social capital and smoking
DESCRIPTION
An examination of the association between social capital and smoking behaviour in neighbourhoods in Caerphilly County Borough Dr. Anne Marie Cunningham December 2004 This dissertation is submitted in part fulfilment of the requirements for the degree of Masters in Public HealthTRANSCRIPT
Social Capital and Smoking
An examination of the association between social capital and
smoking behaviour in neighbourhoods in Caerphilly County
Borough
Dr. Anne Marie Cunningham
December 2004
This dissertation is submitted in part fulfilment of the requirements for the degree of
Masters in Public Health
Acknowledgements
I have been able to undertake this Masters in Public Health because of the Welsh
Assembly Government’s support for deprived communities in South Wales through
the Department of General Practice, Cardiff University. I hope that the experience I
have gained from this study will help me to contribute to the reduction of inequalities
in health in these areas. I am very appreciative of the opportunities available to
develop academic skills afforded by the foresight of those who have sought funding
for the programmes I and others are working within.
Shortly after I began working in Wales, Dr. John Watkins introduced me to the
Caerphilly Health and Social Needs Survey. I am grateful for our initial discussions
and his encouragement to study this area in greater depth.
My supervisor, Dr. David Fone, has been very patient and I am thankful for his
guidance whilst I worked on this topic.
I would like to thank friends and family for being there when needed and forgiving
my absence at other times. And last but not least, my warmest thanks to Chris for his
continual caring and kindness.
2
Abstract
The health risks of smoking have been established for over 50 years. Whilst rates of
smoking have fallen amongst those who are better off, prevalence remains high
amongst the poorest members of society and explains much of the socio-economic
inequalities in health. This work begins by discussing evidence that in deprived areas
levels of smoking are higher than might be expected based on the characteristics of
the individuals living there. Social capital, which describes the potential benefits - or
drawbacks - of social relations between individuals, has been suggested as a means of
explaining variation in health outcomes between places.
Although understanding smoking behaviour is key to understanding health
inequalities, little work examining the contribution of social capital to smoking had
been published at the time this dissertation was began. In line with the growth in
general social capital literature, twice as many publications have come available since
the work was started as were originally. This work has been reviewed and used to
provide a context for the analysis of the Caerphilly Health and Social Needs Survey
dataset constructed by others in 2001.
The analysis has shown that women in Caerphilly are more likely to smoke if they
live in areas where there are lower levels of attachment to the community and
increased neighbourhood problems. This however does not apply to men. Women
who have closer relationships with their neighbours are more likely to smoke than
those who are more isolated.
The implications and possible explanations of these findings are then explored.
Further work is needed to explain the mechanisms through which social networks and
the quality of the environment affect likelihood of smoking in women. This will
hopefully lead to policies and interventions that can effectively address the widening
inequalities in smoking prevalence in the UK and beyond.
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ACKNOWLEDGEMENTS...............................................................................................2ABSTRACT..................................................................................................................3
CHAPTER 1 INTRODUCTION................................................................................6
SMOKING....................................................................................................................6Why do people smoke?...........................................................................................6Association of smoking with deprivation...............................................................6Area-level studies...................................................................................................8Insights from Qualitative Studies.........................................................................10
SOCIAL CAPITAL.......................................................................................................11Definitions............................................................................................................11Social Capital and Health....................................................................................13Neighbourhood disorder and collective efficacy.................................................14Social capital and Inequalities in Health.............................................................17
SOCIAL CAPITAL AND SMOKING..............................................................................18Research Aim.......................................................................................................18Research Objectives.............................................................................................18
CHAPTER 2 BACKGROUND: THE CAERPHILLY HEALTH AND SOCIAL NEEDS SURVEY DATASET...................................................................................19
SETTING....................................................................................................................19SAMPLE.....................................................................................................................19QUESTIONNAIRE DESIGN...........................................................................................20SOCIAL CAPITAL INDICATORS..................................................................................20
CHAPTER 3 LITERATURE REVIEW..................................................................24
AIMS AND OBJECTIVES.............................................................................................24METHODS..................................................................................................................24RESULTS...................................................................................................................27
Overview..............................................................................................................27Datasets................................................................................................................29Outcome measures...............................................................................................30Independent Variables.........................................................................................30Social Capital Variables......................................................................................33Analytical Methods...............................................................................................38Results..................................................................................................................39
CONCLUSIONS...........................................................................................................41Association of Social Capital with Smoking........................................................41Limitations of Reviewed Studies...........................................................................42Comparability to the Caerphilly Dataset.............................................................42
CHAPTER 4 METHODS..........................................................................................44
STUDY POPULATION.................................................................................................44DEFINITIONS.............................................................................................................44
Outcome Variable................................................................................................44Independent variables..........................................................................................44
DESCRIPTIVE ANALYSIS...........................................................................................46
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Individual.............................................................................................................46Ecological............................................................................................................46
MULTIVARIATE ANALYSIS.......................................................................................47
CHAPTER 5 RESULTS............................................................................................48
DESCRIPTIVE STATISTICS..........................................................................................48Overall prevalence of smoking.............................................................................48Age........................................................................................................................49Socio-economic variables....................................................................................51Social Capital Variables and Area Level Income Deprivation............................56Continuous Social Capital and Area Level Income Deprivation Variables........61Ecological Bivariate Analysis..............................................................................63
MULTIVARIATE LOGISTIC REGRESSION ANALYSIS..................................................66Individual socio-economic variables, age and income deprivation.....................66Social Capital Variables......................................................................................69
CHAPTER 6 DISCUSSION......................................................................................78
COMPARISONS WITH EXISTING LITERATURE............................................................79Individual Determinants of Smoking....................................................................79Area Deprivation and Smoking............................................................................80Gender differences in associations between neighbourhood and health.............81Neighbourhood Quality........................................................................................81Neighbourhood Disorder.....................................................................................83Neighbourhood Belonging...................................................................................84Social Cohesion....................................................................................................84
STRENGTHS AND WEAKNESSES OF STUDY...............................................................87Survey Data..........................................................................................................87Analysis................................................................................................................89
CONCLUSION.............................................................................................................89SUGGESTED AREAS OF FURTHER RESEARCH...........................................................90IMPLICATIONS FOR POLICY.......................................................................................91
CHAPTER 7 REFERENCES...................................................................................92
APPENDIX A TABLES AND FIGURES..............................................................104
Appendix B Websites of Surveys included in Literature Review..............................106
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Chapter 1 Introduction
Smoking
Why do people smoke?
“Me nerves, Doctor; sort of helps me to think; can’t digest nothing otherwise; keeps down the weight; keeps up the weight; everyone else does it; habit I suppose; keeps the moth out of the carpet.” (Gordon 1954, cited in van Proosdij 1960;161)
Until recently, smoking has been seen as an individual behaviour (Jarvis and Wardle
1999). Many studies have focussed on determining the features of people who smoke
including their psychosocial characteristics such as coping styles, locus of control,
neuroticism, relationship difficulties and supportive resources (Billings and Moos
1983; Droomers et al. 2002; Stronks et al. 1997). Billings and Moos (1983) state that
“smokers are viewed as especially sensitive to stress, in part because they lack the
personal and social coping resources to adequately resolve the dysphoric states that
accompany stress”. Brandon (1994) reviewing the literature surrounding negative
affect and smoking found that smokers were twice as likely to be depressed as non-
smokers. Nicotine stimulates the autonomic nervous system, although many smokers
claim that smoking “calms their nerves”.
Jarvis and Wardle (1999) suggest that smokers may be blamed for their subsequent
health problems. If they do not give up their smoking habit then they are responsible
for the consequences. Butler et al. (1998) found that smokers were often reluctant to
discuss their habit in the doctor’s surgery for fear of being lectured to.
Association of smoking with deprivation
The move away from seeing smoking as a predominantly individual behaviour
occurred along with the renewed focus on socio-economic inequalities in health
(Bartley et al. 1998; Shaw et al. 1999; Townsend and Davidson 1982; Wilkinson
1996). The improvements in health gain for the UK population in the last century
6
have not been evenly distributed. Between 1931 and 1961 mortality rates fell by 39%
for men in social class I, but by only 6% for those in social class V (Lawlor et al.
2003).
Smoking is considered to be the health behaviour with the largest impact on these
health inequalities (DOH 1998). Although the prevalence of smoking has been
decreasing over the past fifty years (Peto et al. 2000) the difference in rates of
smoking between those with highest and lowest socio-economic status has markedly
increased (Jarvis and Wardle 1999; Lawlor et al. 2003; Marsh and McKay 1994).
Smoking became a common habit in the early 20 th century in the UK following the
introduction of mechanically produced cigarettes (van Proosdij 1960). In 1950 there
were similar rates of prevalence across social classes, with slightly greater levels of
smoking in higher social classes (Lawlor et al. 2003). In 1973, the poorest were about
60% more likely to smoke than the most affluent. By 1996, this differential had grown
so that the poorest had smoking prevalence more than four times higher than the most
affluent.
Individually, an increased risk of smoking is associated with lower social class, rented
tenure, lack of access to a car, unemployment, over-crowding, lower educational
attainment, being a lone parent, and being divorced or separated. Jarvis and Wardle
(1999) note that the odds of these individual risk factors taken together mean that an
unemployed, manual worker living in rented, overcrowded accommodation without
access to a car is 17.8 times more likely to smoke than a professional owner-occupier,
with degree level education who has a car.
Marsh and McKay’s study, “Poor Smokers” (1994) was an in depth investigation of
smoking among the socially disadvantaged. They note that although there is a similar
initiation rate to smoking for all income groups (around 75%), the differences in
prevalence result from half of those who are well off stopping whilst the poorest do
not. As a possible explanation for this they suggest that high proportions of
unqualified school-leavers in social housing estates in the 1970s allowed the
development of a “protective social habitat for high levels of smoking not seen
7
elsewhere since the 1960s” (Marsh and McKay 1994;78). They go on to state that
smoking and deprivation are so linked that “You can almost study social advantage
itself through variation in smoking prevalence” (Marsh and McKay 1994;78).
Although, Marsh and McKay seem to consider smoking almost a direct consequence
of deprivation, others do not agree. When investigating the causes of the excess
mortality found in poorer areas and the more north of England and Wales, Law and
Morris (Law and Morris 1998) found the higher prevalence of smoking explained the
majority of the differences. They also found that about 12% of the difference was due
to the “direct” influence of poverty, for example, through accidents, suicide or drug
misuse. However they specifically state, “smoking related diseases are related only
indirectly in that poor people smoke more”.
Area-level studies
The preceding paragraphs suggest that there may be ‘something’ about areas, such as
large swathes of social housing, that are conducive to smoking. There has recently
been a surge of interest in the connection between place and health (Diez Roux 2003;
Macintyre et al. 1993) and there have been several epidemiological studies looking at
this kind of ‘area effect’ and associations with smoking.
In the US, at a state-level, Colby et al (1994) sought to find an association between
‘social stress’ (measured by the State Stress Index- SSI) and smoking prevalence and
consumption. They found that the higher the SSI, the higher both measures of
smoking usage, even after adjusting for state-level measures of poverty, educational
attainment, ethnicity and rurality.
Diehr et al (1993), analysing evidence from a cross-sectional survey of fifteen
communities in the US, found that there were differences in community prevalence of
smoking not accounted for by several individual level characteristics. They did not
look at the impact of ‘area-level’ measures, but mentioned that an association had
been seen with the percentage of unemployed within a state.
8
In, the UK one of the first multi-level analyses of the relationship between ward
deprivation and smoking was carried out in North Thames by Kleinschmidt et al.
(1995). It had been common to use the Carstairs index as a surrogate measure of
health behaviour in small area studies looking at environmental pollutants, where
individual level data was not available. They validated this practice by showing that
area-level deprivation did predict smoking after adjusting for individual socio-
economic group. Their motivation had not been to show a contextual effect of area on
smoking per se.
Studies such as that in North Thames could be criticised for under specifying the
individual level co-variates. Significant area-level deprivation could be accounted for
by not including compositional attributes of the population such as education and
income, which are associated with smoking in the model. Later studies such as that of
Reijneveld (1998) in Amsterdam included individual level income, occupation and
education and still found differences in smoking status at the individual level
associated with area-level deprivation.
Duncan et al. (1999) in a re-analysis of the Health and Lifestyle Survey data from
1984/5, extensively described by Blaxter (1990), included many of the individual
level variables identified by Jarvis and Wardle (1999): age, gender, social class,
housing tenure, employment status and marital status. Adjusting for all these factors
in a multi-level model, a significant area based deprivation effect was seen. However,
compositional attributes and deprivation did not account for all of the variance
between wards. The authors suggest that future work should investigate the attributes
of neighbourhoods that are conducive to smoking, including environmental quality
and “social collectivities”.
9
Insights from Qualitative Studies
It has been established that it is individuals who are poor, and further poor individuals
in poor areas, who have the highest rates of smoking. But why might this be?
Qualitative research can help to explain the reasons why these epidemiological
pictures may arise.
Hilary Graham (1987; 1993) pioneered qualitative research investigating deprivation
and smoking. Her research on low income women caring for children established that
often women were using cigarettes as a way of dealing with the chronic stresses of
managing a family with few financial resources. In particular, they used smoking as
an escape and way of maintaining their identity. “Smoking a cigarette can be the only
purchase that women make, and the only activity that women do, just for themselves.”
(Graham 1987;55)
In a study of smokers in disadvantaged areas of Edinburgh, Bancroft et al (2003)
identified that smoking was often used as a way of dealing with feelings of stress and
boredom, as well as being necessary to satisfy cravings resulting from nicotine
dependence. This utilisation of cigarettes to cope with the lack of activity and
interaction associated with deprivation and unemployment may reflect a
phenomenological perspective mentioned by van Proosdij (1960). He refers to work
done by Linschoten on ‘the situation’ and reflects that:
“A person preoccupied with a problem can relegate the problem to the background by lighting a cigarette. In doing so he changes his situation and, at the same time, the landscape consisting of the situation around him.” (van Proosdij 1960;198)
In Glasgow, Stead et al. (2001) investigated the contexts and experiences of people in
social housing that may contribute to the very high smoking prevalence seen amongst
low-income families in these areas. They found through a series of focus groups in
three different housing “schemes” that residents had a strong sense of community, but
also felt excluded from wider society. High levels of crime and disorder, and lack of
10
amenities were features of every-day existence. They lived in an area where smoking
was the norm, and when exposed to anti-smoking norms outside the area, such as a
ban in cinemas, they were resentful. There were also behaviours associated with
smoking, such as pooling coupons, and being able to “tap money for a packet of fags”
(Stead et al. 2001;339), which demonstrate norms of trust and recipricocity.
In summary, people living in poverty may use smoking as a way of coping with the
stresses associated with life in poverty and in disadvantaged areas. The work of Stead
et al. further suggests that smoking may be part of the social fabric of life in these
communities.
Social Capital
Definitions
Szreter and Woolcock (2004) have stated that “ it seems likely that social capital is
destined to become, like ‘class’, ‘gender’, and ‘race’, one of the ‘essentially contested
concepts’ of the social sciences” (2004;654).
The basis for their concern about the clarity of the concept is the wide-ranging way in
which different disciplines and individual authors have discussed and defined social
capital. Without doubt there is much political interest (Blunkett 2002; Howson 2003;
Primarolo 1999) and the political scientist Robert Putnam is credited with bringing the
concept into popular usage (Schuller et al. 2000). He defined social capital as
“features of social life-networks, norms, and trust- that enable participants to act
together more effectively to pursue shared objectives” (Putnam 1996) and noted that
what might be achieved through social capital would not necessarily be
“praiseworthy”. Putnam however did little work on the theoretical development of the
concept. The first sociologists to use the term were Bourdieu (1986) and Coleman
(1986).
Pierre Bourdieu, a French sociologist, defined social capital as “the aggregate of the
actual or potential resources which are linked to potential of a durable network of
11
more or less institutionalised relationships of mutual acquaintance or recognition”
(Bourdieu 1986;249). He saw the ‘capital’ as a resource that could only be accessed
by individuals who were participating in networks.
Around the same time James Coleman, a sociologist with a particular interest in
education, stated that social capital was different to other forms of capital, such as
economic or human capital because it “inheres in the structure of relations between
actors and among actors”(Coleman 1988;S98). He describes three forms that these
relations may take: norms and sanctions, information flows and obligations and
expectations based on trust. Putnam based his analysis on the work of Coleman.
Bourdieu therefore saw social capital as a resource that is accessed by the individual
through networks whilst Coleman and Putnam describe it as existing within the
networks themselves. This distinction is crucial as it influences the way in which one
would seek to ascertain the social capital that a community has access to.
A further recent development has been the elucidation by Szreter and Woolcock of
three different kinds of social capital: bonding, bridging and linking (Szreter and
Woolcock 2004). Bonding social capital exists between individuals of similar status
who see themselves as sharing common identities, for example women in a mothers
and toddlers group. Bridging social capital relates to the networks that exist between
groups who have similar status but who have distinct identities, for example different
groups of workers in the Trades Union Congress. It is recognised that some
communities can be high in levels of bonding social capital but low in levels of the
bridging form, for example Northern Ireland society (McGrellis 2004). Linking social
capital exists across levels within a hierarchy, generally institutions and those they
serve. Szreter and Woolcock note the lack of this type of social capital may be most
important to those in deprived communities.
Collective efficacy is a related concept based on Bandura’s ideas of human agency. It
describes “a group’s shared belief in its conjoint capabilities to organize and execute
the courses of action required to produce given levels of attainment”(Bandura
12
1997;477). In contrast to social capital, which was originally measured in large areas
such as states, collective efficacy has been considered at the level of community or
neighbourhood. For this reason it is of particular interest and is discussed further.
Social Capital and Health
In the decades before the emergence of social capital, researchers were aware that
relationships and community could affect health. I will discuss two examples.
Longitudinal studies of the population in Alameda County, California found that
individuals who had more social contacts had decreased mortality(Berkman and Syme
1979). This relationship persisted after adjusting for socio-demographic factors and
health practices (including smoking). Although the more socially isolated were more
likely to smoke, as noted by Berkman et al. (2000). House and Kahn describe how
“the study of social support emerges, seemingly out of nowhere, during the 1970s”
(1985;83). House formulated a typology of “social support” which postulated the kind
of benefits that may be available to individuals through supportive relationships with
others.
Earlier than Alameda, the “Roseto effect” was used to describe the phenomenon of a
close-knit Italian-American community in 1950’s Pennsylvania which had much
lower levels of heart disease than nearby towns although individuals had high levels
of behavioural risk factors (Egolf et al. 1992). The investigators concluded that the
differences were due to the family-centred, cohesive nature of the community in
Roseto. They also predicted that as the town became “Americanised” the society
would break down and a corresponding rise in heart disease would occur. This was
observed in the subsequent decades. A local cardiologist (Wolf) began the study after
a chance remark about the low incidence of cardiovascular disease by a family
physician in the area (Ruberman 1993). There were criticisms of methodological
issues around the work and the general plausibility of its claims, however Ruberman
states that Wolf and his colleague, Bruhn, were “among the first to popularise the idea
that social factors are related to death from heart disease” (Ruberman 1993;669).
13
Whilst Alameda County showed how individuals could benefit from social networks
and social support, the Roseto story was of a community that seemed to produce
better health, and this did not receive so much attention. Hawe and Shiell (2000) note
how researchers interested in public capital (Kawachi et al. 1997; Lomas 1998;
Wilkinson 1996) have more recently “retold” the Roseto story.
Neighbourhood disorder and collective efficacy
Sampson and Groves described social disorganization as the “inability of a
community structure to realize the common goals of its residents and maintain
effective social controls” (Sampson and Groves 1989;221). As such it seems to
describe lack of social capital as described by Putnam. With other colleagues,
Sampson later developed the concept of collective efficacy(see Table 1-1) that is
defined as “social cohesion among neighbours combined with their willingness to
intervene on behalf of the common good” (Sampson et al. 1997).
Sampson went on to explicitly distinguish his construct of collective efficacy from
social capital, partly because “unfortunately, over time the concept of social capital
has come to mean many different things” (Sampson et al. 1999;634). He sees
collective efficacy as a measure of community agency, whilst social capital relates to
structural (community) resource, and notes that “resources or networks alone (e.g.
voluntary organizations, friendship-ties, organisational density) are neutral- they may
or may not be effective mechanisms for achieving an intended effect”(Sampson et al.
1999;635).
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Table 1-1 Sampson's Collective Efficacy Scale
Social Control Would you say it is likely that neighbours could be counted to intervene if:
Children were skipping school and hanging out on a street cornerChildren were spray-painting graffiti on a local buildingChildren were showing disrespect to an adultA fire broke out in front of your houseThe fire station closest to your home was threatened with budget cuts
Social Cohesion How strongly do you agree:People around here are willing to help their neighboursThis is a close knit neighbourhoodPeople in this neighbourhood can be trustedPeople in this neighbourhood generally don't get along with each other (reverse coded)People in this neighbourhood do not share the same values (reverse coded)
Source: Sampson et al. (1997)
Ross et al (2000) sought to investigate the relationship between area-level deprivation,
disorder and mental health. They developed an instrument to measure neighbourhood
disorder that consisted of fifteen items and covered perception of crime, social and
physical disorder and social cohesiveness (Ross and Mirowsky 1999;see Table 1-2).
They found support for their social isolation hypothesis, which stated that in poor
communities increased neighbourhood stability would be associated with higher
levels of distress, because social ties would only protect against disorder in more
affluent communities. The authors suggest that the higher levels of disorder in poorer
communities mediate the relationship between deprivation and poorer mental health.
Residential stability increases distress in poorer neighbourhoods because, with little
chance of leaving, people feel trapped in a stressful environment. Since the Ross-
Mirowsky Neighborhood Disorder scale (see Table 1-2) simultaneously measures
perceived disorder and social cohesion, it cannot distinguish between them.
Figure 1-1 shows what I consider to be possible links between the models of Sampson
and Ross in establishing how crime and disorder are related to deprivation and health.
15
Table 1-2 Ross-Mirowsky Perceived Neighborhood Disorder Scale
There is a lot of graffiti in my neighborhood.My neighborhood is noisyVandalism is common in my neighborhoodThere are a lot of abandoned buildings in my neighborhoodThere is too much alcohol use in my neighborhoodThere is too much drug use in my neighborhoodThere are too many people hanging around on the streets near my homeThere is a lot of crime in my neighborhoodI'm always having triuble with my neighborsMy neighborhood is cleanPeople in my neighborhood take good care of each otherMy neighborhood is safePeople in my neighborhood watch out for each otherThe police protection in my neighborhood is adequateI can trust most people in my neighborhood
Source: Mirowsky and Ross (1999)
Figure 1-1 Links between the models of Sampson and Ross
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Social capital and Inequalities in Health
Debate surrounds the relationship between social capital and health, partly because
low levels of social capital have been suggested as an explanation for the inequalities
in health discussed earlier. There are essentially two schools of thought on the causes
of the widening health outcomes of the poor and the rich in the UK and other
developed countries. These will be referred to as the ‘neomaterial’ and ‘psychosocial’
arguments.
The psychosocial case states that inequalities in health are related to the disparity in
income between the richest and poorest in a society, which produces feelings of
anxiety and stress responses in the poor. It builds on the work of Marmot that showed
loss of control in work lead to increased deaths due to cardiovascular disease in the
Whitehall studies of civil servants (Brunner and Marmot 1999). Richard Wilkinson in
his book, “Unhealthy Societies: The Affliction of Inequality” (1996) is credited with
bring Putnam’s concept of social capital to a wider health audience (Szreter and
Woolcock 2004). Wilkinson saw falling levels of social capital as being an
intermediary in the psychosocial argument. This was supported by the work of
Kawachi and colleagues that demonstrated a relationship between social capital (trust
and participation levels) and health, first at the ecological (Kawachi et al. 1997), then
individual level (Kawachi et al. 1999) in the US.
Neo-materialist critics have suggested that apparent associations between stress and
health outcomes may be due to confounding (Macleod et al. 2001). They believe that
the poorest have worse health because they have less access to resources including
health services (Muntaner et al. 2000). They agree that income inequality may lead to
less trust and social cohesion in society but the cause of ill health in the poor (Lynch
2000). Moreover, they are concerned that deprived communities with low levels of
cohesion will be blamed for their poor health. Lynch and Muntaner contend that
rather than introducing policies to reduce income inequality in response to
Wilkinson’s theory, UK and US governments have sought to increase social cohesion
without the redistribution of wealth (Muntaner and Lynch 1999).
17
Social Capital and Smoking
In summary, rates of smoking are higher in individuals living in difficult socio-
economic circumstances and higher again in deprived communities. This may be
because living in a poor area increases one’s material disadvantage and restricts
access to services, or because a concentration of poor people creates a ‘social miasma’
effect (Sloggett and Joshi 1994) whereby there is less social support and increased
stressors at the community level. This work will use the Caerphilly Health and Social
Needs Survey Dataset, constructed in 2001, to begin to address these issues.
Research Aim
To examine the influence of individual and contextual factors, including social
capital, on smoking behaviour in Caerphilly Borough, South Wales.
Research Objectives
1. To review the literature on the association between social capital and smoking.
2. To assess the association between individual factors (age, education,
household income, employment status, social class and housing tenure),
contextual factors (social capital and deprivation) and smoking behaviour in
men and women in Caerphilly using the Caerphilly Health and Social Needs
Survey data set
3. To identify areas for further research and the policy implications of findings.
18
Chapter 2 Background: The Caerphilly Health and
Social Needs Survey Dataset
Setting
The county borough of Caerphilly is located in South East Wales. The Borough has a
large ageing population living predominantly in small towns and villages, numbering
around 50 in total. The settlement pattern is typical of an ex-coal mining area. In
1950, 29 pits employed 24,000 people in the area. However, by November 1990 the
last pit had closed (Gough 2000). The borough has higher levels of economic
inactivity due to disability and sickness than average in Wales (Richardson 2003).
Sample
The Caerphilly Health and Social Needs Survey was a self-completing cross-sectional
survey of the adult resident population of Caerphilly Borough, conducted in 2001. It
was carried out by the Caerphilly Research Collaboration (Department of Public
Health of the former Gwent Health Authority and Caerphilly Borough Council) with
the aim of obtaining non-routine social, environmental, lifestyle and health status data
at electoral ward level. The Survey was Stage 3 of a four-stage process. Overall, the
aim of the Caerphilly Health and Social Needs Study is to increase understanding of
the wider determinants of health in Caerphilly county borough, and thus to inform and
enable the development of local strategies to improve health and guide allocation of
resources.
The sampling frame was the population registered with a GP practice as recorded by
the EXETER system held by the former Gwent Health Authority. A random sample
of 22,290 residents, stratified by electoral ward, were sent a postal questionnaire and
followed up by electoral canvassers. Questionnaires had a unique identifier, which
enabled responses to be matched to postcode, and allocated to electoral wards and
enumeration district
19
The sample size was based on an estimated response rate of 60%. The actual response
rate was 63%. Overall, the female response rate was significantly higher than the
male response rate (67% vs. 58%), and there was an increase in response rate with
age. As expected, the response rate in electoral wards was negatively correlated with
Townsend Deprivation index, that is more deprived wards had lower response rates.
Full details of response bias are given in Fone et al. (2003)
Questionnaire design
The questionnaire was a 20 page document. It had six sections.
1. “ You and your lifestyle”, including questions on height and weight, smoking,
alcohol consumption, diet, physical activity.
2. “Your health” including limiting long-term illness, chronic diseases, accidents
and injuries, general health status and locus of control.
3. “You and your neighbourhood” which were used to generate measures of
social capital.
4. “You and your job”, which established employment-status, occupation (to
derive Registrar General social-class), and educational-qualifications.
5. “Your home”: included questions on housing-tenure and conditions.
6. “Your income” asked about gross household income.
Social Capital Indicators
The section of the questionnaire entitled “You and Your Neighbourhood” included
two questions that evaluated aspects of social capital.
The first question was a modified version of Buckner’s Neighbourhood Cohesion
Index (Buckner 1988). Buckner used the term “social cohesion” to describe the
aggregate of psychological sense of community. Lochner et al. (1999) refer to three
concepts which have elements of social cohesion: collective efficacy, psychological
sense of community and neighbour-hood cohesion. Sampson et al. (1997) developed
their own index of social cohesion to measure collective efficacy in communities, as
described earlier (see Table 1-1). The Sampson index assessed trust in others and
20
shared values, rather than associations and friendships as he does not believe that this
is an important dimension (Sampson 2004).
Buckner’s Index, originally containing eighteen items, has been validated in Canada
(Robinson and Wilkinson 1995) where one item was found be less useful. Ellaway et
al. (2001) used the Canadian version modified version in work in Glasgow. Gatrell et
al. (2000) use ten of the items in a study in Lancaster and Salford.
The Caerphilly Study uses fourteen of the original eighteen items and an additional
item: “Overall, I think this is a good place to bring up children”. This item has also
been used in other studies (Hutchinson 2001). Factor analysis by the Caerphilly
research team showed that these items described two different aspects of relationships
within the neighbourhood, which they designated ‘social cohesion’ and
‘neighbourhood belonging’. The items contributing to each component are shown in
Tables 2-1 and 2-2.
The question asked “how much do you agree with the following statements about
your neighbourhood?” Responses were given on a five-point Likert scale ranging
from ‘strongly disagree’ to ‘strongly agree’. Possible scores ranged from 8-40 for
social cohesion, and 7-35 for neighbourhood belonging, with higher scores
representing a more positive perception of the neighbourhood.
21
Table 2-3Items contributing to Social Cohesion component
Social Cohesion
I visit my friends in their homes The friendships and associations I have with other people in my neighbourhood mean a lot to me If I need advice about something I could go to someone in my neighbourhood I believe my neighbours would help in an emergency I borrow things and exchange favours with my neighbours I would be willing to work together with others on something to improve my neighbourhood I rarely have a neighbour over to my house to visit *I regularly stop and talk with people in my neighbourhood * Reverse coded
Table 2-4 Items contributing to Neighbourhood Belonging component
Neighbourhood Belonging
Overall, I am attracted to living in this neighbourhood I feel like I belong to this neighbourhood Given the opportunity, I would like to move out of this neighbourhood *I plan to remain a resident of this neighbourhood for a number of years I like to think of myself as similar to the people who live in this neighbourhood Living in this neighbourhood gives me a sense of community
Overall I think this is a good place to bring up children
* Reverse coded
The second question asked “in this area how much of a problem are the following…?”
The items had previously been used in work in Glasgow (Macintyre et al. 2000;
Sooman and Macintyre 1995). Factor analysis by the Caerphilly research team
divided the items into two components ‘neighbourhood disorder’ and ‘neighbourhood
quality’. Neighbourhood disorder here refers to the perception of crime and drug use
in the area. Neighbourhood quality includes some of the minor incivilities assessed
by the Ross-Mirowsky Neighbourhood Disorder index (see Table 1-2) along with the
perception of other “quality of life” issues such as noise, uneven pavements and
nuisance from dogs. The items contributing to each component are shown in Tables
2-3 and 2-4.
22
The Likert scale for responses had 3 options: “a serious problem”, “some problem” or
“not a problem”. Scores for neighbourhood disorder ranged from 5-15, and from 7-21
for neighbourhood quality, with higher scores representing more favourable
perceptions of the area.
Table 2-5 Items contributing to Neighbourhood Disorder component
Neighbourhood Disorder
Vandalism Assaults and muggings Burglaries Discarded needles and syringes Walking around after dark
Table 2-6 Items contributing to Neighbourhood Quality component
Neighbourhood Quality
Litter and rubbishDisturbance by children or youngsters Speeding traffic Uneven or dangerous pavementsNuisance from dogs Lack of safe places for children to play Noise
23
Chapter 3 Literature Review
Aims and Objectives
The aim of the literature review is to establish the extent of previous work studying
the links between social capital indicators and smoking behaviour in population based
studies. The review shall provide a context for the analysis of the Caerphilly dataset.
Particular emphasis shall be placed on the following areas:
Social capital indicators used and the justification for them.
The form of analysis: individual only or including area-level measures.
Results showing the relative influence of social capital indicators to
established demographic and socio-economic indicators on smoking
behaviour.
Methods
A literature search of the Medline, Cinahl, Embase, HMIC and PsycINFO databases
was undertaken through the OVID interface using the following search terms: social
capital, social cohesion, social network, neighbourhood disorder, collective efficacy
and social disorder. The results are shown in table 3-1.
Included studies met the following criteria:
1. Smoking tobacco as a dependent variable
2. Study population is a random sample of the general population, aged over 16.
3. Independent variables include measures of social capital
4. Published between 1966 and present.
Studies were excluded if they looked only at subsections of the populations, e.g. older
people, pregnant women, adolescents or had outcome measures such as smoking
cessation and not smoking prevalence, as these although of interest would not be
directly comparable to the proposed work on the Caerphilly dataset.
24
The following journals were also searched through their online publications: Journal
of Epidemiology and Community Health, British Medical Journal, Tobacco Control,
American Journal of Public Health, International Journal of Epidemiology, Health and
Place, and Social Science and Medicine. This was particularly useful since corrected
proofs of forthcoming publications are now available for several journals , sometimes
before they have been entered into databases such as Medline.
To identify papers in the “grey literature”, an Internet search was carried out using the
Google search engine, using the search terms. The SIGLE (System for Information on
Grey Literature in Europe) was also searched.
Specific searches were made of the following websites:
http://www.dh.gov.uk/Home/fs/en Department of Health UK
http://www.official-documents.co.uk/index.html The Stationery Office (UK
Government publications)
http://www.hda-online.org.uk/ Health Development Agency
http://www.wales.gov.uk/index.htm National Assembly for Wales
http://www.scotland.gov.uk/topics/?pageid=1 Scottish Executive
http://www.statistics.gov.uk/ Office for National Statistics
http://www.ni-assembly.gov.uk/ Northern Ireland Assembly
http://www.who.int/en/ World Health Organization
References cited in identified papers were searched for papers that matched the entry
criteria. This did not reveal any additional studies.
25
Table 3-7 Results of Database Search
Search History Results
1 Social capital.mp 1164
2 Social cohesion.mp 503
3 Social network$.mp 5067
4 Neighbourhood disorder.mp 2
5 Collective efficacy.mp 232
6 Social disorder.mp 115
7 1 or 2 or 3 or 4 or5 or 6 6968
8 Smoking.mp 164584
9 7 and 8 253
10 Remove duplicates from 9 163
11 Limit 10 to English language 149
12 Limit 11 to abstracts 145
26
Results
Overview
There was a high false positive rate since many retrieved studies included smoking as
a confounding variable when investigating the relationship between health outcomes
and social capital rather than having smoking as the outcome variable. Six studies
were identified which met the inclusion criteria. Demonstrating that this is an
evolving field or ‘hot topic’, all of these publications were in the last five years, and
four were in the duration of the dissertation.
The OVID search identified two published papers: Lindstrom et al (2003) and
Patterson et al (2004). The online journal search found the Parkes and Kearns paper
(in press), although an earlier form of it had been identified on the ESRC Centre for
Neighbourhood Research web page through a general internet search.
The other three studies are from the ‘grey literature’ and commissioned by UK
government agencies. Two relevant studies were found on the Health Development
Agency’s website (Cooper et al. 1999; Pevalin and Rose 2003) and the work of
Boreham et al (2002), commissioned by the Department of Health, was identified on
the website of the Stationary Office. Although these researchers are independent
academics, the political nature of social capital creates the possibility of bias in these
reports.
Although smoking was an outcome variable in all the studies it was the exclusive
focus of only two of the reports (Lindstrom et al. 2003; Patterson et al. 2004). The
relationship of social capital indicators with general health outcomes and other health-
related behaviours is beyond the scope of this review. All of the studies could be
considered exploratory in that they were not testing a specific theory that may relate
smoking to social capital. A summary of the study characteristics and results is given
in Table 3-2.
27
Table 3.2 Summary of population studies investigating relationship between social capital indicators and health.
Author, Year, Dataset / source (year) Sample size and Study Design Indicator of Social Capital Results
Country Unit of Analysis
Lindstrom et al , Malmo public health survey, 3,393 adults Multi-level I. Social participation Increased individual participation
2003, Sweden Sweden (1994) within 77 model A. Not entered associated with decreased smoking
neighbourhoods Cross-sectional Contextual effect not examined
Patterson et al, SHAPE (Survey of the Health 10,062 adults Marginal I. Social cohesion, safety Increased social cohesion at both
2004, US of Adults, the Population within 19 model (home and neighbourhood) levels associated with lower smoking
and the Environment) geographical areas Cross-sectional A. Social cohesion, safety with decreased smoking. Safety at
Hennepin County, Minnesota (1998) (home and neighbourhood) area level also decreased smoking.
Parkes and Kearns, Scottish Household Survey (2001) 9593 adults Single level I. Various measures of social and Neighbourhood problems and
in press, UK model physical environment and disputes, disliking appearance and poor
Cross-sectional facilities and services facilities-increase smoking
Social support not related
Cooper et al, Health and Lifestyle Study (1992) and HALS- 5007 adults Single level I.HALS. Perception of neigh- Positive perception of neighbourhood
1999, UK Health Survey for England (1993&1994) HSE- 32,374 adults models bourhood and civic engagement associated with lower smoking in women only
Cross-sectional HSE. Social support (emotional) Civic engagement decreased smoking for both
Lack of emotional support increased smoking
in men and women
Boreham et al, Health Survey for England (2000) 7988 adults Multi- level I. Emotional support, contact with For women, severe lack of emotional support,
2002, UK model friends and family, trust, lack of contact with family, low levels of trust,
(unspecified) participation and low participation with increased smoking
Cross-sectional A. Neighbourhood problems and For men, low participation increased smoking.
access to services For men and women, higher contact with friends
increased smoking
Pevalin and Rose, British Household Panel Survey Varies from 8000 to Single level I. Various measures Social participation and low levels of contact
Note : I refers to individual level variables and A to area-level variables. Marginal models are individual logistic regressions where a correction is made for the clustering effect in the area-level variables.
28
Table 3.2 Summary of population studies investigating relationship between social capital indicators and health.
2003, UK (pooled data from 9 waves) 85,000 person years model with friends decreased smoking. High levels of
for different variables. crime increased smoking.
Note : I refers to individual level variables and A to area-level variables. Marginal models are individual logistic regressions where a correction is made for the clustering effect in the area-level variables.
29
Datasets
The study populations varied from 3,393 to over 30,000. As might be expected these
surveys were not conducted with the sole aim of examining the relationship between
social capital and smoking. In fact, the majority of studies use secondary analyses of
existing data sets of ongoing national or local government surveys. Of these, two
relate specifically to health: the Health Survey for England (HSE) and the Survey of
the Health of Adults, the Population and the Environment (SHAPE) in Hennepin
County, Minnesota, USA. Two surveys in the UK cover more general aspects of
social and economic life, but include questions relating to health: British Household
Panel Survey (BPHS) and the Scottish Household Survey (SHS). The purpose of the
Health and Lifestyles Survey (HALS) in 1984/5 was specifically to investigate the
relationship between health, health-related behaviour, social circumstances and beliefs
and attitudes. A follow-up of the original participants was performed in 1991/2. The
Malmo Public Health Survey in 1994 appears to have been a one-off study.
The earliest work by Cooper et al. (1999) used HSE and HALS data from the early
1990s before social capital was routinely examined in health surveys. The indicators
in these datasets tend to be the least specific to social capital, although they both
include measures of social support. This work and that of Pevalin and Rose (2003)
was carried out as part of a Health Development Agency (then Health Education
Authority) programme investigating the relationships between social capital and
health. The data used by Pevalin and Rose (2003) is pooled from nine waves of the
BPHS. In different waves different social capital indicators were used.
The 2001 SHS and 2000 HSE were specifically designed to address neighbourhood
characteristics and social capital respectively. They are therefore richer sources.
Unfortunately the SHS sampling structure did not allow construction of neighbour-
hood level variables.
30
Response rates, when given, are above 65%. However, studies that have pooled data
from two or more years of the survey have not cited response rates (Cooper et al.
1999; Pevalin and Rose 2003).
Outcome measures
The Swedish study was the only one to dichotomise smoking behaviour into “daily”
or “not daily”. The authors have also examined the relationship between social capital
and intermittent smoking elsewhere (Lindstrom 2003; Lindstrom and Ostergren 2001)
and stated that the sample size in this study was too small to analyse intermittent
smokers separately. The other five studies included examined “current smoker” vs.
“not current smoker”.
Independent Variables
Individual
All studies included age, gender and socio-economic variables. Cooper et al. (1999)
and Boreham et al. (2002) performed separate analyses by gender. The socio-
economic measures varied and are summarised in Table 3-3. The study that adjusts
least for socio-economic status is that of Lindstrom et al (2003). They were concerned
about over-adjusting for individual level confounding. They also state that education
and socio-economic status correlate highly in Malmo.
Area Level
Patterson et al. (2004) use two categorical measures of area deprivation constructed
from survey data: concentration of poverty and concentration of low education. Only
4.4% of the respondents reported low education, defined as less than high school
education. Areas with <4.4% of residents reporting this had low concentration, >4.7%
were high, and the rest as medium. Since the mean number of respondents in each
area was 530, this variable seems prone to sampling error.
31
Boreham et al (2003) used the Townsend Index of the postcode sector of residence.
Postcode sectors are comparable in size to electoral wards (Twigg 1999). They were
used in this instance as the design involved selecting 19 households in a sample of
630 postcode sectors throughout England.
32
Table 3-3 Summary of Independent Variables in included studies
Author, Year, Socio-economic and other variables
Country Individual Area
Cooper et al, Social class None
1999, UK PDI score (1)
Living arrangement (2)
General health (2)
Chronic ilness (2)
Perceived stress (2)
Employment Status (3)
Boreham et al, Social class Townsend Index
2002, UK Household income
Educational qualifications
Car ownership
Pevalin and Rose, Employment status Region of residence2003, UK Highest level of education
Marital statusSocial class
Race
Lindstrom et al , Educational status Not entered.
2003, Sweden Country of Origin
Patterson et al, Educational Status Concentration of poverty
2004, US Fanily Income Concentration of low education
Race
Health insurance
Parkes and Kearns, Housing tenure None
in press, UK Vehicle for household use
Marital statusSingle parent
Employment status
Financial Stress
Notes: (1) In the HSE dataset Personal Deprivation Index consisted 6 items:no central heating/telephone/car, home not owned, unemployed (rather than economically inactive) and income support received by anyone in household.The HALS PDI index did not include unemployment.(2) HSE only (30 HALS only
33
Social Capital Variables
As can be seen from Table 3-2, a wide range of measures of social capital has been
used in the studies. The social support variables are not true measures of social
capital, and the authors recognise this. The other variables used are: trust, social
participation, social cohesion or neighbourliness, neighbourhood problems, including
crime and feelings of safety, and contact with friends and family. These shall be
discussed separately. All studies except Patterson et al. (2004) construct categorical
variables from the social capital indicators.
Trust
Trust in others is central to Putnam’s definition of social capital (Putnam 1996). The
HSE 2000 dataset had a question on trust taken directly from the US General Social
Survey. It was used to assess social capital by Kawachi et al (1999; 1997).
Social Participation and Interaction
Measures of organizational membership have become associated with social capital
through Putnam’s work (Schuller et al. 2000). Several studies have included this
dimension of social capital (Boreham et al. 2002; Cooper et al. 1999; Lindstrom et al.
2003; Pevalin and Rose 2003). Items are summarised in table 3-4.
It is the only indicator of social capital used in the Swedish study and the authors state
that this index has been shown to measure different aspects of the “psychosocial
environment than other social network and social support variables” (Lindstrom et al.
2003;445). The Swedish index assesses a very wide range of social participation,
including family activities and the attendance of cultural activities. One of the
measures does not involve direct contact with another individual (writing a letter to a
paper). The index seems to cover elements of civic-mindedness and general
sociability. Although I would agree that it does not assess social support, it does seem
to measure some aspects of social networks.
34
The British indices tend to relate to concepts of civic engagement and general
association membership. It is worth noting that Catherine Campbell is doubtful of the
significance of association membership as a measure of social capital in communities
in the United Kingdom (Campbell 2000). Qualitative work in Luton (Campbell et al.
1999) found that most residents had little time for organised activities and most of
their social networks revolved around informal associations with friends and family.
This may explain why in the seven waves of the BHPS which included social
participation, between 48% and 53% of the population reported not being involved in
one of the organisations listed (Pevalin and Rose 2003).
Table 3-4 Social Participation Variables in Included Studies
Author, Year, Items assessing social participation/
Country civic engagement
Cooper et al, adult education or night-class course; voluntary group or local community group;
1999, UK community or religious activities
HALS (attended or participated in any past two weeks or not)
Boreham et al, political groups; trade unions; environmental groups; parent/school associations; 2002, UK residents' groups/neighbourhood watch; education/art/music group or evening class; religious
group/church; elderly person's group; youth group; women’s group; social/working men's clubs.(regularly attended or participated any or not)
Lindstrom et al , study circle/ course at workplace; other study circle/course; union meeting; meeting of other
2003, Sweden organizations; theatre/cinema; arts exhibition; church; sports event; letter to editor of a news-
paper/journal; demonstration; night club/entertainment; big gathering or relatives; private party
(low participation- less than 3 in past 12 months)
Pevalin and Rose, political party; trade union; environmental group; parents' association; tenants' group;
2003, UK religious group; voluntary group; other community group; social group; sports club; Women's
Institute; women's group; other organisation; professional organisation; pensioners' organisation; Scout/Guides organisation.
(active in any or not )
35
Social Cohesion
The BPHS uses eight of the fifteen items of Buckner’s “Neighborhood Cohesion
Index”. Although McCulloch gives additional background in a preliminary analysis
(McCulloch 2003), details are not given for why these items were chosen. Patterson et
al. (2004) use another index, the “Social Support Index”. It seems to cover similar
constructs to that of the Buckner Index. The HALS dataset (Cooper et al. 1999)
included some elements of social cohesion in their general assessment of “social
capital”. Parkes and Kearns (in press) identified several items from the Scottish
Household Survey that cover elements of social cohesion. They did not use an index
measure and the impact of each question on smoking behaviour is reported separately.
Details of the social cohesion indices and questions are given in Table 3-5.
36
Table 3-5 Details of social cohesion indices in included studies.
Author, Year, Items assessing social cohesion/belonging or neighbourliness
Country
Cooper et al, Is it a place you enjoy living in?
1999, UK Is it a place where you personally feel safe?*
Is it a place where neighbours look after each other or not?
Has it good facilities for young children or not?*
Has it good local transport or not?*
Has it good leisure facilities for people like yourself or not?*
Pevalin and Rose, I feel like I belong to this neighbourhood
2003, UK The friendship and associations I have with other people in my neighbourhood mean a lot to me
If I need advice about something I could go to someone in my neighbourhood
I borrow things and exchange favours with my neighbours
I would be willing to work together with others on something to improve my neighbourhood
I plan to remain a resident of this neighbourhood for a number of years
I like to think of myself as similar to those people who live in this neighbourhood
I regularly stop and talk with people in this neighbourhood
Patterson et al, People can depend on each other in this community
2004, US Living in this community gives me a secure feeling
People here know they can get help from the community if they are in trouble
This is not a very good community to bring up children in
There is a feeling in this community that people should not get too friendly with each other
If I had an emergency even people I do not know in this community would be prepared to help
Parkes and Kearns, Spoke with neighbour in last fortnight
in press, UK Expect to get help from a neighbour
Like people in area
Feel involved in community
Note: * These items do not relate specifically to social cohesion. Cooper et al. described all six items as assessing 'social capital' in the area.
37
Neighbourhood Disorder
The relationship between neighbourhood disorder and social capital has previously
been discussed in the introductory chapter. Higher levels of neighbourhood disorder
are associated with poverty and decreased levels of collective efficacy (Sampson
2003). The summary of questions investigating perceptions of crime and disorder in
the area are given below in Table 3-6. The items relating to physical and social
disorder are similar to those used in the Chicago Studies (Sampson and Raudenbush
1999; Sampson et al. 1997;see Table 1-1) and the Ross-Mirowsky Neighbourhood
Disorder Scale (Ross and Mirowsky 1999;see Table 1-2).
Boreham at al. (2003) aggregate scores for their “neighbourhood disorder” index at
postcode sector level to allow an area analysis.
Table 3-6 Assessment of Neighbourhood Disorder in included studies
Author, Year, Variables assessing neighbourhood problems and
Country feelings of safety/ perception of crime
Boreham et al, Index measure of: rubbish; vandalism; teenagers hanging around; not like living in area*; noisy2002, UK neighbours/loud parties; not feeling safe walking alone after dark; problem of drunks/tramps;
neighbours not looking after each other*; not having good local transport*.
Pevalin and Rose, Index measure of: graffiti; teenagers hanging around; drunks/tramps on street; vandalism;
2003, UK racial insults/attacks; cars stolen/broken into; people attacked on street.
Concern about crime.Concern about walking alone after dark.
Patterson et al, During the past year have you restricted your activities because you do not feel safe
2004, US (1) in you home? (2) in your neighbourhood?
Parkes and Kearns, Experience of one or more crime
in press, UK Safe to walk in the neighbourhood in the evening (safe/not safe)
Disputes with neighbours or not
Index measure of: noisy neighbours; vandalism; groups of young people; drink/drug abuse;
rubbish or litter
Note: * These items do not relate to the construct of safety/crime but were included in Boreham et al.'s Index.
38
Analytical Methods
The method chosen to analyse the data is determined by the theoretical perspective
explaining the links between social capital and health, and by the limitations of the
datasets.
Single level logistical regressions
Three of the studies used this form of analysis. Parkes and Kearns (in press) are
explicit in stating that they were not able to obtain area-level measures because the
sampling strategy of the Scottish Health Survey does not allow the estimation of
reliable area level estimates.
Cooper et al. state that social capital is a community resource and that they are
measuring the “individual’s perception of social capital in their neighbourhood, rather
than an area level characteristic of the surrounding environment” (Cooper et al.
1999;62). But they felt that their study added to the ecological work published at that
time.
Pevalin and Rose (2003) state that although some see social capital as a characteristic
of collectives rather than individuals, they see it a property of the individual and the
community. They also draw attention to the use of proxies for social capital, and
suggest that at the very least the individual measure may assess access to the
collective resources, which presumably differs with social and demographic variables.
Multi-level models
Boreham et al. (2002), Patterson et al. (2004) and Lindstrom et al (2003) use models
that allow area-level characteristics to be assessed. The Swedish study used a full
multilevel model that would have allowed the analysis of contextual and individual
variable simultaneously. However, they did not enter any contextual variables as all
the individual-level variables explained the inter-neighbourhood variance. Patterson et
al (2004) used a ‘marginal hierarchical multi-level model’. This is a refinement of the
analysis that to be performed on the Caerphilly dataset, where mean area level
39
variables are constructed but there is adjustment for clustering of data within areas.
Boreham et al. (2003) state that they used a multi-level model but do not give details.
Results
Socio-economic variables
All of the studies found associations between the socio-economic variables and
smoking. Pevalin and Rose (2003) state that the major determinants of smoking are
the ‘structural’ socio-economic variables.
Patterson et al. (2004) do not give details of the adjusted odds for the socio-economic
variables. Details were available in a report on the Hennepin County website (Mara
and Rak 1999). Results of the associations of the area-level deprivation indices with
smoking in the multivariate analysis are not given.
In the final model in the HSE 2000 analysis (Boreham et al. 2002), deprivation
measured by the Townsend index was more strongly associated with smoking in men
than women.
Trust
Low levels of trust were associated with an increase in smoking for women only
(Boreham et al 2002). No theoretical basis for this association is given.
Social Participation and Interaction
Pevalin and Rose (2003), Cooper et al. (1999) and Lindstrom et al. (2003) found that
increased social participation was associated with a decreased likelihood of smoking
in men and women at the individual level. Boreham et al. (2002) found that low social
participation only increased likelihood of smoking in men.
Informal interaction with friends was also a significant predictor of smoking in some
studies. Pevalin and Rose (2003) found that less contact with friends increased the
40
chances of smoking. They state that this "“appears couther-intuitive as the socially
isolated are thought more likely to smoke”. Boreham et al. (2002) also found that men
and women who had higher levels of contact with friends had increased likelihood of
smoking. However, they do not report this in their text; this significant result appears
in a table only. Less contact with family members was associated with increased
smoking in women. Alternatively, Parkes and Kearns (in press) found no significant
association with levels of contact with friends or family.
Social Cohesion
Cooper et al. (1999) found that higher levels of social capital, as measured by their
general variable (see Table 3-5), were only associated with decreased smoking in
women. Pevalin and Rose (2003) did not find any association in the full model,
although there was a significant bivariate association.
In Parkes and Kearns (in press) study, those who had some sense of involvement in
the community had a reduced odds of smoking compared to those who felt no
involvement. They also report the significant interaction that community involvement
was less likely to be associated with decreased smoking in social renters compared to
other tenures. Contact with a neighbour, or the expectation of help from a neighbour
was not significantly associated with smoking.
Patterson et al. (2004) found that social cohesion as assessed by the individual and at
the aggregate level was associated with decreased likelihood of smoking. They
recognise that they cannot determine the reason for the association from this data but
hypothesise that the effect may be through greater information sharing about the
negative impacts of tobacco, or the reduction of distress by “strengthening
psychological resources”.
Neighbourhood disorder
In the BPHS dataset, Pevalin and Rose (2003) found that higher perception of crime
was associated with increased likelihood of smoking at the individual level. Parkes
41
and Kearns (in press) found those who had experience of crime were more likely to
smoke, as were those who had had a serious dispute with their neighbours or who
perceived that problems such as vandalism were more common in their area. In
addition the authors noted interaction effects: high levels of neighbourhood problems
were less likely to be associated with smoking in social renters compared to other
tenures, and in women compared to men.
Boreham et al. (2002) found a non-significant association with the ‘neighbourhood
problems’ variable; the trend was towards decreased odds of smoking in the areas
with higher levels of problems.
In Minnesota, Patterson et al. (2004) found area level measures of feeling safe at
home or in the neighbourhood, but not individual measures, were associated with
decreased odds of smoking.
Conclusions
Association of Social Capital with Smoking
Several of the social capital indicators were found to be associated with likelihood of
smoking. However, the associations with socio-economic variables were stronger. Of
the peer-reviewed publications, Parkes and Kearns (in press) were only able to assess
relationships at the individual level and this limited their work. Patterson et al. (2004)
report area effects of social cohesion and feelings of safety on smoking. Lindstrom et
al. (2002) found that most of the difference in smoking rates between areas was
accounted for by the educational level of residents, although higher levels of social
participation made a small reduction in odds of smoking. They did not investigate
contextual effects as no significant neighbourhood variance was left after accounting
for individual variables. They consider this their most significant finding as it
suggests that smoking behaviour is individually determined rather than through
contextual processes.
42
Limitations of Reviewed Studies
Several of the studies identified in this review are hampered by the fact that they are
secondary analyses of existing datasets that were not specifically constructed to
examine the links between social capital and health. The Health Survey for England
2000, the Scottish Household Survey 2001 and some waves of the British Household
Panel Survey were expressly conducted to examine social capital or neighbourhood
indicators. Although large datasets these did not allow for the measurement of social
capital at an aggregate level in a meaningful way, although Boreham et al. (2003) did
attempt this, and so the analyses rest on how individual perceptions of the local area,
or constructs such as trust and participation relate to smoking behaviour. Further,
these works are exploratory in nature and the authors, if they attempt to, have
difficulty in constructing narratives for the associations observed.
This review highlighted the difficulties in assessing social capital. Even when studies
were attempting to assess similar constructs such as “low social participation”, and
found significant associations with smoking, their results may not be comparable. For
example, to be defined as having low participation in the HALS dataset one would not
have attended a night class, community or religious activities, or a voluntary or
community group in the past two weeks. In the Swedish survey, you would not have
low participation if you had gone to the theatre, been to a friend’s birthday party and
went nightclubbing the last three months. These are obviously measuring different
constructs. Even, within the analysis of recent UK datasets the same definitions of
social participation were not used. Boreham et al (2002) excluded participation in a
sports club from the analyses, but it was included by Pevalin and Rose (2003).
Comparability to the Caerphilly Dataset
The Caerphilly dataset does not contain some of the social capital indicators used in
these studies. There is no assessment of formal social participation or civic
engagement, which has been linked to smoking in several of the studies examined.
However, the “social cohesion” component assesses the informants’ experience of
43
neighbourliness within the community and elements of informal interaction with
neighbours.
Generalised trust is not directly measured, however the social cohesion component
also assesses respondents trust in their neighbours in several items. In addition,
components such as “neighbourhood disorder” and “neighbourhood quality” are
comparable to some of the measures in reviewed studies.
The Caerphilly dataset will be more capable of examining the relationship between
social capital and smoking than most of the studies reviewed because of the large
sample size distributed over a comparatively small geographical area. It will therefore
be possible to construct aggregate neighbourhood-level variables from the social
capital indicators, and so examine the contextual effects of social capital, in addition
to individual perceptions. Only Patterson et al (2004) and Lindstrom et al. (2003) had
similarly strong datasets.
44
Chapter 4 Methods
Study Population
The study is based on the analysis of the Caerphilly Health and Social Needs Survey
dataset described in Chapter 2. In this analysis 12,038 of the 12,408 valid responses
will be used. 316 were excluded because the responses were missing the unique
identifier and so could not be allocated to an enumeration district. 55 were excluded
because there was no information on smoking status. This represents 97% of the final
study sample.
The survey sampling strategy was stratified using the 36 electoral wards in Caerphilly
County Borough. These contain 325 enumeration districts which are small
geographical areas containing on average 500 residents and although also dependent
on administrative boundaries may be closer approximations to neighbourhoods. They
will be used as the ecological unit throughout the analysis.
Definitions
Outcome Variable
Responses have been dichotomised into current smoker (daily and occasional
smokers) and non-smoker (ex-smoker and never smoked). This dichotomy was used
in five of the six studies reviewed in Chapter 3.
Independent variables
Individual socio-economic and demographic
The following variables were used:
Age. In the descriptive analysis, the relationship of age and smoking will be
examined by using ten-year age bands. In the multivariate analysis it will be
assessed as a continuous variable.
Gender.
45
Educational status was categorised into “high” (equivalent to A-level and
above), “medium” (qualifications below A-level), and “low” (no
qualifications).
Social class was analysed in the following categories: I & II; III Non-
manual ;III Manual; IV & V and other. Merged categories were used because
of small numbers in social classes I and V.
Employment status was categorised as: employed; unemployed (and seeking
employment); Home maker/ carer; disabled; retired or student.
Household income was analysed using three categories: less than £95 per
week; between £95 and £215 per week; more than £215 per week The lowest
income group is below the threshold for obtaining means-tested benefits and
all except the highest income group are judged as being below the level
defined as equating to poverty (60% of median income).
Housing Tenure. Responses were dichotomised into “Home owner” and
“Non-home owner”.
Individual Social Capital Variables
The dataset included pre-constructed variables for neighbourhood belonging; social
cohesion; neighbourhood quality and neighbourhood disorder. The individual score
for each component is the sum of scores for each item in Tables 2-1 to 2-4. In the
multivariate analyses these will be entered as continuous variables so that the data is
maximised. The descriptive analysis also uses the score divided by tertiles (the 33 rd
and 66th centile) to give high, medium and low categories for each component.
Area Deprivation
The Townsend Index of Deprivation was not available at enumeration district (ED)
level from the 2001 Census, as EDs were not used. Area income deprivation was
therefore assessed by CACI Paycheck data. This uses census and market research data
to model pre-tax household income at postcode level (CACI 2004). The statistic used
was the percentage of households per ED with an annual income of less than £10,000,
which is 60% of the median income in Wales.
46
Area Social Capital Variables
The dataset contained the mean score at enumeration district level for each of the
social capital components. Again, these were used as a continuous variable in the
multivariate analysis, but are divided into tertiles in the individual descriptive
analysis.
Descriptive Analysis
Individual
Individual and area level social capital scores were divided into tertiles to give high,
medium and low categories of neighbourhood quality, neighbourhood disorder,
neighbourhood belonging and social cohesion. The high category for neighbourhood
disorder indicated low reported neighbourhood problems.
All analyses were undertaken separately for men and women. The proportion of
respondents who reported current smoking was calculated for the following
categorical variables: 10 year age bands, educational status, employment status,
social class, housing tenure, household income, area income deprivation, and the
social capital variables at area and individual levels. Confidence intervals for these
proportions were calculated using Newcombe’s method (1998). Unadjusted odds
ratios are given for each of these individual variables. Confidence intervals were
calculated for odds ratios with SPSS v11.5, by using the enter procedure with
individual logistic regressions.
Ecological
For each enumeration district the proportion of respondents who reported smoking
was calculated. From here on this will be referred to as prevalence. In bivariate
analysis, correlation coefficients were calculated between the following variables:
percentage smoking, income deprivation, social cohesion, neighbourhood belonging,
neighbourhood quality and neighbourhood disorder.
47
Multivariate Analysis
For the dichotomous outcome, smoker or not, stepwise logistic regressions using the
“forward conditional” mode of variable selection in SPSS v11.5 were performed
separately for men and women. The base model included the five individual
categorical socio-economic variables and age as a continuous variable. First, area
income deprivation was added to the base model to establish if there were contextual
effects of deprivation on individual smoking behaviour. Social capital and income
deprivation were treated as continuous variables in the analyses.
The following sequence of models was then carried out separately for each of the
social capital indicators.
Model 1: Base model + Individual Social Capital Score
Model 2: Model 1 + ED Social Capital Score
Model 3: Model 2 + ED Income Deprivation
48
Chapter 5 Results
Descriptive Statistics
Overall prevalence of smoking
The overall prevalence of smoking in Caerphilly Borough is 27.2% (Table 5-1). This
is similar to the overall smoking prevalence in the UK in 2001 (ONS 2004). In the
second Welsh Health Survey in 1998, 26.2% of the population reported current
smoking, whilst in Caerphilly 27.2 % did (NAW 1999). In the UK as a whole there is
higher smoking prevalence in men (28%) compared to women (26%), whilst no
significant difference was found in Caerphilly. The 2004 Welsh Health Survey reports
that more women under 45 smoke than men (NAW 2004). This was also found in this
study.
Table 5-8 Frequency of smoking with gender
Total Non-smoker Smoker % Smoker Odds ratio (95% CI) (95% CI) Gender Male 5324 3857 1467 27.6 (26.4-28.8) 1
Female 6714 4896 1818 27.1 (26.0-28.2) 0.98 (0.90-1.06)
Total 12038 8753 3285 27.2 (26.5-28.1)
Within enumeration districts (ED), the proportion of respondents reporting smoking
ranged from 0 to 72%. When EDs with less than 25 respondents (13% of respondents)
were excluded this ranged from 4 to 65%.
Table 5-9 Number of respondents and proportion smokers per enumeration district (ED)
Mean SD Median Inter-quartile Minimum Maximum Range
Number of respondents 37.0 18.0 34 24, 45 4 132% Smokers 28.0 11.6 26.3 20.2, 35.2 0 72.2
49
Age
Figure 5-1 shows smoking prevalence for males and females by 10-year age bands.
This shows the reducing prevalence of smoking with increasing age. This is in
keeping with previously reported studies in the UK (NAW 2004; ONS 2004). Table
5-3 gives unadjusted odds ratios for smoking, and shows that the odds ratio for
smoking in females over 35 is significantly reduced compared to the reference group
(aged 18-24). Only men over age 55 having a significantly lowers odds of smoking
than the reference group.
This is unlikely to be because of cohort effects with regards to smoking initiation, as
the prevalence of smoking in all age groups was higher over the last 40 years (ONS
2004). It may reflect success at stopping smoking with continued attempts as one gets
older. There may also be a significant ‘survival’ affect, with higher mortality amongst
smokers, leading to increased numbers of non-smokers in older age groups. With
cross-sectional data definite conclusions cannot be drawn.
50
Figure 5-2 Prevalence of smoking by Age Group (with 95% CI)
Table 5-10 Unadjusted odds ratios for smoking by 10 year age band
Female Male Odds ratio 95% CI Odds ratio 95% CI
Lower Upper Lower Upper
18-24 1.00 1.0025-34 0.83 0.66 1.03 0.94 0.72 1.2335-44 0.75 0.60 0.93 0.81 0.63 1.0545-54 0.69 0.56 0.86 0.92 0.72 1.1955-64 0.69 0.55 0.87 0.74 0.57 0.9665-74 0.43 0.34 0.55 0.53 0.40 0.6975+ 0.23 0.17 0.30 0.37 0.27 0.52
51
Socio-economic variables
Figures 5-2 to 5-6 show prevalence of smoking with employment status, social class,
household income, housing tenure and level of education. Table 5-4 shows the
unadjusted odds ratios (with 95% CI) for smoking of these variables. As expected,
there is a strong association between these variables and likelihood of smoking.
Within the ordinal variables there is a decrease in smoking prevalence with higher
levels of education, income and social class. For men, the increase in smoking with
lower social class is more marked than for women. This is perhaps dependent on the
higher prevalence of smoking for women than men in the reference category, social
class I and II (see Fig. 5-3). Although there is also a higher prevalence of smoking for
women than men in social class IV and V, the proportion of men smoking in the
poorest households is significantly higher than of women. Those who are retired are
significantly less likely to smoke which reflects the relationship with age already
discussed. Homeowners are half as likely to smoke as non-home owners and this
relationship is similar for men and women.
52
Table 5-11 Unadjusted odds ratios for smoking by individual-level socio-economic variables
Female Male Odds ratio 95% CI Odds ratio 95% CI
n Lower Upper Lower Upper
EducationHigh 2827 1.00 1.00Medium 3332 1.28 1.09 1.50 1.61 1.35 1.92Low 4446 1.55 1.33 1.81 1.74 1.48 2.04Missing 1433 1.37 1.13 1.65 1.82 1.44 2.29Employment StatusEmployed 5559 1.00 1.00Unemployed 290 2.11 1.45 3.09 2.91 2.14 3.96Student 194 0.86 0.54 1.35 0.44 0.24 0.81Home/carer 823 1.95 1.65 2.31 1.85 1.17 2.92Disability 1388 1.58 1.32 1.88 1.75 1.46 2.08Retired 2994 0.59 0.51 0.69 0.63 0.54 0.75Missing 790 1.06 0.86 1.31 1.29 1.00 1.67Social ClassI&II 2576 1.00 1.00IIINM 2231 1.03 0.87 1.23 1.43 1.11 1.86IIIM 2389 1.50 1.20 1.89 1.78 1.49 2.12IV&V 2936 2.02 1.71 2.38 2.16 1.80 2.61Other 772 2.31 1.87 2.86 2.15 1.54 3.01Missing 1134 1.26 1.02 1.56 1.70 1.31 2.21Household IncomeMore than £215 per week 5305 1.00 1.00Between £95 and £215 per week 4559 1.55 1.37 1.75 1.47 1.29 1.68Less than £95 per week 1249 1.72 1.45 2.04 2.51 2.03 3.10Missing 925 0.95 0.76 1.19 0.92 0.71 1.19Housing TenureOwner 9436 1.00 1.00Not owner 2355 2.51 2.22 2.85 2.59 2.24 3.00Missing 247 1.31 0.91 1.89 1.66 1.09 2.53
53
Figure 5-3 Prevalence of smoking by employment status (with 95% CI)
Figure 5-4 Prevalence of smoking by social class (with 95% CI)
54
Figure 5-5 Prevalence of smoking by household income (with 95% CI)
Figure 5-6 Prevalence of smoking by housing tenure (with 95% CI)
55
Figure 5-7 Prevalence of smoking by educational status (with 95% CI)
56
Social Capital Variables and Area Level Income Deprivation
Individual Categorical
Although the social capital measures will be entered into the logistic regressions as
continuous variables, the following is an analysis of scores at individual and area
levels divided into tertiles as described in the methods section.
There is an increase in smoking prevalence with lower scores for perception of
neighbourhood quality (Figure 5-7). This relationship is stronger for women, with
significant differences in prevalence for the three categories of neighbourhood
quality. The odds of smoking for a woman having a low or medium score are
significantly higher than for those in the reference group (Table 5-5). For men it is
only those in the lowest group who have significantly higher odds.
For neighbourhood disorder, only those with scores in the lowest tertile have
significantly higher odds of smoking and this is reflected in the prevalence of
smoking in the three groups.
Neighbourhood belonging is also associated with smoking prevalence for women, but
more weakly than disorder and quality. For men the relationship is unclear, with those
in the highest and lowest tertiles having higher smoking prevalence than those in the
middle tertile.
For social cohesion this ambiguous relationship is repeated for men and for women.
Those in the highest and lowest tertiles have higher odds of smoking than those in the
middle tertile. For women the highest prevalence of smoking is found in those scoring
most highly for social cohesion.
57
Figure 5-8 Prevalence smoking by individual social capital categories (with 95% CI)
59
Table 5-12 Unadjusted odds ratios of smoking for individual and area level social capital categories and area level income measures.
Female Male Odds ratio 95% CI Odds ratio 95% CI
Lower Upper Lower Upper
Ind Neighbourhood High 1.00 1.00Quality Medium 1.30 1.13 1.50 1.10 0.94 1.28
Low 1.76 1.53 2.02 1.32 1.13 1.53Missing 1.13 0.90 1.42 1.06 0.80 1.42
Ind Neighbourhood High 1.00 1.00 Disorder Medium 1.04 0.90 1.19 1.17 1.00 1.36
Low 1.41 1.22 1.62 1.18 1.01 1.38Missing 0.99 0.80 1.23 1.02 0.77 1.36
Ind Neighbourhood High 1.00 1.00Belonging Medium 1.13 0.98 1.11 0.86 0.73 1.00
Low 1.28 1.12 1.47 1.09 0.94 1.27Missing 1.00 0.80 1.26 0.89 0.66 1.20
Ind Social High 1.00 1.00Cohesion Medium 0.79 0.69 0.91 0.83 0.71 0.98
Low 0.91 0.80 1.04 1.00 0.87 1.17Missing 0.76 0.62 0.94 0.89 0.70 1.14
ED Neighbourhood High 1.00 1.00Quality Medium 1.39 1.21 1.59 1.21 1.04 1.41
Low 1.98 1.73 2.27 1.53 1.32 1.78ED Neighbourhood High 1.00 1.00Disorder Medium 1.21 1.05 1.39 1.09 0.94 1.27
Low 1.80 1.57 2.05 1.52 1.31 1.77ED Neigbourhood High 1.00 1.00Belonging Medium 1.19 1.04 1.37 1.04 0.90 1.21
Low 1.83 1.61 2.09 1.34 1.16 1.56ED Social High 1.00 1.00Cohesion Medium 1.22 1.07 1.40 1.16 1.00 1.35
Low 1.43 1.25 1.63 1.24 1.07 1.44ED Low income Low 1.00 1.00
Medium 1.18 1.03 1.36 1.35 1.16 1.57High 1.97 1.72 2.25 1.53 1.32 1.78
60
Area Categorical
At enumeration district (ED) level there continues to be an association between social
capital indicators and smoking prevalence, however the relationships are stronger for
women than for men.
Those areas with the lowest scores in all social capital indicators have the highest
smoking prevalence (Fig 5-8). The difference between those in the highest and middle
tertiles is not as large as between the middle and lowest. This is particularly true for
neighbourhood disorder and neighbourhood belonging. For women, the relationships
are stronger than for males.
For women in EDs with the highest neighbourhood quality scores, there is a smoking
prevalence of 21%, whilst in the lowest tertile it is 34%. This compares to a similar
difference for the EDs with the lowest and highest concentrations of low household
income (21% and 35%).
These differences in prevalence in smoking are reflected in the odds ratios for
smoking determined by the area level measures (see Table 5-5). The area level social
capital indicators are more strongly related to odds of smoking than the individual
measures. Women in EDs with the lowest neighbourhood belonging scores have 1.8
the odds of smoking of those in the EDs with the highest levels. This compares to the
odds ratio of 1.3 for women with the lowest individual neighbourhood belonging
scores compared to those with the highest.
ED level neighbourhood quality produces the largest difference in odds of smoking
for men and for women. The difference between those in the highest and lowest
tertiles is slightly greater than the difference is odds between the EDs with the lowest
and highest concentrations of low household income.
61
Figure 5-9 Prevalence of smoking by ED level social capital and income deprivation (% households with income <£10000/yr) (with 95% CI)
62
Continuous Social Capital and Area Level Income Deprivation Variables
Table 5-6 shows descriptive statistics for the continuous individual and area level
variables used in the multivariate analysis. There are differing numbers of missing cases
for each of the social capital variables, with the highest number missing for social
cohesion. The distribution of scores for men and women are similar.
Table 5-13 Descriptive statistics for continuous variables used in multivariate analysis.
N Missing Mean SD MedianInter-
quartile Min. Max. Range
Female ScoreNeighbourhood Quality 6220 494 14.8 3.3 15 12,17 7 21Neighbourhood Disorder 6148 566 12.2 2.1 12 11,14 5 15Neighbourhood Belonging 6222 492 26.3 6.1 27 23,31 7 35Social Cohesion 6080 634 29.3 5.5 30 26,33 8 40
Male ScoreNeighbourhood Quality 5039 285 14.8 3.3 15 12,17 7 21Neighbourhood Disorder 5019 305 12.2 2.1 13 11,14 5 15Neighbourhood Belonging 5061 263 26.1 5.9 27 22,30 7 35Social Cohesion 4912 412 29.1 5.4 29 26,33 8 40
ED ScoreNeighbourhood Quality 14.8 1.4 14.8 13.8, 15.8 10.9 18.2Neighbourhood Disorder 12.1 0.9 12.3 11.5, 12.8 9.3 14.4Neighbourhood Belonging 26.1 2.1 26.3 24.9, 27.6 18.5 30.5Social Cohesion 29.1 1.3 29.1 28.3, 29.9 25.2 32.4Low Income (percent households 31.3 13.0 30.3 21.9, 39.8 3.6 73.7with income less than £10,000 per year)
63
Tables 5-7 and 5-8 show the Pearson correlation coefficients for the continuous variables
used in the logistic regressions. Distributions approximate sufficiently to normal. All
correlations are highly significant. At individual level, neighbourhood belonging only
correlates strongly with social cohesion and vice versa, whilst at ED level neighbourhood
belonging has strong correlations with all other social capital indicators, particularly
neighbourhood disorder. The area income deprivation variable has highest correlation
with neighbourhood quality.
Table 5-14 Pearson correlation coefficients for individual social capital indicators
Neighbourhood Social Neighbourhood Neighbourhood Belonging Cohesion Quality Disorder
Neighbourhood Belonging 1.00 Social Cohesion 0.66 1.00 Neighbourhood Quality 0.29 0.27 1.00 Neighbourhood Disorder 0.09 0.09 0.60 1.00
All significant at 0.001 level
Table 5-15 Pearson correlation coefficients for ED continuous variables
Neighbourhood Social Neighbourhood Neighbourhood Low Belonging Cohesion Quality Disorder Income
Neighbourhood Belonging 1.00 Social Cohesion 0.71 1.00 Neighbourhood Quality 0.65 0.33 1.00 Neighbourhood Disorder 0.63 0.36 0.81 1.00 Low Income -0.51 -0.35 -0.62 -0.52 1.00
All significant at 0.001 level
64
Ecological Bivariate Analysis
Figures 5-9 to 5-13 are scatter plots (with Pearson correlation coefficients and
significance levels) showing the association of ED social capital and income deprivation
indicators with the prevalence of smoking in the 325 EDs. The scatter reflects the
variability from small numbers of respondents in some enumeration districts. All
correlations are highly significant. The highest coefficients are seen for neighbourhood
quality and income deprivation, which are both strongly correlated together (see Table 5-
8).
Figure 5-10 ED Neighbourhood quality vs. smoking prevalence. r= -0.55 p<0.001
65
Figure 5-11 ED Neighbourhood disorder vs. smoking prevalence. r=-0.40 p<0.001
Figure 5-12 ED Neighbourhood belonging vs. smoking prevalence. r=-0.49 p<0.001
66
Figure 5-13 ED Social cohesion vs. smoking prevalence. r=-0.28 p<0.001
Figure 5-14 ED Income deprivation vs. smoking prevalence. r= 0.52 p<0.001
67
Multivariate Logistic Regression Analysis
Individual socio-economic variables, age and income deprivation
Six variables were entered into the initial model (see Table 5-. Five of these are individual
categorical socio-economic variables (education level, employment status, social class,
household income and housing tenure) with age entered as a continuous variable. There
are differences between the models for females and males.
Women with a low level of education have 1.7 times the odds of smoking of highly
educated women. This is also a significant predictor for men, but with less difference
between the groups. Household income and social class have larger influences on
smoking for men than women. For women there seems to be a threshold effect for
income, whilst men show a stepwise increase in likelihood of smoking with decreasing
income.
Women who are at home as carers are more likely to smoke than those employed outside
the home, whilst men are not. On the other hand, men who are unemployed and seeking
employment are more likely to smoke that those employed. Housing tenure is still the
strongest predictor of smoking status. Non- homeowners have around twice the odds of
smoking of owner-occupiers.
Model 1 shows the effect of the introduction of area income deprivation onto the model.
As described in the methods section, “forward conditional” was initially used to select
variables to enter the model. In this case, area deprivation was not added for men, as it did
not produce a significant change to the model. Table 5-9 shows forced entry of area
deprivation into the male model. It is non-significant and produces little, if any, change to
the odds ratios for the other variables.
68
Table 5-16 Logistical regression of smoking on individual socio-economic factors, age and ED income Deprivation (showing 95% CI for odds ratio).
Female Model 0 Female Model 1 Male Model 0 Male Model 1OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Lower Upper Lower Upper Lower Upper Lower Upper
EducationHigh 1.00 1.00 1.00 1.00Medium 1.18 0.99 1.41 1.16 0.97 1.39 1.29 1.06 1.56 1.29 1.06 1.56Low 1.71 1.39 2.10 1.64 1.34 2.01 1.48 1.20 1.83 1.47 1.19 1.82Missing 1.44 1.12 1.86 1.38 1.07 1.78 1.62 1.21 2.17 1.61 1.20 2.17
Social ClassI&II 1.00 1.00 1.00 1.00IIINM 0.83 0.69 1.00 0.82 0.68 0.99 1.17 0.89 1.54 1.17 0.89 1.54IIIM 1.08 0.84 1.38 1.06 0.83 1.36 1.33 1.09 1.62 1.32 1.08 1.61IV&V 1.29 1.06 1.57 1.25 1.03 1.52 1.31 1.05 1.64 1.31 1.05 1.63Other 1.15 0.88 1.50 1.12 0.86 1.47 0.86 0.57 1.28 0.86 0.57 1.28Missing 1.20 0.91 1.58 1.17 0.89 1.54 1.10 0.80 1.51 1.09 0.80 1.51
Household Income per week>£215 1.00 1.00 1.00 1.00£95- £215 1.33 1.15 1.54 1.29 1.11 1.50 1.31 1.10 1.56 1.31 1.10 1.56<£95 1.35 1.09 1.67 1.32 1.06 1.63 1.66 1.27 2.16 1.66 1.27 2.16Missing 0.95 0.74 1.21 0.91 0.71 1.17 0.86 0.65 1.15 0.86 0.65 1.14
Housing TenureOwner 1.00 1.00 1.00 1.00Not owner 1.87 1.62 2.15 1.70 1.46 1.96 1.98 1.68 2.34 1.96 1.65 2.32Missing 1.30 0.86 1.95 1.28 0.85 1.92 1.67 1.04 2.69 1.67 1.04 2.69
Employment StatusEmployed 1.00Unemployed 1.21 0.81 1.82 1.23 0.82 1.85 1.59 1.13 2.24 1.59 1.13 2.24Student 0.57 0.35 0.93 0.58 0.36 0.94 0.32 0.17 0.62 0.32 0.17 0.62Home/carer 1.22 1.01 1.48 1.21 0.99 1.46 1.00 0.61 1.64 1.00 0.61 1.63Disability 1.21 0.98 1.49 1.19 0.96 1.46 1.33 1.06 1.68 1.33 1.06 1.67Retired 0.74 0.60 0.91 0.73 0.59 0.91 0.72 0.56 0.91 0.72 0.56 0.91Missing 1.07 0.82 1.40 1.05 0.80 1.38 1.08 0.79 1.47 1.08 0.79 1.47
Age 0.98 0.97 0.98 0.98 0.97 0.98 0.98 0.98 0.99 0.98 0.98 0.99
ED Income Deprivation 1.01 1.01 1.02 1.00 1.00 1.00
69
In the female model, area income deprivation produced a significant improvement in the
model. It reduces the odds ratios for smoking for women in lower social classes, with low
income, who are disabled and who are not owner-occupiers, relative to the reference
categories. For a 1-point rise in the percentage of low-income households in a
neighbourhood, the odds of smoking for a woman in that area increase by 1%. Across the
inter-quartile range this will produce an increase in odds of smoking of 1.30 (95% CI,
1.19-1.42).
To check the robustness of the finding that area deprivation did not affect smoking in
men, a separate analysis was undertaken using Ward-level Townsend Index as an area-
deprivation measure, with electoral wards as the ecological unit. This was also not
significant.
70
Social Capital Variables
Separate models were run for each of the social capital variables because of the
significant correlations between them: neighbourhood quality, neighbourhood disorder,
neighbourhood belonging and social cohesion. The following process was followed.
Model 0: Individual socio-economic variables and age (as in Table 5-9).
Model 1: Model 0 and Individual level social capital variable.
Model 2: Model 1 and ED level social capital variable.
Model 3: Model 2 and ED level income deprivation variable
The forward conditional selection procedure was used to select variables to add to the
model. In the male models none of the social capital variables at individual or area level
produced significant improvements to the models, so they were not added. Therefore,
only the additional female models are described.
71
Neighbourhood Quality
Table 5-10 shows that perception of neighbourhood quality was significant at the
individual and area levels in the initial models. When ED deprivation is added in Model
3, neighbourhood quality remains significant at both levels. However, the aggregate level
variable is more strongly associated with smoking status than the individual level
variable. The decrease in odds of smoking across the interquartile range of individual
scores is 0.90 (95% CI, 0.81-0.99), whilst across the interquartile range of ED scores it is
0.82 (95% CI, 0.73-0.92).
In summary, a woman who lives in an area generally perceived to be of poor quality has
an increased risk of smoking after adjusting for the level of poverty in the area. In
addition, women who notice problems in their area have a slightly increased risk of
smoking compared to those who do not.
72
Table 5-17 Logistic regressions of smoking in women on individual socio-economic variables, age, area level deprivation and neighbourhood quality variables (showing odds ratio and 95% CI limits).
Model 1 Model 2 Model 3 OR 95% CI OR 95% CI OR 95% CI
Lower Upper Lower Upper Lower UpperEducationHigh 1.00 1.00 1.00Medium 1.18 0.99 1.41 1.18 0.98 1.41 1.16 0.97 1.39Low 1.71 1.39 2.10 1.66 1.35 2.05 1.62 1.31 2.00Missing 1.44 1.12 1.86 1.42 1.10 1.85 1.37 1.05 1.78Social ClassI&II 1.00 1.00 1.00IIINM 0.83 0.69 1.00 0.82 0.68 1.00 0.82 0.68 0.99IIIM 1.08 0.84 1.38 1.09 0.84 1.40 1.08 0.83 1.39IV&V 1.29 1.06 1.57 1.30 1.06 1.58 1.27 1.04 1.55Other 1.15 0.88 1.50 1.13 0.85 1.50 1.11 0.84 1.47Missing 1.20 0.91 1.58 1.22 0.91 1.64 1.21 0.90 1.61Household IncomeMore than £215 per week 1.00 1.00 1.00Between £95 and £215 per week 1.33 1.15 1.54 1.28 1.10 1.49 1.25 1.07 1.45Less than £95 per week 1.35 1.09 1.67 1.28 1.03 1.61 1.25 1.00 1.57Missing 0.95 0.74 1.21 0.95 0.73 1.23 0.92 0.71 1.19Housing TenureOwner 1.00 1.00 1.00Not owner 1.87 1.62 2.15 1.86 1.60 2.17 1.77 1.52 2.06Missing 1.30 0.86 1.95 1.43 0.89 2.28 1.41 0.88 2.26Employment StatusEmployed 1.00 1.00 1.00Unemployed 1.21 0.81 1.82 1.26 0.83 1.91 1.31 0.86 1.98Student 0.57 0.35 0.93 0.50 0.30 0.83 0.51 0.30 0.85Home/carer 1.22 1.01 1.48 1.19 0.98 1.45 1.18 0.97 1.44Disability 1.21 0.98 1.49 1.18 0.95 1.46 1.15 0.93 1.43Retired 0.74 0.60 0.91 0.72 0.57 0.90 0.72 0.58 0.90Missing 1.07 0.82 1.40 1.16 0.87 1.55 1.15 0.86 1.53
Age 0.98 0.97 0.98 0.98 0.97 0.98 0.98 0.97 0.98
Ind Neighbourhood Quality 0.96 0.94 0.98 0.98 0.96 1.00 0.98 0.96 1.00ED Neighbourhood Quality 0.87 0.83 0.91 0.89 0.84 0.94ED Income Deprivation 1.01 1.00 1.01
73
Neighbourhood Disorder
From Model 1 in Table 5-11, individual level perceptions of neighbourhood disorder are
significant predictors of smoking likelihood. A one-point rise in the score for
neighbourhood disorder (that is a perceived decrease in disorder levels) is associated with
a 5% decrease in the odds of smoking. But in model 2, the addition of the aggregate
variable removes the significance of the individual’s perception.
In model 3 when ED deprivation is added to the model, ED level neighbourhood disorder
is no longer significant. However women who perceive more crime in their area are more
likely to smoke. Across the interquartile range in individual neighbourhood disorder
scores this produces an increased odds of smoking of 1.1 (95% CI, 1.0- 1.2).
74
Table 5-18 Logistic regressions of smoking in women on individual socio-economic variables, age, area level deprivation and neighbourhood disorder variables (showing odds ratios and 95% CI limits).
Model 1 Model 2 Model 3 OR 95% CI OR 95% CI OR 95% CI
Lower Upper Lower Upper Lower UpperEducationHigh 1.00 1.00 1.00Medium 1.19 0.99 1.43 1.18 0.99 1.42 1.17 0.98 1.41Low 1.72 1.39 2.12 1.70 1.38 2.10 1.65 1.34 2.04Missing 1.46 1.12 1.90 1.43 1.10 1.86 1.40 1.08 1.83Social ClassI&II 1.00 1.00 1.00IIINM 0.83 0.69 1.01 0.83 0.69 1.01 0.82 0.68 1.00IIIM 1.10 0.85 1.42 1.09 0.85 1.40 1.08 0.84 1.40IV&V 1.27 1.04 1.56 1.26 1.03 1.54 1.24 1.01 1.52Other 1.16 0.87 1.53 1.17 0.88 1.54 1.13 0.86 1.50Missing 1.18 0.88 1.58 1.18 0.88 1.58 1.15 0.86 1.54Household IncomeMore than £215 per week 1.00 1.00 1.00Between £95 and £215 per week 1.32 1.13 1.53 1.31 1.12 1.53 1.29 1.11 1.50Less than £95 per week 1.40 1.11 1.75 1.40 1.12 1.75 1.37 1.10 1.72Missing 0.90 0.69 1.18 0.90 0.69 1.17 0.89 0.68 1.16Housing TenureOwner 1.00 1.00 1.00Not owner 1.81 1.55 2.10 1.75 1.50 2.04 1.66 1.42 1.94Missing 1.35 0.83 2.20 1.35 0.82 2.20 1.32 0.81 2.16Employment StatusEmployed 1.00 1.00 1.00Unemployed 1.18 0.78 1.80 1.18 0.77 1.79 1.19 0.78 1.82Student 0.53 0.32 0.88 0.53 0.32 0.88 0.53 0.32 0.89Home/carer 1.22 1.00 1.48 1.20 0.98 1.46 1.20 0.99 1.47Disability 1.14 0.92 1.42 1.13 0.91 1.40 1.12 0.90 1.39Retired 0.74 0.59 0.93 0.74 0.59 0.92 0.73 0.59 0.92Missing 1.19 0.89 1.60 1.18 0.88 1.57 1.17 0.88 1.57
Age 0.98 0.97 0.98 0.98 0.97 0.98 0.98 0.97 0.98
Ind Neighbourhood Disorder 0.95 0.93 0.98 * 0.97 0.94 0.99ED Neighbourhood Disorder 0.88 0.82 0.94 *ED Income Deprivation 1.01 1.01 1.02
Note: *variable produced an insignificant change and was not selected in the final model.
75
Neighbourhood Belonging
In Model 1, the individual assessment of neighbourhood belonging or attachment to the
area was significantly associated with smoking (see Table 5-12). However, in Model 2, it
did not produce a significant change to the model when the aggregate neighbourhood
belonging variable was present.
When ED level income deprivation is added (Model 3), higher levels of neighbourhood
belonging in the area continue to be associated with reduced odds of smoking. A 1-point
rise in ED neighbourhood belonging produces a 6% reduction in the odds ratio for
smoking. Across the interquartile range this reduces the odds of smoking by 0.82 (95%
CI, 0.73- 0.92).
76
Table 5-19 Logistic regressions of smoking in women on individual socio-economic variables, age, area level deprivation and neighbourhood belonging variables (showing odds ratio and 95% CI limits).
Model 1 Model 2 Model 3 OR 95% CI OR 95% CI OR 95% CI
Lower Upper Lower Upper Lower UpperEducationHigh 1.00 1.00 1.00Medium 1.18 0.99 1.42 1.16 0.97 1.40 1.15 0.96 1.38Low 1.67 1.35 2.06 1.62 1.31 2.00 1.58 1.28 1.96Missing 1.42 1.09 1.84 1.36 1.04 1.77 1.33 1.02 1.73Social ClassI&II 1.00 1.00 1.00IIINM 0.84 0.70 1.02 0.84 0.69 1.01 0.83 0.69 1.01IIIM 1.09 0.85 1.41 1.07 0.83 1.38 1.06 0.82 1.37IV&V 1.31 1.07 1.60 1.28 1.05 1.57 1.26 1.03 1.55Other 1.21 0.91 1.60 1.18 0.89 1.56 1.17 0.88 1.55Missing 1.27 0.95 1.70 1.27 0.95 1.70 1.25 0.94 1.67Household IncomeMore than £215 per week 1.00 1.00 1.00Between £95 and £215 per week 1.29 1.11 1.50 1.26 1.08 1.47 1.25 1.07 1.45Less than £95 per week 1.38 1.11 1.73 1.37 1.09 1.71 1.35 1.08 1.69Missing 0.88 0.68 1.15 0.87 0.67 1.14 0.86 0.66 1.13Housing TenureOwner 1.00 1.00 1.00Not owner 1.83 1.57 2.13 1.72 1.48 2.01 1.66 1.42 1.94Missing 1.17 0.72 1.91 1.17 0.72 1.90 1.16 0.71 1.89Employment StatusEmployed 1.00 1.00 1.00Unemployed 1.21 0.80 1.82 1.24 0.82 1.88 1.24 0.82 1.88Student 0.53 0.32 0.87 0.54 0.33 0.89 0.54 0.33 0.89Home/carer 1.22 1.00 1.48 1.20 0.99 1.46 1.20 0.98 1.46Disability 1.17 0.95 1.45 1.16 0.94 1.44 1.15 0.93 1.43Retired 0.72 0.58 0.90 0.72 0.58 0.90 0.72 0.57 0.90Missing 1.13 0.84 1.53 1.12 0.82 1.51 1.11 0.82 1.50
Age 0.98 0.97 0.98 0.98 0.97 0.98 0.98 0.97 0.98
Ind Neighbourhood Belonging 0.99 0.98 1.00 * *ED Neighbourhood Belonging 0.92 0.89 0.94 0.94 0.91 0.97ED Income Deprivation 1.01 1.00 1.02
Note: *variable produced an insignificant change and was not selected in the final model.
77
Social Cohesion
In model 1, social cohesion is associated with likelihood of smoking at the individual
level, however unlike the other social capital variables, higher levels of social cohesion
are associated with higher odds of smoking (Table 5-13).
Model 2 introduces the aggregate ED level measure of social cohesion. Individuals who
live in EDs with a higher aggregate level of social cohesion have a lower likelihood of
smoking.
Although social cohesion was the social capital variable least correlated with ED income
deprivation (see Table 5-8), when income deprivation is added to model 3, ED levels of
social cohesion become non-significant. However those women who have higher levels of
social cohesion persist in having higher odds of smoking.
Individual scores for social cohesion vary from 8 to 40. A woman scoring 40 has 1.6
(95% CI, 1.1-2.3) times the odds of smoking of a woman with all other individual
characteristics the same. However, few women score such extreme scores. Across the
interquartile range of the increase in odds of smoking is 1.1 (95% CI, 1.03-1.20). The
magnitude of this association is small compared to the individual socio-economic
variables but significant.
78
Table 5-20 Logistic regressions of smoking in women on individual socio-economic variables, age, area level deprivation and social cohesion variables (showing odds ratios and 95% CI)
Model 1 Model 2 Model 3 OR 95% CI OR 95% CI OR 95% CI
Lower Upper Lower Upper Lower UpperEducationHigh 1.00 1.00 1.00Medium 1.15 0.96 1.39 1.15 0.96 1.38 1.13 0.94 1.36Low 1.65 1.34 2.04 1.64 1.33 2.03 1.58 1.28 1.96Missing 1.27 0.97 1.66 1.25 0.96 1.64 1.20 0.92 1.57Social ClassI&II 1.00 1.00 1.00IIINM 0.83 0.69 1.01 0.83 0.68 1.01 0.82 0.68 1.00IIIM 1.10 0.85 1.43 1.09 0.84 1.41 1.09 0.84 1.41IV&V 1.33 1.09 1.63 1.32 1.08 1.62 1.29 1.05 1.58Other 1.22 0.92 1.61 1.21 0.91 1.60 1.19 0.90 1.58Missing 1.32 0.98 1.77 1.32 0.98 1.78 1.28 0.95 1.73Household IncomeMore than £215 per week 1.00 1.00 1.00Between £95 and £215 per week 1.30 1.11 1.51 1.29 1.11 1.51 1.26 1.08 1.47Less than £95 per week 1.36 1.08 1.70 1.35 1.08 1.69 1.32 1.05 1.65Missing 0.94 0.72 1.23 0.94 0.72 1.23 0.91 0.69 1.19Housing TenureOwner 1.00 1.00 1.00Not owner 1.94 1.66 2.25 1.88 1.61 2.19 1.76 1.51 2.06Missing 1.49 0.91 2.44 1.46 0.89 2.39 1.46 0.89 2.39Employment StatusEmployed 1.00 1.00 1.00Unemployed 1.24 0.81 1.88 1.25 0.82 1.90 1.26 0.83 1.92Student 0.53 0.32 0.87 0.53 0.32 0.88 0.53 0.32 0.88Home/carer 1.20 0.99 1.47 1.20 0.98 1.46 1.19 0.97 1.45Disability 1.23 0.99 1.53 1.23 0.99 1.53 1.21 0.97 1.50Retired 0.72 0.58 0.90 0.72 0.58 0.91 0.72 0.57 0.90Missing 1.15 0.85 1.57 1.15 0.85 1.57 1.13 0.83 1.54
Age 0.98 0.97 0.98 0.98 0.97 0.98 0.98 0.97 0.98
Ind Social Cohesion 1.01 1.00 1.02 1.02 1.01 1.03 1.02 1.00 1.03ED Social Cohesion 0.93 0.89 0.98 *ED Income Deprivation 1.02 1.01 1.02
Note: *variable produced an insignificant change and was not selected in the final model.
79
Chapter 6 Discussion
Summary of Results
This study examined the contribution of individual socio-economic circumstance and
area-level contextual measures to the likelihood of current smoking in men and women in
Caerphilly using the Caerphilly Health and Social Needs Survey Dataset. The overall
prevalence of smoking was 27.2%; this is similar to the prevalence found in Welsh Health
Surveys (NAW 1999, 2004).
Increased likelihood of smoking is strongly associated with lower income, lower levels of
education, lower social class and not being a homeowner. In addition, men who are
unemployed are significantly more likely to smoke than the employed. Likelihood of
smoking also decreases with age.
Next, the association of income deprivation and social capital with smoking was
investigated. The level of poverty in the surrounding area was not associated with the
likelihood of men smoking. However, women living in poor areas had significantly
higher odds of smoking than those in more affluent areas after adjusting for their own
socio-economic status.
None of the social capital indicators investigated were associated with the likelihood of
smoking in men. This was true of individual perceptions of the area, and the aggregate
variables constructed from the mean scores of all who lived in the area.
In contrast, women who live in neighbourhoods of poorer quality (for example, with
higher reported noise, vandalism and speeding traffic) are significantly more likely to
smoke, after adjusting for the level of poverty in the area. Individual women who
perceive higher levels of problems are also more likely to smoke. Although the aggregate
measure of neighbourhood disorder is not significantly associated with smoking after
80
adjusting for deprivation in the area, women who perceive higher levels of crime in their
neighbourhood are more likely to smoke.
In neighbourhoods where there is a greater sense of community attachment, women are
less likely to smoke. Women who live in neighbourhoods with higher levels of interaction
between residents are less likely to smoke, but individual women who describe greater
interaction with their neighbours, are more likely to smoke than those who do not. This is
an example of a “reactive effect” (Jones and Duncan 1995;32), where the effect of the
ecological and individual variables are in opposite directions.
Comparisons with Existing Literature
Individual Determinants of Smoking
In this study, level of education, income, social class and housing tenure are significantly
associated with smoking in men and women. This is consistent with previous findings
(Jarvis and Wardle 1999; Marsh and McKay 1994). For women, the differential between
high and low levels of education was greater than for men, whilst for income the opposite
was true. This fits with recent evidence that shows that education is a stronger predictor
of smoking status than income for women in countries in Europe (Huisman et al. in
press). It has also been found that father’s social class predicted smoking in women but
not in men (Brunner et al. 1999). Together with other work (Lynch et al. 1997) this
suggests that childhood socio-economic circumstances may have an impact on current
smoking status, and possibly a larger impact in women (Jefferis et al. 2004).
Macintyre and colleagues have extensively investigated the relationship between housing
tenure and health (Ellaway and Macintyre 1998; Macintyre et al. 2003; Macintyre et al.
2000). They did not analyse the relationship with smoking, however their findings that
problems with social housing (such as damp and noise) were associated with higher levels
of anxiety in residents may contribute to the differential in smoking between home and
non-home owners (the vast majority of whom were in social housing). Alternatively,
those who rent, compared to those who have bought their homes on the same housing
81
estates, have been shown to spend their lives mainly within the confines of the estate in
relative social exclusion and to have little interaction with the owner-occupiers (Atkinson
and Kintrea 2000). This combined with the pro-smoking norms found in social housing
by Stead et al.(2001) may explain the difference. Renting may also reflect past as well as
present income levels. Gordon (2003) suggests that as income rises and individuals climb
out of poverty, there is a lag in the increase in their standard of living. As such, even
when financial circumstances improve, individuals may not be immediately able to afford
to purchase their own home.
Area Deprivation and Smoking
There is variation in smoking rates between areas because of composition effects (the
attributes of residents) and contextual effects such as deprivation (Diez-Roux 2003;
Macintyre and Ellaway 2000). Deprivation has been previously shown to have an effect
on the likelihood of residents smoking, after adjusting for their individual socio-economic
circumstances (Boreham et al. 2002; Diez-Roux et al. 2003; Duncan et al. 1999;
Kleinschmidt et al. 1995; Patterson et al. 2004; Shohaimi et al. 2003; Tseng et al. 2001).
In Caerphilly, there is an association between area deprivation and likelihood of smoking
in women only. In studies that have previously modelled interaction effects between
gender and deprivation (Duncan et al. 1999; Patterson et al. 2004) no significant
associations with smoking have been reported. Shohaimi et al. conducted separate
analyses by gender and found that area deprivation predicted smoking in both sexes
similarly. In that paper the “conventional” method of attributing social class to women
was used, whereby women were assigned to the social class of their partner unless
unmarried (Arber 1997). In the Caerphilly study the “individualistic” method was used
where women were attributed to a social class on the basis of their own occupation. Arber
has suggested that the conventional method may give a truer reflection of the material
circumstances of the home since the husband’s occupation is likely to contribute more to
this (Arber 1991). Bartley et al. (2004) have shown in analysis of the Whitehall studies
that the social class of a female civil servant’s partner is a stronger predictor of her
smoking than her own civil service grade. In the Caerphilly study, the use of the woman’s
82
own social class may result in some mis-specification, which is then explained by area
deprivation.
Gender differences in associations between neighbourhood and
health
In this analysis, no social capital variables were associated with the likelihood of smoking
in men. The individual social capital scores for men and women had similar means and
distributions (see Table 5-6), unlike in the work of Ellaway and Macintyre (Ellaway et al.
2001) in which women perceived significantly more social and environmental problems,
and higher levels of cohesion. In addition the area-level measure was the mean of both
male and female scores. The observed difference is therefore unlikely to be due to
reporting bias.
Molinari et al. (1997) found that women’s self-reported physical and mental health was
related to the presence of community problems of a social nature, whilst for men they
were related to physical environmental problems such as perceived water quality. More
recently, in a multi-level analysis, Stafford et al. (in press) found that between-
neighbourhood differences in self-reported health were larger for women than men, after
controlling for age, social class, economic activity and family type. Again this analysis
risks mis-specifying the socio-economic status of women as social class was determined
in the individualistic fashion.
In conclusion, the observed difference of the impact of neighbourhood characteristics on
smoking in men and women, may be a true contextual difference reflecting women’s
increased propensity to be affected by the neighbourhood environment, or may result
from mis-specification of women’s socio-economic status in the individual-level models.
The following is a discussion of the association between the social capital components
and smoking in women.
Neighbourhood Quality
83
In this work, poor quality of the area (problems such as vandalism, graffiti, noise, dogs,
uneven pavements, speeding traffic, disturbance by young people, and lack of safe places
for children to play) significantly increased the likelihood of women smoking, after
adjusting for area-level income deprivation. It seems very unlikely that this could be due
simply to the mis-specification of individual attributes. Parkes and Kearns (in press) also
found that a ‘neighbourhood problems’ measure such as this was associated with
increased smoking, and it was also more strongly related to smoking in men than women.
This analysis considered the individual’s perception of neighbourhood quality but this
was also only significant for women.
It could also be considered whether neighbourhood quality is an accurate measure of
social capital. Portes (2000) and Stone (2001) have criticised tautological, circular
definitions of social capital, whereby the presence of social capital is inferred by a
positive outcome or distal measure. I have earlier considered that some elements of this
component could relate to “good council services” (certainly the item asking about
quality of pavements). The component could then be described as assessing “linking
social capital” (Szreter and Woolcock 2004). But this would be a tautological definition,
as Putman has pointed out in his criticism of Szreter and Woolcock’s discussion of that
construct (Putnam 2004). Other items relate to ‘neighbourhood disorder’ as measured
directly by Sampson and Raudenbush (1999). They did this so that they could assess if
community levels of collective efficacy predicted area disorder. This concept of collective
efficacy does not suffer the tautological criticism because what is being measured as
collective efficacy (social cohesion and control) is proximal to the outcome (the presence
of crime or neighbourhood disorder) (Sampson et al. 1999; Sampson et al. 1997). In the
Caerphilly study, although social cohesion was measured, no assessment was made of the
‘social control’ element of collective efficacy.
However, the finding that women in a community with high levels of incivilities and poor
infrastructure are more likely to smoke than women in an equally impoverished area that
does not have such poor ‘quality’ is significant and deserves further exploration.
84
Sooman and Macintyre (1995) found significant correlations between this measure of
neighbourhood quality and levels of anxiety and depression in individuals in Glasgow. In
addition, Ross et al. (2000) found that increased levels of poor mental health in
communities in Chicago were associated with area-level measures of neighbourhood
quality. They suggested that increased levels of incivilities mediated the relationship
between deprivation and poor mental health. However in Caerphilly the relationship
between smoking and quality persists after adjusting for deprivation.
Sampson and Raudenbush (1999) found a reciprocal relationship between collective
efficacy and crime and disorder. Although collective efficacy was associated with levels
of disorder and crime, previous higher levels of crime reduced the collective efficacy of
the community. Incivilities increased the feelings of powerlessness in residents in
Chicago (Ross et al. 2000). If poor neighbourhood quality leads to a sense of lack of
control in the lives of residents then this may partially explain the increased likelihood of
smoking. Research in the Netherlands has shown that levels of self-efficacy and
perceived control predict current smoking (Stronks et al. 1997) and the ability of
individuals to stop smoking (Droomers et al. 2004). Whilst work has examined the
association between perceived sense of control in the workplace and at home on
cardiovascular risk and risk behaviours(Chandola et al. 2004; Lindstrom 2004), none has
been identified that assesses the impact of neighbourhood.
Neighbourhood Disorder
This component related to perception of crime rather than experience of crime. At the
area level, although communities that had higher perceived crime had higher levels of
smoking, this effect was not significant when income deprivation was considered.
Sampson and Raudenbush (1999) have previously shown that perception of crime and
recorded level of crime correlate closely with deprivation. However, individual women
who perceived higher levels of crime were more likely to smoke, when area deprivation
and area perceptions of crime were considered.
85
Boreham et al. (2002) has also found that perception of crime is associated with smoking
at the individual level. Parkes and Kearns (in press) found that individuals who had
experienced crime were more likely to smoke. It may be that women in Caerphilly, who
have actual experience of crime, therefore perceive higher levels of crime than their
neighbours and are more likely to smoke. Alternatively, those who perceive higher levels
of crime in the neighbourhood than their community at large may be of a more anxious
disposition. Those who fear crime have higher distress levels than those who do not (Ross
and Mirowsky 2001; Ross et al. 2000). Stronks et al.(1997) found that smokers in the
Netherlands had higher scores for neuroticism than former and never smokers.
Neighbourhood Belonging
Community attachment has been shown to be related to stability of the community, longer
mean length of residence and increased friendship ties (Sampson 1986). In this analysis,
an individual’s sense of belonging to the community was weakly related to smoking
status, and was insignificant when the area variable was added. Living in a
neighbourhood that had a high collective sense of community was strongly associated
with decreased likelihood of smoking, an effect that was only slightly reduced by
adjusting for area income deprivation. This may reflect increased opportunities for
individuals in these areas to access social support, which is related to decreased likelihood
of smoking, particularly in women (Boreham et al. 2002; Cooper et al. 1999). Ross et al.
(2000) found that neighbourhood stability and friendship ties decreased levels of distress
less in individuals in poor communities. In addition, it has been shown that social support
is only protective of mental and physical health in more affluent communities (Elliott
2000). Hence a possible explanation for why the association between neighbourhood
belonging and smoking persists after adjusting for income deprivation.
86
Social Cohesion
This component assessed the amount of interaction respondents had with their neighbours
and the extent to which they believed they could turn to neighbours in times of difficulty.
The latter aspect is related to instrumental and informational social support (House and
Kahn 1985). Higher area levels of social cohesion were associated with reduced
likelihood of smoking. This effect was no longer significant when income deprivation
was considered. This suggests that social cohesion may partly mediate the association of
income deprivation with prevalence of smoking. Patterson et al. (2004) found that the
effect of social cohesion persisted after adjusting for concentration of poverty and low
education. However, as discussed earlier, these measures may not have been good at
differentiating area deprivation, as they were constructed from survey data prone to
sampling error. In addition, the social cohesion index used by Patterson et al. (2004)
differs to that used in this study (see Tables 2-1 and 3-5). The US measure does not assess
interaction with neighbours, and asks if someone the respondent did not know would help
in an emergency, whilst the Caerphilly index asks if respondents would be able to access
or advice from a neighbour, without specifying that this should be someone they do not
know. As such, the Caerphilly measure could be said to reflect more closely the lived
experience of residents in the area, rather than hypothetical situations.
In contrast, an individual woman who has stronger relationships with her neighbours is
more likely to be a smoker, than someone who does not. Pevalin and Rose (2003) and
Boreham et al. (2002) found similar associations for both men and women, whilst Parkes
and Kearns (in press) found no association. These findings are not unique to the United
Kingdom. An analysis of the Third US National Health and Nutrition Examination
Survey found that higher levels of social interaction, both formal and informal, were
associated with decreased risk of negative health behaviours generally, but this was not
true of smoking (Ford et al. 2000). Although organisational membership was associated
with lower risk of smoking, informal contact with friends and neighbours increased the
risk of smoking. The authors describe the finding as “a somewhat contrary result” (Ford
et al. 2000;89) and a search of ISI Web of Science shows that it has not been widely cited
since.
87
As mentioned earlier, Pevalin and Rose (2003) also commented that this result was
surprising because it was thought that the socially isolated were more likely to smoke.
The “social isolation- pro-smoking” theory dates to the Alameda County study (Berkman
and Syme 1979). Table 6-2 shows that the prevalence of smoking is higher in those who
have lower social contacts. Amongst men, the proportion of ever-smokers (current and
ex-smokers) is similar for all quartiles, so it seems that the socially isolated are least
likely to stop smoking.
Table 6-21 Proportion current and ex-smokers by Social Network Index quartile in Alameda County Study
%Current % Ex-Social Network Index Smoker Smoker
Male I 57 18II 52 23III 53 23IV 46 26
Female I 50 11II 45 12III 47 9IV 34 11
Note: I (most isolated), IV (least isolated)Source data: Berkman and Syme (1979)
A limited literature search shows that further work has tended to look at the connection
between social isolation and health behaviours in those with established cardiovascular
disease rather than the population at large (Brummett et al. 2001; Rutledge et al. 2004).
Rutledge et al. (2004) found that women with cardiovascular disease who are socially
isolated are more likely to smoke than those who are not. It may be that once disease has
been diagnosed then friends and relatives exert social control on patients to stop smoking.
Parry et al. (2002) report feelings of isolation associated with smoking in older people
diagnosed with arterial disease. In particular, they describe disapproval from family
members limiting smoking, and some only smoked outside the home in social situations
because of this.
88
In contrast, qualitative work examining attitudes to smoking in younger people has
consistently shown that smoking is a social activity (Laurier et al. 2000; McKie et al.
2003; Stead et al. 2001). The effect of policies limiting smoking at work may even have
been to increase the social bonds between smokers (McKie et al. 2003). In Glasgow,
Stead et al. (2001) found a similar sense of persecution in those exposed to the wider
norms of non-smoking outside their pro-smoking communities.
The relationship between social interaction and smoking is obviously complex and needs
further exploration. It is not known from this dataset whether smokers spend more time
associating with other smokers, or generally have higher levels of integration. “Contagion
effects” have been cited as a possible mechanism through which social interaction may
encourage the uptake or maintenance of behaviour such as smoking, whereby being in the
presence of other smokers may increase one’s likelihood of smoking, particularly in an
area of high prevalence (Diez-Roux et al. 2003; Greinera et al. 2004; Kawachi et al. 2004;
Oakes 2004; Wilcox 2003). Analysis of effects such as this requires complex
methodologies. In an evolution of game theory, economists have shown that there is an
“asymmetric social influence” between adolescent smokers and non-smokers(Harris and
López-Valcárcel 2004). The pro-smoking norms of a smoker have a much greater effect
on another smoker, than those of a non-smoking peer.
Strengths and Weaknesses of Study
Survey Data
The cross-sectional nature of the Caerphilly Health and Social Needs dataset imposes
restrictions on the interpretation of the findings. Since smoking status and individual and
area-level covariates are measured simultaneously, their association does not necessarily
indicate a causal relationship. Macleod and Davey Smith (2003) have drawn attention to
the particular risks of mis-labelling non-causal associations as causal associations in work
assessing the impact of psychosocial factors on health.
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Self-reported smoking status was used as the outcome variable. This has been shown in
other work to be reliable (Patrick et al. 1994; Vartiainen et al. 2002), which reduces the
risk of affect bias that has concerned researchers investigating links between self-reported
health and social capital (Stafford et al. in press). In addition the prime focus of this work
was the impact of area-level variables for which the responses of smokers and non-
smokers were aggregated. Of the two significant associations shown between smoking
and individual social capital measures, one could be related to positive affect (social
cohesion) and one with negative affect (fear of crime).
The possibility of confounding in the association between area-covariates and smoking
has been reduced through the use of multiple individual variables. This had the effect of
removing any association in men between area and smoking likelihood. I have already
discussed that the use of the individual method of attributing social class in women may
have lead to mis-specification of their socio-economic circumstances. Again the use of
other variables including educational status, limits this possibility. The use of an
independent measure of area income deprivation also reduced the possibility of
confounding.
Reverse causation is a possibility with cross-sectional datasets; in the case of some of the
associations found the relationship may be reciprocal as previously discussed. Smoking
may increase the bonds between neighbours, and contact with neighbours who are
smokers may increase the likelihood of an individual smoking. There may be
explanations that suggest smokers are more likely to harm their environment, or create
negative feelings in the community, but I have not come across empirical evidence to
support them.
The large size of the dataset and number of individual variables enabled a comprehensive
assessment of factors known to be associated with smoking. In addition, the questions
assessing social capital allowed the construction of variables to assess social capital.
There is much debate surrounding social capital, and as yet there is no consensus on what
it is or how to measure it (Morgan and Swann 2004). The measures included here have
limitations, but are representative of those used in other work.
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The response rate was higher than is often reported for postal surveys. The lower
response rates from young men in deprived communities may have affected the results. It
is possible that this is why area-effects are seen for women but not men.
Analysis
The single-level multivariate logistic regression models used allowed the assessment of
individual and area-level variables on likelihood of an individual smoking. However, this
form of logistic regression assumes that observations are independent. Within the
enumeration districts used as the ecological unit, there may be unaccounted residual
correlation. This may have lead to the over-estimation of the effects of the independent
area-level variables on the dependent variable, smoking.
Conclusion
This work set out to examine the association between individual and contextual variables
on smoking behaviour in Caerphilly. I have found, in common with previous research,
that individuals who have poor material circumstances, and those who live in deprived
communities have increased likelihood of smoking. This social patterning of smoking
behaviour is long established, but the explanations are still uncertain (Kunst et al. 2004;
Layte and Whelan 2004; Oakes 2004). There is emerging evidence that inequalities in
smoking are related to ‘social trajectories’ and that the accumulation of material
disadvantage throughout life increases likelihood of smoking and difficulties with
stopping (Jefferis et al. 2004; Kunst et al. 2004).
This work adds to the published research by demonstrating that social and environment
aspects of the neighbourhood are associated with smoking in women. Areas of poor
quality have greater levels of smoking amongst women than can be explained solely by
the characteristics of the individuals living there and the level of income deprivation.
Neighbourhoods where there is a stronger sense of attachment to the area have lower
levels of smoking. These measures of ‘social capital’ may reflect levels of “ambient
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strain” (Pearlin 1989;246) and opportunities for social support. In addition women who
are more socially integrated in their communities are more likely to smoke.
Suggested Areas of Further Research
The findings of this exploratory research may lend support to ‘psychosocial’ explanations
for differences in prevalence of smoking, and therefore poor health, between areas. Future
work could also assess ‘neomaterial’ explanations: differential access to smoking
cessation services for example.
First, it will be necessary to analyse the dataset using multi-level models. This will allow
the robust simultaneous examination of the individual and area-level variables on the
likelihood of smoking in individuals. If the relationships described above still exist then
further work will be required.
The Caerphilly Dataset includes assessments of mental health (SF-36), and constructs
such as ‘locus of control’. These may prove useful initially in investigating the possible
mechanisms through which area quality and community attachment are associated with
smoking. A follow-up survey would also allow the examination of longitudinal
associations between social capital variables and smoking initiation and cessation. This
could also be analysed using a multilevel model (Diez Roux 2003).
Qualitative work, exploring the lived experience of women within these communities
may also yield additional insights, particularly with regards to the relationship between
interaction with friends and neighbours and smoking. The impact of policies designed to
limit smoking in public places on the social interaction of women who are smokers
should also be assessed. The failure of community-based programmes to change
smoking-related norms (Ritchie et al. 2004; Ross and Taylor 1998) suggests that further
research is needed into the mechanisms through which these norms are constructed and
replicated. A recent UK funding call has asked specifically for work which examines the
role of social networks in smoking behaviour (MRC 2004). This area undoubtedly
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deserves further exploration and would necessitate the use of complex methodologies and
analysis (Oakes 2004).
Implications for Policy
The exploratory nature of this work limits the conclusions that can be drawn for policy
direction. The finding that in South Wales, attributes of neighbourhoods are associated
with the likelihood of smoking in women suggests that preventive smoking programmes
and policy should target areas as well as individuals. Unfortunately the mechanisms that
would be successful at this are still to be determined.
It may be that living in communities where there is less sense of belonging and higher
levels of problems, reflects persistent material deprivation beyond currently having a low
income or low levels of education. However, the route by which material deprivation
itself increases prevalence of smoking is also not understood. It may partly be through
‘psychosocial’ pathways such as the social capital indicators examined here. If this is
found to be the case then increasing current individually focussed smoking cessation
programmes is unlikely to be successful. It may instead be necessary to invest in human
capital and infrastructure in these areas.
In the meantime initiatives that seek to work with communities to improve health may
provide opportunities for innovative approaches to smoking cessation and prevention.
Communities First (NAW 2001) and Healthy Living Centres are examples.
At the individual level, confirmation of the finding that social integration, not isolation, is
related to smoking in women, may lead to policy interventions depending on the causal
pathway. This could prove to be an example of a negative relationship between social
capital or social integration and health, so that “ being alone might be better”(O'Brien
Caughy et al. 2003).
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Chapter 7 References
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Appendix A Tables and FiguresTABLE 1-1 SAMPSON'S COLLECTIVE EFFICACY SCALE......................................................15TABLE 1-2 ROSS-MIROWSKY PERCEIVED NEIGHBORHOOD DISORDER SCALE..................16TABLE 2-1ITEMS CONTRIBUTING TO SOCIAL COHESION COMPONENT...............................22TABLE 2-2 ITEMS CONTRIBUTING TO NEIGHBOURHOOD BELONGING COMPONENT...........22TABLE 2-3 ITEMS CONTRIBUTING TO NEIGHBOURHOOD DISORDER COMPONENT.............23TABLE 2-4 ITEMS CONTRIBUTING TO NEIGHBOURHOOD QUALITY COMPONENT...............23TABLE 3-1 RESULTS OF DATABASE SEARCH...................................................................26TABLE 5-1 FREQUENCY OF SMOKING WITH GENDER..........................................................48TABLE 5-2 NUMBER OF RESPONDENTS AND PROPORTION SMOKERS PER ENUMERATION
DISTRICT (ED)............................................................................................................48TABLE 5-3 UNADJUSTED ODDS RATIOS FOR SMOKING BY 10 YEAR AGE BAND.................50TABLE 5-4 UNADJUSTED ODDS RATIOS FOR SMOKING BY INDIVIDUAL-LEVEL SOCIO-
ECONOMIC VARIABLES................................................................................................52TABLE 5-5 UNADJUSTED ODDS RATIOS OF SMOKING FOR INDIVIDUAL AND AREA LEVEL
SOCIAL CAPITAL CATEGORIES AND AREA LEVEL INCOME MEASURES........................58TABLE 5-6 DESCRIPTIVE STATISTICS FOR CONTINUOUS VARIABLES USED IN
MULTIVARIATE ANALYSIS...........................................................................................61TABLE 5-7 PEARSON CORRELATION COEFFICIENTS FOR INDIVIDUAL SOCIAL CAPITAL
INDICATORS................................................................................................................62TABLE 5-8 PEARSON CORRELATION COEFFICIENTS FOR ED CONTINUOUS VARIABLES......62TABLE 5-9 LOGISTICAL REGRESSION OF SMOKING ON INDIVIDUAL SOCIO-ECONOMIC
FACTORS, AGE AND ED INCOME DEPRIVATION (SHOWING 95% CI FOR ODDS RATIO).....................................................................................................................................67
TABLE 5-10 LOGISTIC REGRESSIONS OF SMOKING IN WOMEN ON INDIVIDUAL SOCIO-ECONOMIC VARIABLES, AGE, AREA LEVEL DEPRIVATION AND NEIGHBOURHOOD QUALITY VARIABLES (SHOWING ODDS RATIO AND 95% CI LIMITS)..........................71
TABLE 5-11 LOGISTIC REGRESSIONS OF SMOKING IN WOMEN ON INDIVIDUAL SOCIO-ECONOMIC VARIABLES, AGE, AREA LEVEL DEPRIVATION AND NEIGHBOURHOOD DISORDER VARIABLES (SHOWING ODDS RATIOS AND 95% CI LIMITS).......................73
TABLE 5-12 LOGISTIC REGRESSIONS OF SMOKING IN WOMEN ON INDIVIDUAL SOCIO-ECONOMIC VARIABLES, AGE, AREA LEVEL DEPRIVATION AND NEIGHBOURHOOD BELONGING VARIABLES (SHOWING ODDS RATIO AND 95% CI LIMITS)......................75
TABLE 5-13 LOGISTIC REGRESSIONS OF SMOKING IN WOMEN ON INDIVIDUAL SOCIO-ECONOMIC VARIABLES, AGE, AREA LEVEL DEPRIVATION AND SOCIAL COHESION VARIABLES (SHOWING ODDS RATIOS AND 95% CI)....................................................77
Table 6-1 Proportion current and ex-smokers by Social Network Index quartile in Alameda County Study..............................................................................................86
FIGURE 1-1 LINKS BETWEEN THE MODELS OF SAMPSON AND ROSS..................................16FIGURE 5-1 PREVALENCE OF SMOKING BY AGE GROUP (WITH 95% CI)...........................50FIGURE 5-2 PREVALENCE OF SMOKING BY EMPLOYMENT STATUS (WITH 95% CI)...........53FIGURE 5-3 PREVALENCE OF SMOKING BY SOCIAL CLASS (WITH 95% CI).......................53FIGURE 5-4 PREVALENCE OF SMOKING BY HOUSEHOLD INCOME (WITH 95% CI).............54FIGURE 5-5 PREVALENCE OF SMOKING BY HOUSING TENURE (WITH 95% CI)..................54FIGURE 5-6 PREVALENCE OF SMOKING BY EDUCATIONAL STATUS (WITH 95% CI)..........55
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FIGURE 5-7 PREVALENCE SMOKING BY INDIVIDUAL SOCIAL CAPITAL CATEGORIES (WITH 95% CI)......................................................................................................................57
FIGURE 5-8 PREVALENCE OF SMOKING BY ED LEVEL SOCIAL CAPITAL AND INCOME DEPRIVATION (% HOUSEHOLDS WITH INCOME <£10000/YR) (WITH 95% CI)............60
FIGURE 5-9 ED NEIGHBOURHOOD QUALITY VS. SMOKING PREVALENCE. R= -0.55 P<0.001....................................................................................................................................63
FIGURE 5-10 ED NEIGHBOURHOOD DISORDER VS. SMOKING PREVALENCE. R=-0.40 P<0.001.......................................................................................................................64
FIGURE 5-11 ED NEIGHBOURHOOD BELONGING VS. SMOKING PREVALENCE. R=-0.49 P<0.001.......................................................................................................................64
FIGURE 5-12 ED SOCIAL COHESION VS. SMOKING PREVALENCE. R=-0.28 P<0.001..........65Figure 5-13 ED Income deprivation vs. smoking prevalence. r= 0.52 p<0.001................65
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Appendix B Websites of Surveys included in Literature Review
(all accessed 30/11/04)
http://www.esds.ac.uk/longitudinal/access/bhps/introduction.asp Introduction to BHPS
http://www.co.hennepin.mn.us/vgn/portal/internet/hcdetailmaster/0,2300,1273_1716_100013257,00.html SHAPE survey
http://www.statistics.gov.uk/ssd/surveys/general_household_survey.asp Introduction to GHS
http://www.statistics.gov.uk/STATBASE/Source.asp?vlnk=1313&More=Y HALS
http://www.scotland.gov.uk/about/SR/CRU-SocInc/00016002/SHShome.aspx Scottish Household Survey
http://www.dh.gov.uk/PublicationsAndStatistics/PublishedSurvey/HealthSurveyForEngland/fs/en Health Survey for England
http://www.esds.ac.uk/government/hse/ Health Survey for England
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