, 1997; Krieger, 2001; Galobardes et al., 2006a, b; Glymour et al., 2014). Drawing from and expanding on these reviews, we summarize individual indicators of SES, including examples of indicators and notes on their interpretation, in Table 4.1.

Table 4.1

Individual indicators of socioeconomic status: their measurement and interpretation.

SES indicators have typically included individual measures of income, education, and occupational social class, and these measures have formed the mainstay of cancer socioeconomic analyses in relation to health and disease outcomes. Education level, followed by occupational social class and income, is widely used in analytical epidemiology investigating individual SES risk associations with cancer. Such associations are evident in the systematic reviews and meta-analyses of case–control and/or cohort studies across many cancer sites, including oral cavity (Conway et al., 2008), lung (Sidorchuk et al., 2009), stomach (Uthman et al., 2013), colon and rectum (Manser and Bauerfeind, 2014), head and neck (Conway et al., 2015), and breast (Lundqvist et al., 2016). These analyses sought to both quantify the risk association of SES with cancer, and assess whether such associations are attenuated (explained) by behavioural risk factors. Measures of wealth have increasingly been a focus of health inequality studies, but have received limited attention in relation to cancer (Pollack et al., 2007).

Area-based socioeconomic indicators are summarized in Table 4.2. These indicators are frequently used in descriptive epidemiological analyses of cancer registry data at the state or regional level (Harper and Lynch, 2005) or country level (Purkayastha et al., 2016), often to overcome the lack of individual-level SES data. At the global level, IARC is leading the field in the assessment of the burden of cancer using sophisticated measures that go beyond categorizations of developing versus developed countries, by using more sophisticated measures of development including the Human Development Index (Bray et al., 2012; Arnold et al., 2016). There are concerns that interpretations of the association between area-based indicators and health outcomes are prone to the ecological fallacy, where all individuals living in an area are assigned an SES based on the characteristics of that particular area. However, people living in the same area will share many of the socioeconomic environmental circumstances that have an impact on health apart from (or over and above) individual SES factors (Berkman and Macintyre, 1997).

Table 4.2

Area-based socioeconomic indicators: their measurement and interpretation.

Within- and between-country inequalities in cancer mortality have also been regularly examined in Europe via educational attainment (Menvielle et al., 2008); see Chapter 6 for further details.

Recent studies have begun to investigate the contribution of multiple measures of SES to inequalities in cancer; this demonstrates the interconnectedness of different SES measures (Spadea et al., 2010; Sharpe et al., 2014). These studies highlight both the independent effects of different SES measures and the further elevated risk associations observed with combinations of SES indicators, for example, low educational attainment or living in a deprived community.

Sociodemographic factors

In addition to the dominant effect of socioeconomic inequalities, there are also important inequalities related to other population demographic groups. These factors, summarized in Table 4.3, are also known as equality domains and have their origins in human rights (United Nations, 2018). The concept of the interrelationship between these various measures of social stratification and SES was named “intersectionality” by Crenshaw (1991), who proposed a theoretical framework for analysing the combined effects of multiple social categories.

Table 4.3

Sociodemographic factors: their measurement and interpretation.

The future of SES measurement

The cancer epidemiology scientific community has been challenged to characterize the exposome, which encompasses individual environmental exposures across the life-course from as early as the prenatal period, using similar conceptual approaches and level of rigour as those taken to map and study the genome (Wild, 2005). The concept has been refined and updated with a general external environment domain that includes SES factors: “social capital, education, financial status, psychological and mental stress, urban–rural environment, [and] climate” (Wild, 2012). More recently, a “socio-exposome” has been proposed to capture the wide range of socioeconomic environments and influences (Senier et al., 2017). Socioeconomic measures and exposures need to be better and more comprehensively captured, so that cancer risks associated with socioeconomic factors and their interactions with physical environmental exposures and genetics can be investigated.

Analysis approaches

Risk association analyses between SES and health or disease (including cancer) are so well accepted that it is very unusual to investigate risk factors without adjusting for SES (Berkman and Macintyre, 1997). However, with the growing discipline of social epidemiology during the past decades, studies have increasingly reversed this logic and focused on socioeconomic factors as risk factors (Kawachi and Subramanian, 2018). Typically, analyses take the highest SES strata as the referent category and quantify the relative risk associations in lower SES strata, quantifying inequalities between groups with different SES. These analyses can be influenced by the number and size of SES strata. Reducing the size (and increasing the number) of strata will increase the extent of inequalities observed between the extreme groups, with smaller strata implying more extreme social groupings. More sophisticated analyses of inequalities that make adjustments for such changes are therefore required (see the section on “Metrics of health inequalities” below).

Life-course analysis takes advantage of the ever-present and potentially changing socioeconomic circumstances at all stages of the life-course from birth (or in utero) to death (Ben Shlomo and Kuh, 2002; see also Chapter 12 for a detailed description); SES is therefore a time-varying exposure, and combinations of SES measures at different times across the life-course can be used. Insights from life-course epidemiology have highlighted the limitation of capturing SES on the basis of data collected at a single time point in terms of investigating socioeconomic risk as well as adjusting for SES confounding in analyses. Although recognition of this limitation represents progress, investigating and disentangling all the processes occurring over a life-course (e.g. critical periods, social mobility, cumulative effects, or combinations of these) is challenging (Hallqvist et al. 2004). Much attention has been given to the effect of adverse SES in childhood and early life on the occurrence of disease in adulthood and later life, independent of adult SES; for example, children who experience conditions of overcrowding or poor hygiene are more likely to become infected with Helicobacter pylori, which increases the risk of stomach cancer in later life (Stemmermann and Fenoglio-Preiser, 2002). A few studies have investigated the association between cancer risk and social mobility (Schmeisser et al., 2010; Behrens et al., 2016), and the cumulative effects of SES across the life-course are increasingly recognized as being associated with disease outcomes (Ben-Shlomo and Kuh, 2002).

Multilevel analysis is a form of regression analysis that takes into account the natural clustering of one unit of analysis (such as the individual) within another (such as the area of residence), and can be used to distinguish between contextual (macro) and compositional (micro) influences (Diez Roux, 2002). Even in the absence of any interest in contextual influences, multilevel analysis enables us to correct for the lack of independence between observations at the micro (e.g. individual) level. In assessing inequalities in health, multilevel analysis can be used to estimate a variance indicative of the size of inequality between areas or over time (Leyland, 2004), or to provide an appropriate estimate of a contextual effect, such as an indicator of area deprivation (Krieger et al., 2003).

Metrics of health inequalities

Numerous measures have been proposed as a means of measuring inequalities in health, whether in relation to cancer specifically (Harper and Lynch, 2005) or to health in general (Regidor, 2004a, b; Wagstaff and van Doorslaer, 2004; Blair et al., 2013). Aside from the longstanding debate about whether more emphasis should be placed on absolute or relative measures of inequality (Asada, 2010; King et al., 2012; Mackenbach, 2015), there is a realization that the actual metric chosen can influence the inequality observed and hence the monitoring of changes in inequality; as such, the choice of a measure or measures represents a value judgement (Harper et al., 2010; Kjellsson et al., 2015). For a detailed discussion of interpretation of measures of inequalities, see Chapter 14.

In Table 4.4 we present the definition of some of the most commonly used measures of inequality along with their strengths and limitations; see Munoz-Arroyo and Sutton (2007) for further information on some of these measures. We also present the value and interpretation based on data for cancer mortality of men aged 50–59 years across quintiles of area deprivation in Scotland between 2012 and 2016 (the underlying data are shown in Table 4.5).

Table 4.4

Measures of inequality applied to the example of cancer mortality of men aged 50–59 years in Scotland between 2012 and 2016 across quintiles of area deprivation.

Table 4.5

Number of deaths from all cancers of men aged 50–59 years in Scotland between 2012 and 2016, population estimates (2014), and age-standardized mortality rate by quintile of area deprivation.

The complex methods shown in Table 4.4 have the advantage of taking into account all available information from the different groups and their SES. Although simpler measures do have their place, they do not represent the entire picture (Mackenbach and Kunst, 1997). The slope index of inequality (SII) and relative index of inequality (RII) compare the notionally most deprived individuals with the least deprived individuals in the population on an absolute and relative scale, respectively. Different methods have been proposed for calculation of SII, based on either a linear regression of the age-standardized rates weighted by the size of the socioeconomic groups (Pamuk, 1985) or on an additive Poisson regression of the number of deaths (Moreno-Betancur et al., 2015). These different methods produce results of similar magnitude and have the same interpretation; both estimate the absolute difference between the extremes of the distribution. RII can be estimated based on the linear regression model by dividing SII by the population rate (Pamuk, 1985), or through a (standard) multiplicative Poisson regression of the number of deaths (Moreno-Betancur et al., 2015). These methods (denoted RII_L and RII_P, respectively) provide different results with different interpretations as indicated in Table 4.4. The two indices are approximately related by the equation RII_P ≈ (2 + RII_L)/(2 − RII_L). When individual time-to-event data are available, SII and RII_P can be calculated using an additive hazards model and a Cox model, respectively (Moreno-Betancur et al., 2015). Despite having different interpretations, the health concentration index (HCI) and SII are approximately equivalent except for the presence of a multiplicative constant (Lumme et al., 2012). The estimate of the required redistribution of mortality across groups is obtained by multiplying the absolute value of HCI by 75 (Koolman and van Doorslaer, 2004).

SII and RII_L are decomposable; as such, they easily lend themselves to visualizations of inequalities and, in particular, to an investigation of the contribution of different causes to socioeconomic inequalities (Leyland et al., 2007). Examples of inequalities in cancer mortality by area-based SES in Scotland are presented in Fig. 4.1. The overall shapes of the graphs illustrate the inequality in all-cancer mortality. The widths of the different bands show the extent to which inequalities in cancer mortality are attributable to specific cancers. SII tends to be lowest for younger ages and increases for older ages, for which death rates are higher. Absolute inequalities are highest at the age of about 80–85 years for men (absolute rate difference of 1166 per 100 000 population between the most and least deprived) and about 75–80 years for women (absolute rate difference of 683 per 100 000 population). The RII_L peaks earlier, at the age of about 50–55 years for men (RII_L, 1.21) and 60–65 years for women (RII_L, 0.96). At younger ages, relative inequalities in cancer mortality tend to be due to causes such as colorectal cancer, brain cancer, myeloma and leukaemia, whereas at ages 40 years and older, the largest contribution to relative inequalities in cancer mortality is from lung cancer.

Fig. 4.1

Contribution of specific cancers to absolute and relative inequalities in all cancers. (a) SII and (b) RII_L for men aged 15–90 years in Scotland between 2012 and 2016. (c) SII and (d) RII_L for women aged (more...)

Conclusions

Measuring SES and social inequalities is essential to understanding the risk, burden, and impact of socioeconomic factors on cancer. Several indicators have been developed to capture SES, and sophisticated analytical methods have been developed to measure inequalities between socioeconomic strata. In line with the Commission on Social Determinants (CSDH, 2008), it is recommended that social inequalities in cancer be measured and monitored using both absolute and relative measures, ideally using both (or multiple) individual and area-based SES indicators. Improvements in data linkage will facilitate the assignment of SES indicators to cancer registry data. Finally, an improved understanding of the effect of socioeconomic factors on the burden of cancer will enable cancer control strategies to be better targeted at populations, communities, and individuals.

Box

Key points.

References

• Arnold M, Rentería E, Conway DI, Bray F, Van Ourti T, Soerjomataram I (2016). Inequalities in cancer incidence and mortality across medium to highly developed countries in the twenty-first century. Cancer Causes Control. 27(8):999–1007. 10.1007/s10552-016-0777-7 [PubMed: 27329211] [CrossRef]
• Asada Y (2010). On the choice of absolute or relative inequality measures. Milbank Q. 88(4):616–22, discussion 623–7. 10.1111/j.1468-0009.2010.00614.x [PMC free article: PMC3037178] [PubMed: 21166871] [CrossRef]
• Behrens T, Groß I, Siemiatycki J, Conway DI, Olsson A, Stücker I, et al. (2016). Occupational prestige, social mobility and the association with lung cancer in men. BMC Cancer. 16(1):395. 10.1186/s12885-016-2432-9 [PMC free article: PMC4936282] [PubMed: 27388894] [CrossRef]
• Ben-Shlomo Y, Kuh D (2002). A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol. 31(2):285–93. 10.1093/intjepid/31.2.285 [PubMed: 11980781] [CrossRef]
• Berkman LF, Macintyre S (1997). The measurement of social class in health studies: old measures and new formulations. In: Kogevinas M, Pearce N, Susser M, Boffetta P, editors. Social inequalities and cancer. Lyon, France: International Agency for Research on Cancer. IARC Scientific Publications. Volume 138. pp. 51–64. Available from: http://publications​.iarc.fr/293.
• Blair YI, McMahon AD, Macpherson LM (2013). Comparison and relative utility of inequality measurements: as applied to Scotland’s child dental health. PLoS One. 8(3):e58593. 10.1371/journal.pone.0058593 [PMC free article: PMC3592808] [PubMed: 23520524] [CrossRef]
• Bray F, Jemal A, Grey N, Ferlay J, Forman D (2012). Global cancer transitions according to the Human Development Index (2008-2030): a population-based study. Lancet Oncol. 13(8):790–801. 10.1016/S1470-2045(12)70211-5 [PubMed: 22658655] [CrossRef]
• Carstairs V, Morris R (1989). Deprivation and mortality: an alternative to social class? Community Med. 11:210–9. [PubMed: 2605887]
• Conway DI, Brenner DR, McMahon AD, Macpherson LM, Agudo A, Ahrens W, et al. (2015). Estimating and explaining the effect of education and income on head and neck cancer risk: INHANCE consortium pooled analysis of 31 case-control studies from 27 countries. Int J Cancer. 136(5):1125–39. 10.1002/ijc.29063 [PMC free article: PMC4531373] [PubMed: 24996155] [CrossRef]
• Conway DI, Petticrew M, Marlborough H, Berthiller J, Hashibe M, Macpherson LM (2008). Socioeconomic inequalities and oral cancer risk: a systematic review and meta-analysis of case-control studies. Int J Cancer. 122(12):2811–9. 10.1002/ijc.23430 [PubMed: 18351646] [CrossRef]
• Cordier R, Milbourn B, Martin R, Buchanan A, Chung D, Speyer R (2017). A systematic review evaluating the psychometric properties of measures of social inclusion. PLoS One. 12(6):e0179109. 10.1371/journal.pone.0179109 [PMC free article: PMC5466312] [PubMed: 28598984] [CrossRef]
• Crenshaw KW (1991). Mapping the margins: intersectionality, identity politics, and violence against women of color. Stanford Law Rev. 43(6):1241–99. 10.2307/1229039 [CrossRef]
• CSDH (2008). Closing the gap in a generation: health equity through action on the social determinants of health. Final Report of the Commission on Social Determinants of Health. Geneva, Switzerland: World Health Organization. Available from: https://www​.who.int/social_determinants​/thecommission​/finalreport/en/.
• Diez Roux AV (2002). A glossary for multilevel analysis. J Epidemiol Community Health. 56(8):588–94. 10.1136/jech.56.8.588 [PMC free article: PMC1732212] [PubMed: 12118049] [CrossRef]
• Erikson R, Goldthorpe JH (1992). The constant flux. Oxford, UK: Clarendon Press.
• Galobardes B, Shaw M, Lawlor DA, Lynch JW, Smith GD (2006a). Indicators of socioeconomic position (part 1). J Epidemiol Community Health. 60(1):7–12. 10.1136/jech.2004.023531 [PMC free article: PMC2465546] [PubMed: 16361448] [CrossRef]
• Galobardes B, Shaw M, Lawlor DA, Lynch JW, Smith GD (2006b). Indicators of socioeconomic position (part 2). J Epidemiol Community Health. 60(2):95–101. 10.1136/jech.2004.028092 [PMC free article: PMC2566160] [PubMed: 16415256] [CrossRef]
• Glymour MM, Avendano M, Kawachi I (2014). Socioeconomic status and health. In: Berkman LF, Kawachi I, Glymour MM, editors. Social epidemiology. 2nd ed. New York, NY, USA: Oxford University Press; .10.1093/med/9780195377903.003.0002 [CrossRef]
• Hallqvist J, Lynch J, Bartley M, Lang T, Blane D (2004). Can we disentangle life course processes of accumulation, critical period and social mobility? An analysis of disadvantaged socio-economic positions and myocardial infarction in the Stockholm Heart Epidemiology Program. Soc Sci Med. 58(8):1555–62. 10.1016/S0277-9536(03)00344-7 [PubMed: 14759698] [CrossRef]
• Harper S, King NB, Meersman SC, Reichman ME, Breen N, Lynch J (2010). Implicit value judgments in the measurement of health inequalities. Milbank Q. 88(1):4–29. 10.1111/j.1468-0009.2010.00587.x [PMC free article: PMC2888011] [PubMed: 20377756] [CrossRef]
• Harper S, Lynch J (2005). Methods for measuring cancer disparities: using data relevant to healthy people 2010 cancer-related objectives. NCI Cancer Surveillance Monograph Series, Number 6. Bethesda (MD), USA: National Cancer Institute. NIH Publication No. 05–5777.
• Kawachi I, Subramanian SV (2018). Social epidemiology for the 21st century. Soc Sci Med. 196:240–5. 10.1016/j.socscimed.2017.10.034 [PubMed: 29113687] [CrossRef]
• King NB, Harper S, Young ME (2012). Use of relative and absolute effect measures in reporting health inequalities: structured review. BMJ. 345:e5774. 10.1136/bmj.e5774 [PMC free article: PMC3432634] [PubMed: 22945952] [CrossRef]
• Kjellsson G, Gerdtham U-G, Petrie D (2015). Lies, damned lies, and health inequalities measurements: understanding the value judgements. Epidemiology. 26(5):673–80. 10.1097/EDE.0000000000000319 [PMC free article: PMC4521896] [PubMed: 26133019] [CrossRef]
• Koolman X, van Doorslaer E (2004). On the interpretation of a concentration index of inequality. Health Econ. 13(7):649–56. 10.1002/hec.884 [PubMed: 15259044] [CrossRef]
• Krieger N (2001). A glossary for social epidemiology. J Epidemiol Community Health. 55(10):693–700. 10.1136/jech.55.10.693 [PMC free article: PMC1731785] [PubMed: 11553651] [CrossRef]
• Krieger N (2014). Discrimination and health inequalities. In: Berkman LF, Kawachi I, Glymour MM, editors. Social epidemiology. 2nd ed. New York, NY, USA: Oxford University Press; .10.1093/med/9780195377903.003.0003 [CrossRef]
• Krieger N, Chen JT, Waterman PD, Rehkopf DH, Subramanian SV (2003). Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures – the Public Health Disparities Geocoding Project. Am J Public Health. 93(10):1655–71. 10.2105/AJPH.93.10.1655 [PMC free article: PMC1448030] [PubMed: 14534218] [CrossRef]
• Leyland AH (2004). Increasing inequalities in premature mortality in Great Britain. J Epidemiol Community Health. 58(4):296–302. 10.1136/jech.2003.007278 [PMC free article: PMC1732731] [PubMed: 15026442] [CrossRef]
• Leyland AH, Dundas R, McLoone P, Boddy FA (2007). Inequalities in mortality in Scotland, 1981–2001. Glasgow, Scotland: MRC Social and Public Health Sciences Unit. Available from: http://www​.inequalitiesinhealth.com/.
• Liberatos P, Link BG, Kelsey JL (1988). The measurement of social class in epidemiology. Epidemiol Rev. 10(1):87–121. 10.1093/oxfordjournals.epirev.a036030 [PubMed: 3066632] [CrossRef]
• Lombardi C, Bronfman M, Facchini LA, Victora CG, Barros FC, Béria JU, Teixeira AM (1988). Operationalization of the concept of social class in epidemiologic studies [Operacionalização do conceito de classe social em estudos epidemiológicos]. Rev Saude Publica. 22(4):253–65. 10.1590/S0034-89101988000400001 [PubMed: 3241948] [CrossRef]
• Lumme S, Sund R, Leyland AH, Keskimäki I (2012). Socioeconomic equity in amenable mortality in Finland 1992-2008. Soc Sci Med. 75(5):905–13. 10.1016/j.socscimed.2012.04.007 [PubMed: 22647563] [CrossRef]
• Lundqvist A, Andersson E, Ahlberg I, Nilbert M, Gerdtham U (2016). Socioeconomic inequalities in breast cancer incidence and mortality in Europe – a systematic review and meta-analysis. Eur J Public Health. 26(5):804–13. 10.1093/eurpub/ckw070 [PMC free article: PMC5054273] [PubMed: 27221607] [CrossRef]
• Lynch J, Kaplan G (2000). Socioeconomic position. In: Berkman LF, Kawachi I, editors. Social epidemiology. New York, NY, USA: Oxford University Press.
• Mackenbach JP (2015). Should we aim to reduce relative or absolute inequalities in mortality? Eur J Public Health. 25(2):185. 10.1093/eurpub/cku217 [PubMed: 25818489] [CrossRef]
• Mackenbach JP, Kunst AE (1997). Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med. 44(6):757–71. 10.1016/S0277-9536(96)00073-1 [PubMed: 9080560] [CrossRef]
• Manser CN, Bauerfeind P (2014). Impact of socioeconomic status on incidence, mortality, and survival of colorectal cancer patients: a systematic review. Gastrointest Endosc. 80(1):42–60.e9. 10.1016/j.gie.2014.03.011 [PubMed: 24950641] [CrossRef]
• Marmot M (2015). The health gap: the challenge of an unequal world. London, UK: Bloomsbury. [PubMed: 26364261]
• Menvielle G, Kunst AE, Stirbu I, Strand BH, Borrell C, Regidor E, et al. (2008). Educational differences in cancer mortality among women and men: a gender pattern that differs across Europe. Br J Cancer. 98(5):1012–9. 10.1038/sj.bjc.6604274 [PMC free article: PMC2266850] [PubMed: 18283307] [CrossRef]
• Moreno-Betancur M, Latouche A, Menvielle G, Kunst AE, Rey G (2015). Relative index of inequality and slope index of inequality: a structured regression framework for estimation. Epidemiology. 26(4):518–27. 10.1097/EDE.0000000000000311 [PubMed: 26000548] [CrossRef]
• Munoz-Arroyo R, Sutton M (2007). Measuring socio-economic inequalities in health: a practical guide. Scottish Public Health Observatory. Available from: https://pdfs​.semanticscholar​.org/2b3a/cd72bde75db4d876a5d5e5e1ca56c45200d9.pdf.
• Pamuk ER (1985). Social class inequality in mortality from 1921 to 1972 in England and Wales. Popul Stud (Camb). 39(1):17–31. 10.1080/0032472031000141256 [PubMed: 11611750] [CrossRef]
• Pollack CE, Chideya S, Cubbin C, Williams B, Dekker M, Braveman P (2007). Should health studies measure wealth? A systematic review. Am J Prev Med. 33(3):250–64. 10.1016/j.amepre.2007.04.033 [PubMed: 17826585] [CrossRef]
• Purkayastha M, McMahon AD, Gibson J, Conway DI (2016). Trends of oral cavity, oropharyngeal and laryngeal cancer incidence in Scotland (1975-2012) – a socioeconomic perspective. Oral Oncol. 61:70–5. 10.1016/j.oraloncology.2016.08.015 [PubMed: 27688107] [CrossRef]
• Regidor E (2004a). Measures of health inequalities: part 1. J Epidemiol Community Health. 58(10):858–61. 10.1136/jech.2003.015347 [PMC free article: PMC1763348] [PubMed: 15365113] [CrossRef]
• Regidor E (2004b). Measures of health inequalities: part 2. J Epidemiol Community Health. 58(11):900–3. 10.1136/jech.2004.023036 [PMC free article: PMC1732621] [PubMed: 15483304] [CrossRef]
• Schmeisser N, Conway DI, McKinney PA, McMahon AD, Pohlabeln H, Marron M, et al. (2010). Life course social mobility and risk of upper aerodigestive tract cancer in men. Eur J Epidemiol. 25(3):173–82. 10.1007/s10654-010-9429-5 [PubMed: 20143252] [CrossRef]
• Scottish Government (2016). Introducing the Scottish Index of Multiple Deprivation 2016. Available from: https://www​.gov.scot​/Resource/0050/00504809.pdf.
• Senier L, Brown P, Shostak S, Hanna B (2017). The socio-exposome: advancing exposure science and environmental justice in a post-genomic era. Environ Sociol. 3(2):107–21. 10.1080/23251042.2016.1220848 [PMC free article: PMC5604315] [PubMed: 28944245] [CrossRef]
• Sharpe KH, McMahon AD, Raab GM, Brewster DH, Conway DI (2014). Association between socioeconomic factors and cancer risk: a population cohort study in Scotland (1991-2006). PLoS One. 9(2):e89513. 10.1371/journal.pone.0089513 [PMC free article: PMC3937337] [PubMed: 24586838] [CrossRef]
• Sidorchuk A, Agardh EE, Aremu O, Hallqvist J, Allebeck P, Moradi T (2009). Socioeconomic differences in lung cancer incidence: a systematic review and meta-analysis. Cancer Causes Control. 20(4):459–71. 10.1007/s10552-009-9300-8 [PubMed: 19184626] [CrossRef]
• Spadea T, Zengarini N, Kunst A, Zanetti R, Rosso S, Costa G (2010). Cancer risk in relationship to different indicators of adult socioeconomic position in Turin, Italy. Cancer Causes Control. 21(7):1117–30. 10.1007/s10552-010-9539-0 [PubMed: 20349125] [CrossRef]
• Stemmermann GN, Fenoglio-Preiser C (2002). Gastric carcinoma distal to the cardia: a review of the epidemiological pathology of the precursors to a preventable cancer. Pathology. 34(6):494–503. 10.1016/S0031-3025(17)30697-9 [PubMed: 12555987] [CrossRef]
• Townsend P, Phillimore P, Beattie A (1988). Health and deprivation: inequality in the North. London, UK: Croom Helm.
• United Kingdom Government (2010). Equality Act 2010: guidance (updated 2013 and 2015). London, UK: UK Government. Available from: https://www​.gov.uk/guidance​/equality-act-2010-guidance.
• United Nations (2018). The universal declaration of human rights. Available from: http://www​.un.org/en​/universal-declaration-human-rights/.
• Uthman OA, Jadidi E, Moradi T (2013). Socioeconomic position and incidence of gastric cancer: a systematic review and meta-analysis. J Epidemiol Community Health. 67(10):854–60. 10.1136/jech-2012-201108 [PubMed: 23929615] [CrossRef]
• Wagstaff A, van Doorslaer E (2004). Overall versus socioeconomic health inequality: a measurement framework and two empirical illustrations. Health Econ. 13(3):297–301. 10.1002/hec.822 [PubMed: 14981654] [CrossRef]
• WHO (2010). A conceptual framework for action on the social determinants of health. Geneva, Switzerland: World Health Organization. Available from: http://www​.who.int/social_determinants​/publications​/9789241500852/en/.
• Wild CP (2005). Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev. 14(8):1847–50. 10.1158/1055-9965.EPI-05-0456 [PubMed: 16103423] [CrossRef]
• Wild CP (2012). The exposome: from concept to utility. Int J Epidemiol. 41(1):24–32. 10.1093/ije/dyr236 [PubMed: 22296988] [CrossRef]
• Wright EO (1997). Class counts. Comparability studies in class analysis. Cambridge, UK: Cambridge University Press.