Using latent class analysis to develop a model of the relationship between socioeconomic position and ethnicity: cross-sectional analyses from a multi-ethnic birth cohort study

BMC Public Health

John Wright⁵  Kate E Pickett³  Debbie A Lawlor²  Emily S Petherick⁵  Neil Small¹  Baltica Cabieses⁴  Lesley Fairley⁵ 

[1] School of Health Studies, University of Bradford, Bradford, UK;MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK;Department of Health Sciences, University of York, York, UK;Faculty of Medicine, Universidad del Desarrollo, Santiago, Chile;Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK

关键词: Born in Bradford;    Latent class analysis;    Ethnicity;    Socioeconomic position;   

Others  :  1128644
DOI  :  10.1186/1471-2458-14-835

 received in 2013-11-19, accepted in 2014-07-31,  发布年份 2014

【 摘 要 】

Background

Almost all studies in health research control or investigate socioeconomic position (SEP) as exposure or confounder. Different measures of SEP capture different aspects of the underlying construct, so efficient methodologies to combine them are needed. SEP and ethnicity are strongly associated, however not all measures of SEP may be appropriate for all ethnic groups.

Methods

We used latent class analysis (LCA) to define subgroups of women with similar SEP profiles using 19 measures of SEP. Data from 11,326 women were used, from eight different ethnic groups but with the majority from White British (40%) or Pakistani (45%) backgrounds, who were recruited during pregnancy to the Born in Bradford birth cohort study.

Results

Five distinct SEP subclasses were identified in the LCA: (i) "Least socioeconomically deprived and most educated" (20%); (ii) "Employed and not materially deprived" (19%); (iii) "Employed and no access to money" (16%); (iv) "Benefits and not materially deprived" (29%) and (v) "Most economically deprived" (16%). Based on the magnitude of the point estimates, the strongest associations were that compared to White British women, Pakistani and Bangladeshi women were more likely to belong to groups: (iv) "benefits and not materially deprived" (relative risk ratio (95% CI): 5.24 (4.44, 6.19) and 3.44 (2.37, 5.00), respectively) or (v) most deprived group (2.36 (1.96, 2.84) and 3.35 (2.21, 5.06) respectively) compared to the least deprived class. White Other women were more than twice as likely to be in the (iv) "benefits and not materially deprived group" compared to White British women and all ethnic groups, other than the Mixed group, were less likely to be in the (iii) "employed and not materially deprived" group than White British women.

Conclusions

LCA allows different aspects of an individual’s SEP to be considered in one multidimensional indicator, which can then be integrated in epidemiological analyses. Ethnicity is strongly associated with these identified subgroups. Findings from this study suggest a careful use of SEP measures in health research, especially when looking at different ethnic groups. Further replication of these findings is needed in other populations.

【 授权许可】

   
2014 Fairley et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Krieger N: A glossary for social epidemiology. J Epidemiol Community Health 2001, 55(10):693-700.
• [2]Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey SG: Indicators of socioeconomic position (part 1). J Epidemiol Community Health 2006, 60(1):7-12.
• [3]Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey SG: Indicators of socioeconomic position (part 2). J Epidemiol Community Health 2006, 60(2):95-101.
• [4]Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, Patel R, Webb EA, Lawlor DA, Hargreaves JR: Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol 2012, 41(3):871-886.
• [5]Kelaher M, Paul S, Lambert H, Ahmad W, Smith GD: The applicability of measures of socioeconomic position to different ethnic groups within the UK. Int J Equity Health 2009, 8:4.
• [6]Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, Posner S: Socioeconomic status in health research: one size does not fit all. JAMA 2005, 294(22):2879-2888.
• [7]Hagenaars J, McMutcheon A: Applied latent class analysis. Cambridge: Cambridge University Press; 2009.
• [8]Acock AC: Working with missing values. J Marriage Fam 2005, 67(4):1012-1028.
• [9]Enders CK: A primer on maximum likelihood algorithms available for use with missing data. Struct Equation Model 2001, 8(1):128-141.
• [10]Baker D, Taylor H: Inequality in health and health service use for mothers of young children in south west England. survey team of the avon longitudinal study of pregnancy and childhood team. J Epidemiol Community Health 1997, 51(1):74-79.
• [11]Savage M, Devine F, Cunningham N, Taylor M, Li Y, Hjellbrekke J, Le Roux B, Friedman S, Miles A: A new model of social class? findings from the BBC’s Great British Class survey experiment. Sociol 2013, 47(2):219-250.
• [12]Scharoun-Lee M, Kaufman JS, Popkin BM, Gordon-Larsen P: Obesity, race/ethnicity and life course socioeconomic status across the transition from adolescence to adulthood. J Epidemiol Community Health 2009, 63(2):133-139.
• [13]Scharoun-Lee M, Gordon-Larsen P, Adair LS, Popkin BM, Kaufman JS, Suchindran CM: Intergenerational profiles of socioeconomic (dis)advantage and obesity during the transition to adulthood. Demography 2011, 48(2):625-651.
• [14]Dahly D, Gilthorpe M: A latent class analysis of socioeconomic status and obesity in young adults from Cebu, Philippeans. J Epidemiol Community Health 2011, 65(Suppl 1):A71.
• [15]Wright J, Small N, Raynor P, Tuffnell D, Bhopal R, Cameron N, Fairley L, Lawlor DA, Parslow R, Petherick ES, PIckett KE, Waiblinger D, West J on behalf of the Born in Bradford Scientific Collaborators Group: Cohort profile: The Born in Bradford multi-ethnic family cohort study. Int J Epidemiol 2013, 42(4):978-991.
• [16]Rose D, Pevalin DJ: A Researcher's Guide to the National Statistics Socio-Economic Classification. London: Sage; 2003.
• [17]UK NARIC[ http://ecctis.co.uk/naric/ webcite] (Last accessed 30th August 2013)
• [18]Bradshaw J, Holmes J: Family poverty assessed at three years old. In CLS Working Paper. London: Centre for Longitudinal Studies; 2008.
• [19]Adams N, Carr J, Collins J, Johnson G, Matejic P: Households Below Average Income: An analysis of the income distribution 1994/95-2010/11. London: Department of Work and Pensions; 2012.
• [20]Office for National Statistics: Ethnic Group Statistics: A guide for the collection and classification of ethnicity data. London: The Stationary Office; 2003.
• [21]Muthen LK BOM: (1998-2012): Mplus User’s Guide. 7th edition. Los Angeles, CA: Muthen and Muthen; 2012.
• [22]Nylund KL, Asparouhiv T, Muthen BO: Deciding on the number of classes in latent class analysis and growth mixture modelling. Struct Equation Model 2007, 14(4):535-569.
• [23]Muthen B, Muthen LK: Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol Clin Exp Res 2000, 24(6):882-891.
• [24]StataCorp: Stata Statistical Software release 12. College Station, TX: StataCorp LP; 2011.
• [25]Nazroo JY: Ethnicity, Class and Health. London: Policy Studies Institute; 2001.
• [26]Plant L: Poverty and ethnicity in the UK. Bristol: Joseph Rowentree Foundation; 2007.
• [27]Nazroo JY: The structuring of ethnic inequalities in health: economic position, racial discrimination, and racism. Am J Public Health 2003, 93(2):277-284.
• [28]Chandola T: Ethnic and class differences in health in relation to British South Asians: using the new National Statistics Socio-Economic Classification. Soc Sci Med 2001, 52(8):1285-1296.