Association of socioeconomic position with insulin resistance among ch
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《英国医生杂志》
1 Department of Social Medicine, University of Bristol, Bristol BS8 2PR, 2 Department of Exercise and Health Sciences, University of Bristol, 3 Estonian National Institute for Health Development, Tallin, Estonia, 4 Institute of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark, 5 Department of Sports Medicine, Norwegian University of Sport and Physical Education, Oslo, Norway, 6 Faculty of Human Movement, Technical University of Lisbon, Portugal, 7 London Sport Institute, Middlesex University, London, 8 Department of Clinical Biochemistry, United Bristol Healthcare NHS Trust, Bristol
Correspondence to: D A Lawlor d.a.lawlor@bristol.ac.uk
Objectives To examine the association between socioeconomic position and insulin resistance in children from three countries in northern Europe (Denmark), eastern Europe (Estonia), and southern Europe (Portugal) that have different physical, economic, and cultural environments.
Design Cross sectional study.
Participants 3189 randomly selected schoolchildren aged 9 and 15 years from Denmark (n = 933), Estonia (n = 1103), and Portugal (n = 1153).
Main outcome measure Insulin resistance (homoeostasis model assessment).
Results Family income and parental education were inversely associated with insulin resistance in Danish children but were positively associated with insulin resistance in Estonian and Portuguese children. Among Danish children, insulin resistance was 24% lower (95% confidence interval -38% to -10%) in those whose fathers had the most education compared with those with the least education. The equivalent results were 15% (2% to 28%) higher for Estonia and 19% (2% to 36%) higher for Portugal. These associations remained after adjustment for a range of covariates: -20% (-36% to -5%) for Denmark, 10% (-4% to 24%) for Estonia, and 18% (-1% to 31%) for Portugal. Strong statistical evidence supported differences between the associations in Denmark and those in the other two countries in both unadjusted and adjusted models (all P < 0.03).
Conclusions Among Danish children, those with the most educated and highest earning parents had least insulin resistance, whereas the opposite was true for children from Estonia and Portugal.
Adverse childhood socioeconomic position is associated with increased risk of coronary heart disease in later life,1 and this may, at least in part, be mediated by insulin resistance.2 Socio-economic inequalities in health outcomes are dynamic and vary over time and between countries.3 Differences between countries can provide useful insights into the causes of health inequalities.4 5 Although several studies have compared the associations between socioeconomic position and health outcomes among adults in different countries,4 6-10 despite a systematic search of the literature we were unable to find any previous studies comparing differences in the association between socio-economic position and health outcomes in children from different countries.
The objective of this study was to examine the association between socioeconomic position and insulin resistance in children from three countries in northern Europe (Denmark), eastern Europe (Estonia), and southern Europe (Portugal) that have different physical, economic, and cultural environments. These countries have important differences and similarities that could provide insights into the effects of socioeconomic position on insulin resistance. For example, Estonia differs from the other two countries in terms of a recent experience of marked social, cultural, and economic change, whereas Denmark differs from both Estonia and Portugal (two of the poorest countries in Europe) in being one of the richest countries in Europe.10
Methods
We used data from the three countries in the European youth heart study—Denmark (Odense), Estonia (Tartu), and Portugal (Madeira).11 Odense is the third city of Denmark and is situated on the island of Fyn. Tartu is the second city of Estonia, an emerging eastern European country and former member of the Soviet Union. Madeira is a Portuguese island located in the Atlantic, off the west coast of Morocco.
Full details of the selection of study participants and measurements have been previously reported.11 We randomly selected boys and girls aged 9 and 15 years. We chose these age groups to broadly represent children either side of puberty and thus avoid the effect of puberty on metabolic and other cardiovascular disease risk factors. The overall participation was similar in each country (75% in Denmark, 76% in Estonia, and 73% in Portugal), and in total 3317 children (1019 from Denmark, 1174 from Estonia, and 1124 from Portugal) participated. We obtained written, informed consent from the child's parent or legal guardian after they were given, in writing, a full explanation of the aims of the study and its possible hazards, discomfort, and inconvenience. In addition, children had all the procedures verbally explained to them, together with any possible discomfort they might encounter, and were given the option to withdraw at any time.
Children had a physical examination, including measurement of weight, height, waist circumference, skinfold thickness (sum of five sites used in all analyses), and blood pressure; the same standard procedures were used in each country. The equipment used for blood pressure and anthropometric measures was the same in Denmark and Estonia. Measurements were made in Denmark between September 1997 and June 1998 and in Estonia between September 1998 and June 1999. Different equipment (but the same procedures and quality assurance measures) was used in Portugal, and measurements were made here between January 1999 and June 2000. We collected blood samples for the assessment of insulin, glucose, and lipid concentrations after an overnight fast; samples were analysed by Clinical Pathology Accreditation (CPA) accredited laboratories in Bristol, England (Denmark and Estonia) or Cambridge, England (Portugal). Results from 30 samples originally analysed in Bristol and reanalysed in Cambridge showed high levels of agreement. We estimated insulin resistance from fasting glucose and insulin according to the homoeostasis model assessment (HOMA).12
Both parents reported their educational attainment and personal income. In each country we classified parental education into four categories (basic/primary; secondary/trade apprentice; higher vocational qualifications; university). We classified income into eight categories representing the ways in which income was most commonly reported in each country (monthly in Estonia and annually in Denmark and Portugal) and used country specific categories. For each country we calculated mean family income as the mean of both parents' income category and collapsed it into five categories.
Because the proportions of participants in each category of family income and parental education varied between the three countries, we estimated indices of inequality.13 A score from 0 to 1 represents each socioeconomic position variable; the score for those in each category is the mid-point of the proportion of the participants in that category. For example, if in one of the countries 10% of the participants were in the lowest category of education, and a score from 0 (lowest education) to 1 (highest education) represents the whole study population, participants in this group would be allocated the score of 0.05 (that is, 0.1/2). If 20% of the participants were in the second category of education, then this category is allocated a score of 0.20 (0.1+0.2/2), and so on. The slope of the index of inequality is then obtained by regressing each of the outcomes on these 0 to 1 scores. The virtue of this is that it is directly interpretable as comparing the highest (1) with the lowest (0) level of education and income in each country.13
We used multivariable linear regression models to assess the associations of parental education and family income with insulin resistance. We used F tests for statistical interaction to assess differences in any associations between the countries. HOMA scores and triglyceride concentrations were positively skewed; geometric means are shown, and we used the natural log of the values in the regression models. We back transformed the resultant regression coefficients to give a ratio of geometric means from which we calculated a proportionate (percentage) difference between the lowest (0) and highest (1) socioeconomic position score in insulin resistance. As HOMA scores vary by age, sex, and country, we also repeated all analyses using age, sex, and country specific z scores of HOMA. We used Stata version 8.0 for all analyses.
Results
Of all participants, 933 (92%) of those from Denmark, 1153 (98%) of those from Estonia, and 1103 (98%) of those from Portugal had data from blood assays. No differences in mean age, proportion of girls, body mass index, height, waist circumference, or skinfold thickness existed between those with and without these data (all P > 0.7). Table 1 shows the characteristics of the study participants. Among the younger age group, 80% were prepubertal (Tanner stage I), and all the rest were Tanner stage II (early puberty). Among the older age group, 60% were postpuberty (Tanner stage V), 39% were stage III or IV (in puberty), and just 1% were prepubertal or in early puberty. These proportions did not differ by country.
Table 1 Characteristics of participants. Values are means (SDs) unless stated otherwise
We found no evidence of differences in the associations of family income and parental education with insulin resistance by sex or age group (all P values for interactions > 0.4), so all results are presented for sex and age groups combined. We have previously shown that height interacts with age in its association with insulin resistance—among children from the younger age group height was strongly positively associated with insulin resistance, whereas in the older age group no association existed.14 We included an interaction term between height and age in all multivariable models where appropriate. We found no other interactions between covariates (all P > 0.5). The effects of all three measurements of socioeconomic position on HOMA were the same in strata of pubertal stage (prepubertal stage I, pubertal stages II-IV, postpubertal stage V; all P > 0.8).
Table 2 shows the unadjusted associations of parental education and family income with insulin resistance and other characteristics for each country. In Denmark, children from families with higher incomes and whose parents were better educated had lower HOMA scores than did those from lower income families and whose parents were less well educated. In Estonia and Portugal, we found associations in the opposite direction (P for difference < 0.001 between Denmark and Estonia and between Denmark and Portugal). Although many of the associations with other metabolic risk factors were imprecise, children from Denmark who were from lower income families and whose parents had received the lowest levels of education tended to have the worst risk factor profiles, with the opposite being the case for Estonia and Portugal. Family income and parental education were positively associated with fitness in children from Denmark, but no strong associations existed in either Estonia or Portugal.
Table 2 Associations of family income and parental education with insulin resistance syndrome and other characteristics of children from Denmark, Estonia, and Portugal: unadjusted difference (95% confidence interval) between lowest and highest level of income and education in each country
The inverse associations in Danish children and positive associations in Estonian and Portuguese children remained after adjustment for age, sex, parental body mass index, birth weight, breast feeding, height, pubertal stage, and measures of childhood adiposity and fitness (table 3). After adjustment for parental education, the associations between family income and insulin resistance attenuated towards the null in all three countries, but the associations between parental education and insulin resistance remained after adjustment for family income and all other covariates. Statistical evidence of a difference between the effect of maternal and paternal education in Denmark and in the other two countries remained in the fully adjusted models (P = 0.03 for difference between Denmark and Estonia in maternal education and P < 0.001 for paternal education; P < 0.001 for difference between Denmark and Portugal for both maternal and paternal education). When we used z scores for HOMA as the outcome rather than log HOMA scores and actual categories of socioeconomic exposure rather than the indices of inequalities, the directions of associations, effects of adjustment for covariates, and differences between the countries did not differ from those already presented. For example, the figure shows the differing associations between father's education (in its original categories) and z scores for HOMA for each country.
Table 3 Multivariable associations of family income and parental education with insulin resistance in children from Denmark, Estonia, and Portugal: adjusted differences (95% confidence intervals) in HOMA (%) between highest and lowest levels of each factor in each country
Mean z score of homoeostasis model assessment (HOMA) by father's education in children from Denmark, Estonia, and Portugal
Discussion
Wannamethee SG, Whincup PH, Shaper G, Walker M. Influence of fathers' social class on cardiovascular disease in middle-aged men. Lancet 1996;348: 1259-63.
Lawlor DA, Ebrahim S, Davey Smith G. Socioeconomic position in childhood and adulthood and insulin resistance: cross sectional survey using data from the British women's heart and health study. BMJ 2002;325: 805-7.
Davey Smith G, Lynch J. Life course approaches to socioeconomic differentials in health. In: Kuh D, Ben-Shlomo Y, eds. A life course approach to chronic disease epidemiology. Second ed. Oxford: Oxford University Press, 2004: 77-115.
Kunst AE, Groenhof F, Andersen O, Borgan JK, Costa G, Desplanques G, et al. Occupational class and ischemic heart disease mortality in the United States and 11 European countries. Am J Public Health 1999;89: 47-53.
Leon DA. Commentary: Unequal inequalities across Europe. BMJ 1998;316: 1642.
Leclerc A, Lert F, Fabien C. Differential mortality: some comparisons between England and Wales, Finland and France, based on inequality measures. Int J Epidemiol 1990;19: 1001-10.
Kunst AE, Geurts JJM, van den Berg J. International variation in socioeconomic inequalities in self reported health. J Epidemiol Community Health 1995;49: 117-23.
Kunst AE, Mackenbach JP. The size of mortality differences associated with educational level in nine industrialized countries. Am J Public Health 1994;84: 932-7.
Mackenbach JP, Kunst AE, Cavelaars AEJM, Groenhof F, Geurts JJM, EU Working Group on Socioeconomic Inequalities in Health. Socioeconomic inequalities in morbidity and mortality in western Europe. Lancet 1997;349: 1655-9.
Van DE, Koolman X. Explaining the differences in income-related health inequalities across European countries. Health Econ 2004;13: 609-28.
Riddoch C, Edwards D, Page A, Froberg K, Anderssen SA, Wedderkopp N, et al. The European youth heart study—cardiovascular disease risk factors in children: rationale, aims, study design and validation of methods. Journal of Physical Activity and Health 2005;2: 115-29.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28: 412-9.
Mackenbach JP, Kunst AE. Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med 1997;44: 757-71.
Lawlor DA, Riddoch CJ, Page AS, Anderssen SA, Froberg K, Harro M, et al. The association of birth weight and contemporary size with insulin resistance among children from Estonia and Denmark: findings from the European heart study. Diabet Med 2005; in press.
Gunnell D. Commentary: Early insights into height, leg length, proportionate growth and health. Int J Epidemiol 2001;30: 221-2.
Brandou F, Brun J-F, Mercier J. Limited accuracy of surrogates of insulin resistance during puberty in obese and lean children at risk for altered glucoregulation. J Clin Endocrinol Metab 2005;90: 761-7.
Galobardes B, Lynch JW, Davey Smith G. Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation. Epidemiol Rev 2004;26: 7-21.
Leinsalu M, Vagero D, Kunst AE. Estonia 1989-2000: enormous increase in mortality differences by education. Int J Epidemiol 2003;32: 1081-7.
Kunst A. Cross-national comparisons of socio-economic differences in mortality. Rotterdam: Erasmus University, 1997.
Shkolnikov VM, Leon DA, Adamets S, Andreev E, Deev A. Educational level and adult mortality in Russia: an analysis of routine data 1979 to 1994. Soc Sci Med 1998;47: 357-69.
Vodopivec M. Worker reallocation during Estonia's transition to market. International Journal of Manpower 2002;23: 77-97.
Kunst AE, Groenhof F, Mackenbach JP, EU Working Group on Socioeconomic Inequalities in Health. Occupational class and cause specific mortality in middle aged men in 11 European countries: comparison of population based studies. BMJ 1998;316: 1636-42.
Harro M, Villa I, Liiv K, Aru J, Alep J. Nutrition-related health indicators and their major determinants in the new member states: case of Estonia. J Public Health 2005; in press.
Davey Smith G. Socioeconomic differentials. In: Kuh D, ed. A life course approach to chronic disease epidemiology. Oxford: Oxford Medical Publications, 1997: 242-73.(Debbie A Lawlor, senior lecturer in epid)
Correspondence to: D A Lawlor d.a.lawlor@bristol.ac.uk
Objectives To examine the association between socioeconomic position and insulin resistance in children from three countries in northern Europe (Denmark), eastern Europe (Estonia), and southern Europe (Portugal) that have different physical, economic, and cultural environments.
Design Cross sectional study.
Participants 3189 randomly selected schoolchildren aged 9 and 15 years from Denmark (n = 933), Estonia (n = 1103), and Portugal (n = 1153).
Main outcome measure Insulin resistance (homoeostasis model assessment).
Results Family income and parental education were inversely associated with insulin resistance in Danish children but were positively associated with insulin resistance in Estonian and Portuguese children. Among Danish children, insulin resistance was 24% lower (95% confidence interval -38% to -10%) in those whose fathers had the most education compared with those with the least education. The equivalent results were 15% (2% to 28%) higher for Estonia and 19% (2% to 36%) higher for Portugal. These associations remained after adjustment for a range of covariates: -20% (-36% to -5%) for Denmark, 10% (-4% to 24%) for Estonia, and 18% (-1% to 31%) for Portugal. Strong statistical evidence supported differences between the associations in Denmark and those in the other two countries in both unadjusted and adjusted models (all P < 0.03).
Conclusions Among Danish children, those with the most educated and highest earning parents had least insulin resistance, whereas the opposite was true for children from Estonia and Portugal.
Adverse childhood socioeconomic position is associated with increased risk of coronary heart disease in later life,1 and this may, at least in part, be mediated by insulin resistance.2 Socio-economic inequalities in health outcomes are dynamic and vary over time and between countries.3 Differences between countries can provide useful insights into the causes of health inequalities.4 5 Although several studies have compared the associations between socioeconomic position and health outcomes among adults in different countries,4 6-10 despite a systematic search of the literature we were unable to find any previous studies comparing differences in the association between socio-economic position and health outcomes in children from different countries.
The objective of this study was to examine the association between socioeconomic position and insulin resistance in children from three countries in northern Europe (Denmark), eastern Europe (Estonia), and southern Europe (Portugal) that have different physical, economic, and cultural environments. These countries have important differences and similarities that could provide insights into the effects of socioeconomic position on insulin resistance. For example, Estonia differs from the other two countries in terms of a recent experience of marked social, cultural, and economic change, whereas Denmark differs from both Estonia and Portugal (two of the poorest countries in Europe) in being one of the richest countries in Europe.10
Methods
We used data from the three countries in the European youth heart study—Denmark (Odense), Estonia (Tartu), and Portugal (Madeira).11 Odense is the third city of Denmark and is situated on the island of Fyn. Tartu is the second city of Estonia, an emerging eastern European country and former member of the Soviet Union. Madeira is a Portuguese island located in the Atlantic, off the west coast of Morocco.
Full details of the selection of study participants and measurements have been previously reported.11 We randomly selected boys and girls aged 9 and 15 years. We chose these age groups to broadly represent children either side of puberty and thus avoid the effect of puberty on metabolic and other cardiovascular disease risk factors. The overall participation was similar in each country (75% in Denmark, 76% in Estonia, and 73% in Portugal), and in total 3317 children (1019 from Denmark, 1174 from Estonia, and 1124 from Portugal) participated. We obtained written, informed consent from the child's parent or legal guardian after they were given, in writing, a full explanation of the aims of the study and its possible hazards, discomfort, and inconvenience. In addition, children had all the procedures verbally explained to them, together with any possible discomfort they might encounter, and were given the option to withdraw at any time.
Children had a physical examination, including measurement of weight, height, waist circumference, skinfold thickness (sum of five sites used in all analyses), and blood pressure; the same standard procedures were used in each country. The equipment used for blood pressure and anthropometric measures was the same in Denmark and Estonia. Measurements were made in Denmark between September 1997 and June 1998 and in Estonia between September 1998 and June 1999. Different equipment (but the same procedures and quality assurance measures) was used in Portugal, and measurements were made here between January 1999 and June 2000. We collected blood samples for the assessment of insulin, glucose, and lipid concentrations after an overnight fast; samples were analysed by Clinical Pathology Accreditation (CPA) accredited laboratories in Bristol, England (Denmark and Estonia) or Cambridge, England (Portugal). Results from 30 samples originally analysed in Bristol and reanalysed in Cambridge showed high levels of agreement. We estimated insulin resistance from fasting glucose and insulin according to the homoeostasis model assessment (HOMA).12
Both parents reported their educational attainment and personal income. In each country we classified parental education into four categories (basic/primary; secondary/trade apprentice; higher vocational qualifications; university). We classified income into eight categories representing the ways in which income was most commonly reported in each country (monthly in Estonia and annually in Denmark and Portugal) and used country specific categories. For each country we calculated mean family income as the mean of both parents' income category and collapsed it into five categories.
Because the proportions of participants in each category of family income and parental education varied between the three countries, we estimated indices of inequality.13 A score from 0 to 1 represents each socioeconomic position variable; the score for those in each category is the mid-point of the proportion of the participants in that category. For example, if in one of the countries 10% of the participants were in the lowest category of education, and a score from 0 (lowest education) to 1 (highest education) represents the whole study population, participants in this group would be allocated the score of 0.05 (that is, 0.1/2). If 20% of the participants were in the second category of education, then this category is allocated a score of 0.20 (0.1+0.2/2), and so on. The slope of the index of inequality is then obtained by regressing each of the outcomes on these 0 to 1 scores. The virtue of this is that it is directly interpretable as comparing the highest (1) with the lowest (0) level of education and income in each country.13
We used multivariable linear regression models to assess the associations of parental education and family income with insulin resistance. We used F tests for statistical interaction to assess differences in any associations between the countries. HOMA scores and triglyceride concentrations were positively skewed; geometric means are shown, and we used the natural log of the values in the regression models. We back transformed the resultant regression coefficients to give a ratio of geometric means from which we calculated a proportionate (percentage) difference between the lowest (0) and highest (1) socioeconomic position score in insulin resistance. As HOMA scores vary by age, sex, and country, we also repeated all analyses using age, sex, and country specific z scores of HOMA. We used Stata version 8.0 for all analyses.
Results
Of all participants, 933 (92%) of those from Denmark, 1153 (98%) of those from Estonia, and 1103 (98%) of those from Portugal had data from blood assays. No differences in mean age, proportion of girls, body mass index, height, waist circumference, or skinfold thickness existed between those with and without these data (all P > 0.7). Table 1 shows the characteristics of the study participants. Among the younger age group, 80% were prepubertal (Tanner stage I), and all the rest were Tanner stage II (early puberty). Among the older age group, 60% were postpuberty (Tanner stage V), 39% were stage III or IV (in puberty), and just 1% were prepubertal or in early puberty. These proportions did not differ by country.
Table 1 Characteristics of participants. Values are means (SDs) unless stated otherwise
We found no evidence of differences in the associations of family income and parental education with insulin resistance by sex or age group (all P values for interactions > 0.4), so all results are presented for sex and age groups combined. We have previously shown that height interacts with age in its association with insulin resistance—among children from the younger age group height was strongly positively associated with insulin resistance, whereas in the older age group no association existed.14 We included an interaction term between height and age in all multivariable models where appropriate. We found no other interactions between covariates (all P > 0.5). The effects of all three measurements of socioeconomic position on HOMA were the same in strata of pubertal stage (prepubertal stage I, pubertal stages II-IV, postpubertal stage V; all P > 0.8).
Table 2 shows the unadjusted associations of parental education and family income with insulin resistance and other characteristics for each country. In Denmark, children from families with higher incomes and whose parents were better educated had lower HOMA scores than did those from lower income families and whose parents were less well educated. In Estonia and Portugal, we found associations in the opposite direction (P for difference < 0.001 between Denmark and Estonia and between Denmark and Portugal). Although many of the associations with other metabolic risk factors were imprecise, children from Denmark who were from lower income families and whose parents had received the lowest levels of education tended to have the worst risk factor profiles, with the opposite being the case for Estonia and Portugal. Family income and parental education were positively associated with fitness in children from Denmark, but no strong associations existed in either Estonia or Portugal.
Table 2 Associations of family income and parental education with insulin resistance syndrome and other characteristics of children from Denmark, Estonia, and Portugal: unadjusted difference (95% confidence interval) between lowest and highest level of income and education in each country
The inverse associations in Danish children and positive associations in Estonian and Portuguese children remained after adjustment for age, sex, parental body mass index, birth weight, breast feeding, height, pubertal stage, and measures of childhood adiposity and fitness (table 3). After adjustment for parental education, the associations between family income and insulin resistance attenuated towards the null in all three countries, but the associations between parental education and insulin resistance remained after adjustment for family income and all other covariates. Statistical evidence of a difference between the effect of maternal and paternal education in Denmark and in the other two countries remained in the fully adjusted models (P = 0.03 for difference between Denmark and Estonia in maternal education and P < 0.001 for paternal education; P < 0.001 for difference between Denmark and Portugal for both maternal and paternal education). When we used z scores for HOMA as the outcome rather than log HOMA scores and actual categories of socioeconomic exposure rather than the indices of inequalities, the directions of associations, effects of adjustment for covariates, and differences between the countries did not differ from those already presented. For example, the figure shows the differing associations between father's education (in its original categories) and z scores for HOMA for each country.
Table 3 Multivariable associations of family income and parental education with insulin resistance in children from Denmark, Estonia, and Portugal: adjusted differences (95% confidence intervals) in HOMA (%) between highest and lowest levels of each factor in each country
Mean z score of homoeostasis model assessment (HOMA) by father's education in children from Denmark, Estonia, and Portugal
Discussion
Wannamethee SG, Whincup PH, Shaper G, Walker M. Influence of fathers' social class on cardiovascular disease in middle-aged men. Lancet 1996;348: 1259-63.
Lawlor DA, Ebrahim S, Davey Smith G. Socioeconomic position in childhood and adulthood and insulin resistance: cross sectional survey using data from the British women's heart and health study. BMJ 2002;325: 805-7.
Davey Smith G, Lynch J. Life course approaches to socioeconomic differentials in health. In: Kuh D, Ben-Shlomo Y, eds. A life course approach to chronic disease epidemiology. Second ed. Oxford: Oxford University Press, 2004: 77-115.
Kunst AE, Groenhof F, Andersen O, Borgan JK, Costa G, Desplanques G, et al. Occupational class and ischemic heart disease mortality in the United States and 11 European countries. Am J Public Health 1999;89: 47-53.
Leon DA. Commentary: Unequal inequalities across Europe. BMJ 1998;316: 1642.
Leclerc A, Lert F, Fabien C. Differential mortality: some comparisons between England and Wales, Finland and France, based on inequality measures. Int J Epidemiol 1990;19: 1001-10.
Kunst AE, Geurts JJM, van den Berg J. International variation in socioeconomic inequalities in self reported health. J Epidemiol Community Health 1995;49: 117-23.
Kunst AE, Mackenbach JP. The size of mortality differences associated with educational level in nine industrialized countries. Am J Public Health 1994;84: 932-7.
Mackenbach JP, Kunst AE, Cavelaars AEJM, Groenhof F, Geurts JJM, EU Working Group on Socioeconomic Inequalities in Health. Socioeconomic inequalities in morbidity and mortality in western Europe. Lancet 1997;349: 1655-9.
Van DE, Koolman X. Explaining the differences in income-related health inequalities across European countries. Health Econ 2004;13: 609-28.
Riddoch C, Edwards D, Page A, Froberg K, Anderssen SA, Wedderkopp N, et al. The European youth heart study—cardiovascular disease risk factors in children: rationale, aims, study design and validation of methods. Journal of Physical Activity and Health 2005;2: 115-29.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28: 412-9.
Mackenbach JP, Kunst AE. Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med 1997;44: 757-71.
Lawlor DA, Riddoch CJ, Page AS, Anderssen SA, Froberg K, Harro M, et al. The association of birth weight and contemporary size with insulin resistance among children from Estonia and Denmark: findings from the European heart study. Diabet Med 2005; in press.
Gunnell D. Commentary: Early insights into height, leg length, proportionate growth and health. Int J Epidemiol 2001;30: 221-2.
Brandou F, Brun J-F, Mercier J. Limited accuracy of surrogates of insulin resistance during puberty in obese and lean children at risk for altered glucoregulation. J Clin Endocrinol Metab 2005;90: 761-7.
Galobardes B, Lynch JW, Davey Smith G. Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation. Epidemiol Rev 2004;26: 7-21.
Leinsalu M, Vagero D, Kunst AE. Estonia 1989-2000: enormous increase in mortality differences by education. Int J Epidemiol 2003;32: 1081-7.
Kunst A. Cross-national comparisons of socio-economic differences in mortality. Rotterdam: Erasmus University, 1997.
Shkolnikov VM, Leon DA, Adamets S, Andreev E, Deev A. Educational level and adult mortality in Russia: an analysis of routine data 1979 to 1994. Soc Sci Med 1998;47: 357-69.
Vodopivec M. Worker reallocation during Estonia's transition to market. International Journal of Manpower 2002;23: 77-97.
Kunst AE, Groenhof F, Mackenbach JP, EU Working Group on Socioeconomic Inequalities in Health. Occupational class and cause specific mortality in middle aged men in 11 European countries: comparison of population based studies. BMJ 1998;316: 1636-42.
Harro M, Villa I, Liiv K, Aru J, Alep J. Nutrition-related health indicators and their major determinants in the new member states: case of Estonia. J Public Health 2005; in press.
Davey Smith G. Socioeconomic differentials. In: Kuh D, ed. A life course approach to chronic disease epidemiology. Oxford: Oxford Medical Publications, 1997: 242-73.(Debbie A Lawlor, senior lecturer in epid)