Sally Macintyre

There is an extensive literature within anthropology, sociology and psychology about lay concepts of determinants of health and illness. Many of these studies have used single sex or social class samples, often in narrow age bands, and many are qualitative in approach. We asked respondents in a health survey to say how important (on a five-point scale) they thought seven potential influences on health (habits, self-care, the environment, family relationships, one's constitution, money and luck) were. The first three were regarded as very important, the second three as less important and luck as least important. Responses were consistent with current public health and epidemiological knowledge; these respondents endorsed prevailing views about personal responsibility for health and about the role of the physical and social environment in influencing health. In mutually adjusted models, there were no significant gender differences, social class differences and neighbourhood differences in three out of seven influences, and age differences in four out of seven influences. Thus, socio-demographic differences were less marked than might be inferred from studies of specific social groups, indicating a need for caution in health education and health promotion practice against always assuming socio-demographic differences.

Background: There is a popular belief that out-of-home eating outlets, which typically serve energy dense food, may be more commonly found in more deprived areas and that this may contribute to higher rates of obesity and related diseases in such areas. Methods: We obtained a list of all 1301 out-of-home eating outlets in Glasgow, UK, in 2003 and mapped these at unit postcode level. We categorised them into quintiles of area deprivation using the 2004 Scottish Index of Multiple Deprivation and computed mean density of types of outlet (restaurants, fast food restaurants, cafes and takeaways), and all types combined, per 1000 population. We also estimated odds ratios for the presence of any outlets in small areas within the quintiles. Results: The density of outlets, and the likelihood of having any outlets, was highest in the second most affluent quintile (Q2) and lowest in the second most deprived quintile (Q4). Mean outlets per 1,000 were 4.02 in Q2, 1.20 in Q4 and 2.03 in Q5. With Q2 as the reference, Odds Ratios for having any outlets were 0.52 (CI 0.32–0.84) in Q1, 0.50 (CI 0.31 – 0.80) in Q4 and 0.61 (CI 0.38 – 0.98) in Q5. Outlets were located in the City Centre, West End, and along arterial roads. Conclusion: In Glasgow those living in poorer areas are not more likely to be exposed to out-of-home eating outlets in their neighbourhoods. Health improvement policies need to be based on empirical evidence about the location of fast food outlets in specific national and local contexts, rather than on popular 'factoids'.

Background Single questions on self-reported morbidity are commonly used in social or health surveys. It has been suggested that these may underestimate socioeconomic gradients in health because more disadvantaged social groups may have higher thresholds for defining illness. Method Face-to-face interviews by research nurses with community-based respondents in the West of Scotland, using a specially designed suite of prompts following up on responses to the UK General Household Survey (GHS) long-standing illness question. Participants were 858 respondents born in the early 1930s and 852 respondents born in the early 1950s (mean age at interview 59 and 40, respectively) classified by occupational social class and area deprivation. Results Adjusted for age and sex, the Relative Index of Inequality (RII) for reporting any condition in response to the GHS question was 2.14 (95% CIs 1.49–3.08) for social class and 2.01 (1.41–2.87) for Depcat. Among those not reporting any conditions to the GHS question, the RII for reporting conditions to any further prompts was 1.54 (0.87–2.70) for social class and 0.86 (0.50–1.46) for Depcat. The RIIs for reporting any condition after the initial question and all prompts were 2.16 (1.40–3.33) for social class and 1.50 (0.98–2.29) for Depcat. Across a range of conditions defined as more serious, and conditions classified by different ICD categories, socioeconomic status (SES) gradients after the initial question and all prompts were similar to, or less steep than, those produced by the GHS question alone. Conclusions These data do not support the hypothesis that poorer social groups are more stoical and more likely to need detailed prompting in order to elicit morbidity. Nor do they support the hypothesis that SES gradients in morbidity are underestimated by using the GHS question rather than more detailed questioning. This suggests that responses to this type of question can be used in epidemiology and health needs assessment without major socioeconomic bias.

Background Gender differences in health are commonly observed by epidemiologists. Little is known about lay beliefs concerning the gender patterning of common conditions. Methods Using the West of Scotland Twenty-07 Study, we analysed responses to a question in a postal questionnaire asking whether respondents thought men or women (or both equally) were more likely to have heart disease, cancer, mental illness, and accidents, to be fit, and to live longer. This question was answered by 466 females and 353 males, then aged 25, 45, and 65 yr. Results Responses were in general in accord with epidemiological findings, but females had significantly lower odds than males of perceiving men as being at greater risk of accidents and heart disease, and higher odds than males of perceiving women as being at greater risk of mental illness. Conclusions There was a tendency for each gender to think risks were higher for their own sex than did the other gender. This observation needs further exploration, particularly in the light of the research showing ‘optimistic bias’ in relation to health, and research suggesting that socioeconomically disadvantaged people may be least likely to perceive socially structured health inequalities.

Powerful, rich, and well educated people tend to live longer and healthier lives than their less advantaged counterparts. These socioeconomic inequalities in health have been observed in a range of societies—developed, developing, market led, welfare state, and communist. Their expression, however, may vary according to how the particular society is stratified—for example, by income or wealth in the United States, by social class in the United Kingdom, or by education in Europe. They occur across a wide range of causes of death and types of illness, have been observed since accurate statistics were first available, and seem to have been increasing.1 Several governments have recently proposed strategies to reduce socioeconomic inequalities in health.2–5 An issue rendering strategy development in this field difficult is that, although a lot of information is available about the magnitude and causes of socioeconomic inequalities in health, rather less information is available about the effectiveness of policies in reducing them.6 The recent Cross-Cutting review in England noted that intervention research is scanty compared with the much larger body of observational evidence that describes inequalities.5 This is shown by the fact that the review contains six boxed lists, containing between them 50 examples …

A postal questionnaire, with three follow ups, achieved a response rate of 50% (42% male, 58% female), from a random sample of 6500 adults from the electoral roll in eight local authority areas in the west of Scotland in 1997. We examined four domains of self assessed health: chronic, recent and mental health problems, and health in general, measured respectively by the presence/absence of limiting longstanding illness, the number of 20 common symptoms experienced during the past month, the depression subscale of the Hospital Anxiety and Depression Scale3 and perceived health over the past year as either excellent/good or fair/poor. Social class was based on own occupation, using registrar general's classification, income was household income adjusted for family composition.4 We excluded subjects (245) who reported they were economically inactive because of permanent sickness or disability (to reduce the possibility of reverse causation), and those with missing values on any of the independent, control, or dependent variables. As tenure, car access and health are associated with age, sex, and marital status, all models control for these variables. Dichotomous health variables were analysed using logistic regression, numerical ones using the GLM procedure in SPSS.