Bruna Galobardes

Indicators of socioeconomic position (part 1)

This glossary presents a comprehensive list of indicators of socioeconomic position used in health research. A description of what they intend to measure is given together with how data are elicited and the advantages and limitation of the indicators. The glossary is divided into two parts for journal publication but the intention is that it should be used as one piece. The second part highlights a life course approach and will be published in the next issue of the journal.

Is the association between childhood socioeconomic circumstances and cause-specific mortality established? Update of a systematic review

Objective: To update a systematic review on the association between childhood socioeconomic circumstances and cause-specific mortality. Studies published since 2003 include a far greater number of deaths than was previously available justifying an update of the previous systematic review. Methods: Individual-level studies examining childhood socioeconomic circumstances and adult overall and cause-specific mortality published between 2003 and April 2007. Results and conclusions: The new studies confirmed that mortality risk for all causes was higher among those who experienced poorer socioeconomic circumstances during childhood. As already suggested in the original systematic review, not all causes of death were equally related to childhood socioeconomic circumstances. A greater proportion of new studies included women and showed that a similar pattern is valid for both genders. In addition, the new studies show that this association persists among younger birth cohorts, despite temporal general improvements in childhood conditions across successive birth cohorts. The difficulties of establishing a particular life-course model were highlighted.

Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper

Much has been written about the measurement of socio-economic position (SEP) in high-income countries (HIC). Less has been written for an epidemiology, health systems and public health audience about the measurement of SEP in low- and middle-income countries (LMIC). The social stratification processes in many LMIC—and therefore the appropriate measurement tools—differ considerably from those in HIC. Many measures of SEP have been utilized in epidemiological studies; the aspects of SEP captured by these measures and the pathways through which they may affect health are likely to be slightly different but overlapping. No single measure of SEP will be ideal for all studies and contexts; the strengths and limitations of a given indicator are likely to vary according to the specific research question. Understanding the general properties of different indicators, however, is essential for all those involved in the design or interpretation of epidemiological studies. In this article, we describe the measures of SEP used in LMIC. We concentrate on measures of individual or household-level SEP rather than area-based or ecological measures such as gross domestic product. We describe each indicator in terms of its theoretical basis, interpretation, measurement, strengths and limitations. We also provide brief comparisons between LMIC and HIC for each measure.

A common genetic variant in the 15q24 nicotinic acetylcholine receptor gene cluster (CHRNA5–CHRNA3–CHRNB4) is associated with a reduced ability of women to quit smoking in pregnancy

Maternal smoking during pregnancy is associated with low birth weight and adverse pregnancy outcomes. Women are more likely to quit smoking during pregnancy than at any other time in their lives, but some pregnant women continue to smoke. A recent genome-wide association study demonstrated an association between a common polymorphism (rs1051730) in the nicotinic acetylcholine receptor gene cluster (CHRNA5–CHRNA3–CHRNB4) and both smoking quantity and nicotine dependence. We aimed to test whether the same polymorphism that predisposes to greater cigarette consumption would also reduce the likelihood of smoking cessation in pregnancy. We studied 7845 pregnant women of European descent from the South-West of England. Using 2474 women who smoked regularly immediately pre-pregnancy, we analysed the association between the rs1051730 risk allele and both smoking cessation during pregnancy and smoking quantity. Each additional copy of the risk allele was associated with a 1.27-fold higher odds (95% CI 1.11–1.45) of continued smoking during pregnancy (P = 0.0006). Adjustment for pre-pregnancy smoking quantity weakened, but did not remove this association [odds ratio (OR) 1.20 (95% CI 1.03–1.39); P = 0.018]. The same risk allele was also associated with heavier smoking before pregnancy and in the first, but not the last, trimester [OR for smoking 10+ cigarettes/day versus 1–9/day in first trimester = 1.30 (95% CI 1.13–1.50); P = 0.0003]. To conclude, we have found strong evidence of association between the rs1051730 variant and an increased likelihood of continued smoking in pregnancy and have confirmed the previously observed association with smoking quantity. Our data support the role of genetic factors in influencing smoking cessation during pregnancy.

Trends in risk factors for lifestyle-related diseases by socioeconomic position in Geneva, Switzerland, 1993-2000: Health inequalities persist

OBJECTIVES We report on trends in risk factors for lifestyle-related diseases among socioeconomic position (SEP) groups. METHODS We continuously surveyed the adult population of Geneva, Switzerland, for 8 years (1993-2000) with independent, cross-sectional surveys of representative samples (4207 men and 3987 women aged 35-74 years). Age-adjusted linear regression slopes estimated annual risk factor trends. Interaction terms were tested for trend differences between SEP groups. RESULTS Overall, low-SEP persons had the worst risk factor profiles. Eight-year trends indicate that (1) number of pack-years smoked decreased by half a pack-year among high-SEP female current smokers only; (2) obesity prevalence more than doubled from 5% to 11% among high-SEP men only; (3) systolic and diastolic blood pressures decreased similarly in all SEP groups; (4) unsaturated-to-saturated dietary fat ratio declined in the low-SEP group only; and (5) physical inactivity and current/former cigarette smoking prevalences remained unchanged in all SEP groups. CONCLUSIONS Smoking, obesity, high blood pressure, and physical inactivity are more prevalent among low-SEP persons. Most socioeconomic risk factor differences remained stable in the 1990s. Thus, social inequalities in chronic disease morbidity and mortality will persist in the next decades.

Loss to Follow-up in cohort studies: bias in estimates of socioeconomic inequalities

Background: Although cohort members tend to be healthy and affluent compared with the whole population, some studies indicate this does not bias certain exposure-outcome associations. It is less clear whether this holds when socioeconomic position (SEP) is the exposure of interest. Methods: As an illustrative example, we use data from the Avon Longitudinal Study of Parents and Children. We calculate estimates of maternal education inequalities in outcomes for which data are available on almost the whole cohort (birth weight and length, breastfeeding, preterm birth, maternal obesity, smoking during pregnancy, educational attainment). These are calculated for the full cohort (n~12,000) and in restricted subsamples defined by continued participation at age 10 years (n∼7,000) and age 15 years (n∼5,000). Results: Loss to follow-up was related both to SEP and outcomes. For each outcome, loss to follow-up was associated with underestimation of inequality, which increased as participation rates decreased (eg, mean birth-weight difference between highest and lowest SEP was 116 g [95% confidence interval = 78 to 153] in the full sample and 93 g [45 to 141] and 62 g [5 to 119] in those attending at ages 10 and 15 years, respectively). Conclusions: Considerable attrition from cohort studies may result in biased estimates of socioeconomic inequalities, and the degree of bias may worsen as participation rates decrease. However, even with considerable attrition (>50%), qualitative conclusions about the direction and approximate magnitude of inequalities did not change among most of our examples. The appropriate analysis approaches to alleviate bias depend on the missingness mechanism.

Associations between tooth loss and mortality patterns in the Glasgow Alumni Cohort

Objective: To use data from the Glasgow Alumni Cohort to investigate whether oral health in young adulthood is independently associated with later life cardiovascular disease (CVD) and cancer mortality. Methods and results: Of the original cohort (n = 15 322), 12 631 subjects were traced through the National Health Service Central Register. Of these, 9569 men and 2654 women were 30 years or younger at baseline. During up to 57 years of follow-up, 1432 deaths occurred among subjects with complete data, including 509 deaths from CVD and 549 from cancer. After adjusting for potential confounders, no substantial association was found between the number of missing teeth (as a continuous variable) and all-cause mortality (hazard ratio (HR) for each extra missing tooth  = 1.01; 95% confidence interval (CI) 1.00 to 1.02), CVD mortality (HR = 1.01; 95% CI 0.99 to 1.03) or cancer mortality (HR = 1.00; 95% CI 0.98 to 1.02). When the number of missing teeth was treated as a categorical variable, there was evidence that students with nine or more missing teeth at baseline had an increased risk of CVD (HR = 1.35; 95% CI 1.03 to 1.77) compared with those with fewer than five missing teeth. When the number of missing teeth was transformed using fractional polynomials, there seemed to be a non-linear relation between missing teeth and CVD mortality. Conclusions: Although some evidence was found to support the relation between tooth loss and CVD mortality, causal mechanisms underlying this association remain uncertain.

A Swiss neighbourhood index of socioeconomic position: development and association with mortality

Background Area-based measures of socioeconomic position (SEP) suitable for epidemiological research are lacking in Switzerland. The authors developed the Swiss neighbourhood index of SEP (Swiss-SEP). Methods Neighbourhoods of 50 households with overlapping boundaries were defined using Census 2000 and road network data. Median rent per square metre, proportion households headed by a person with primary education or less, proportion headed by a person in manual or unskilled occupation and the mean number of persons per room were analysed in principle component analysis. The authors compared the index with independent income data and examined associations with mortality from 2001 to 2008. Results 1.27 million overlapping neighbourhoods were defined. Education, occupation and housing variables had loadings of 0.578, 0.570 and 0.362, respectively, and median rent had a loading of −0.459. Mean yearly equivalised income of households increased from SFr42 000 to SFr72 000 between deciles of neighbourhoods with lowest and highest SEP. Comparing deciles of neighbourhoods with lowest to highest SEP, the age- and sex-adjusted HR was 1.38 (95% CI 1.36 to 1.41) for all-cause mortality, 1.83 (95% CI 1.71 to 1.95) for lung cancer, 1.48 (95% CI 1.44 to 1.51) for cardiovascular diseases, 2.42 (95% CI 1.94 to 3.01) for traffic accidents, 0.93 (95% CI 0.85 to 1.02) for breast cancer and 0.86 (95% CI 0.78 to 0.95) for suicide. Conclusions Developed using a novel approach to define neighbourhoods, the Swiss-SEP index was strongly associated with household income and some causes of death. It will be useful for clinical- and population-based studies, where individual-level socioeconomic data are often missing, and to investigate the effects on health of the socioeconomic characteristics of a place.

Socioeconomic disparities in trajectories of adiposity across childhood.

BACKGROUND Socioeconomic inequalities in obesity are consistently observed in high-income countries. The development of such inequalities across childhood; however, has not been studied using longitudinal data. METHODS Using data from the Avon Longitudinal Study of Parents and Children (participants were born 1991/2 in South-West England), we modelled trajectories of ponderal index (PI) (N=12 246) from birth to 2 years and body mass index (BMI) (N=11 380) from 2 to 10 years. Individual trajectories were estimated using mixed-effects models, and differences in trajectories by socioeconomic position (measured by maternal education) were investigated. RESULTS There was little socioeconomic patterning of PI from birth to 2 years. Socioeconomic differences in BMI began to emerge by 4 years old, and widened with increasing age. Amongst girls there was a clear gradient across all categories of maternal education by age 8, with daughters of more educated women being less adipose. Amongst boys, sons of degree-educated women had lower BMI but there was little difference between the lower maternal education categories. By 10 years old the mean BMI difference between the highest and lowest maternal education category was 0.38 kg/m(2) for boys and 0.89 kg/m(2) for girls. The results imply that interventions to prevent inequalities in childhood obesity should begin in pre-school years.

Socioeconomic differences in childhood growth trajectories: at what age do height inequalities emerge?

Background Socioeconomic differentials in adult height are frequently observed, but the age at which these inequalities emerge and the patterns they follow through childhood are unknown. Subjects and Methods Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), height trajectories from birth to 10 years (N=12366) were modelled. Individual trajectories were estimated using mixed-effects models. Differences in trajectories by socioeconomic position (SEP) were investigated. Results There was a clear gradient in birth length across categories of maternal education; average birth length in boys was 0.41 cm lower in the lowest maternal education category compared with the highest, which is 0.9% of the average birth length for the highest SEP category (equivalent results for girls 0.65 cm, 1.3%). Socioeconomic differences in childhood growth were small, and only resulted in minimal widening of the height inequality with increasing age. By the age of 10 years, the mean difference between children in the lowest and highest maternal education categories was 1.4 cm for boys and 1.7 cm for girls; similar proportionate differences to those seen at birth (1.0% for boys and 1.2% for girls). Patterns were the same when fathers education or household occupational social class were used to measure SEP. Conclusions The socioeconomic differential in height during childhood in this cohort of children born in the UK in the 1990s arises largely through inequalities in birth length, with small increases in the inequality from differences in growth in later childhood.