Who Feels Inferior? A Test of the Status Anxiety Hypothesis of Social Inequalities in Health

The empirical association between income inequality, population health and other social problems is now well established and the research literature suggests that the relationship is not artefactual. Debate is still ongoing as to the cause of this association. Wilkinson, Marmot and colleagues have argued for some time that the relationship stems from the psycho-social effects of status comparisons. Here, income inequality is a marker of a wider status hierarchy that provokes an emotional stress response in individuals that is harmful to health and well-being. We label this the ‘status anxiety hypothesis’. If true, this would imply a structured relationship between income inequality at the societal level, individual income rank and anxiety relating to social status. This paper sets out strong and weak forms of the hypothesis and then presents three predictions concerning the structuring of ‘status anxiety’ at the individual level given different levels of national income inequality and varying individual income. We then test these predictions using data from a cross-national survey of over 34,000 individuals carried out in 2007 in 31 European countries. Respondents from low inequality countries reported less status anxiety than those in higher inequality countries at all points on the income rank curve. This is an important precondition of support for the status anxiety hypothesis and may be seen as providing support for the weaker version of the hypothesis. However, we do not find evidence to support the stronger version of the hypothesis which requires the negative effect of income rank on status anxiety to be exacerbated by increasing income inequality.

Health status and 6 years survival of 552 90+ Italian sib-ships recruited within the EU Project GEHA (GEnetics of Healthy Ageing).

In a scenario of increasing life expectancy worldwide, it is mandatory to identify the characteristics of a healthy aging phenotype, including survival predictors, and to disentangle those related to environment/lifestyle versus those related to familiarity/genetics. To this aim we comprehensively characterised a cohort of 1,160 Italian subjects of 90 years and over (90+, mean age 93 years; age range 90-106 years) followed for 6 years survival, belonging to 552 sib-ships (familiar longevity) recruited (2005-2008) within the EU-funded GEHA project in three Italian geographic areas (Northern, Central and Southern Italy) different for urban/rural and socio-economical characteristics. On the whole, the following factors emerged as significant predictors of survival after 90 years of age: absence of cognitive impairment and physical disability, high hand grip strength scores and body mass index (BMI) values, "excellent/good" self-reported health, high haemoglobin and total cholesterol levels and low creatinine levels. These parameters, excluding BMI values, were also significantly associated within sib-ships, suggesting a strong familial/genetic component. Geographical micro-heterogeneity of survival predictors emerged, such as functional and physical status being more important in Southern than in Central and Northern Italy. In conclusion, we identified modifiable survival predictors related to specific domains, whose role and importance vary according to the geographic area considered and which can help in interpreting the genetic results obtained by the GEHA project, whose major aim is the comprehensive evaluation of phenotypic and genetic data.

How to classify the Oldest Old according to Health Status: A study of 1160 subjects belonging to 552 90+ Italian sibships characterised by familial longevity recruited within the GeHA EU project

The health status of the oldest old, the fastest increasing population segment worldwide, progressively becomes more heterogeneous, and this peculiarity represents a major obstacle to their classification. We compared the effectiveness of four previously proposed criteria (Franceschi et al., 2000; Evert et al., 2003; Gondo et al., 2006; Andersen-Ranberg et al., 2001) in 1160 phenotypically fully characterized Italian siblings of 90 years of age and older (90+, mean age: 93 years; age range: 90–106 years) belonging to 552 sib-ships, recruited in Northern, Central and Southern Italy within the EU-funded project GEHA, followed for a six-year-survival. Main findings were: (i) ‘‘healthy’’ subjects varied within a large range, i.e. 5.2% (Gondo), 8.7% (Evert), 17.7% (Franceschi), and 28.5% (Andersen-Ranberg); (ii) Central Italy subjects showed better health than those from Northern and Southern Italy; (iii) mortality risk was correlated with health status independently of geographical areas; and (iv) 90+ males, although fewer in number, were healthier than females, but with no survival advantage. In conclusion, we identified a modified version of Andersen-Ranberg criteria, based on the concomitant assessment of two basic domains (cognitive, SMMSE; physical, ADL), called ‘‘Simple Model of Functional Status’’ (SMFS), as the most effective proxy to distinguish healthy from not-healthy subjects. This model showed that health status was correlated within sib-ships, suggesting a familial/genetic component.

Risk of Several Cancers is Higher in Urban Areas after Adjusting for Socioeconomic Status. Results from a Two-Country Population-Based Study of 18 Common Cancers

Some studies suggest that there are urban-rural variations in cancer incidence but whether these simply reflect urban-rural socioeconomic variation is unclear. We investigated whether there were urban-rural variations in the incidence of 18 cancers, after adjusting for socioeconomic status. Cancers diagnosed between 1995 and 2007 were extracted from the population-based National Cancer Registry Ireland and Northern Ireland Cancer Registry and categorised by urban-rural status, based on population density of area of residence at diagnosis (rural 15 people per hectare). Relative risks (RR) were calculated by negative binomial regression, adjusting for age, country and three area-based markers of socioeconomic status. Risks were significantly higher in both sexes in urban than rural residents with head and neck (males RR urban vs. rural = 1.53, 95 % CI 1.42-1.64; females RR = 1.29, 95 % CI 1.15-1.45), esophageal (males 1.21, 1.11-1.31; females 1.21, 1.08-1.35), stomach (males 1.36, 1.27-1.46; females 1.19, 1.08-1.30), colorectal (males 1.14, 1.09-1.18; females 1.04, 1.00-1.09), lung (males 1.54, 1.47-1.61; females 1.74, 1.65-1.84), non-melanoma skin (males 1.13, 1.10-1.17; females 1.23, 1.19-1.27) and bladder (males 1.30, 1.21-1.39; females 1.31, 1.17-1.46) cancers. Risks of breast, cervical, kidney and brain cancer were significantly higher in females in urban areas. Prostate cancer risk was higher in rural areas (0.94, 0.90-0.97). Other cancers showed no significant urban-rural differences. After adjusting for socioeconomic variation, urban-rural differences were evident for 12 of 18 cancers. Variations in healthcare utilization and known risk factors likely explain some of the observed associations. Explanations for others are unclear and, in the interests of equity, warrant further investigation. © 2014 The New York Academy of Medicine.

A randomised, phase II trial of the DNA-hypomethylating agent 5-aza-2'-deoxycytidine (decitabine) in combination with carboplatin vs carboplatin alone in patients with recurrent, partially platinum-sensitive ovarian cancer.

Background: Our previous laboratory and clinical data suggested that one mechanism underlying the development of platinum resistance in ovarian cancer is the acquisition of DNA methylation. We therefore tested the hypothesis that the DNA hypomethylating agent 5-aza-2'-deoxycytodine (decitabine) can reverse resistance to carboplatin in women with relapsed ovarian cancer.

Methods: Patients progressing 6-12 months after previous platinum therapy were randomised to decitabine on day 1 and carboplatin (AUC 6) on day 8, every 28 days or carboplatin alone. The primary objective was response rate in patients with methylated hMLH1 tumour DNA in plasma.

Results: After a pre-defined interim analysis, the study closed due to lack of efficacy and poor treatment deliverability in 15 patients treated with the combination. Responses by GCIG criteria were 9 out of 14 vs 3 out of 15 and by RECIST were 6 out of 13 vs 1 out of 12 for carboplatin and carboplatin/decitabine, respectively. Grade 3/4 neutropenia was more common with the combination (60% vs 15.4%) as was G2/3 carboplatin hypersensitivity (47% vs 21%).

Conclusions: With this schedule, the addition of decitabine appears to reduce rather than increase the efficacy of carboplatin in partially platinum-sensitive ovarian cancer and is difficult to deliver. Patient-selection strategies, different schedules and other demethylating agents should be considered in future combination studies.