Changing places: the impact of rural restructuring on mental health in Australia

Health & Place 11 (2005) 157–171

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doi:10.1016/j.he

Changing places: the impact of rural restructuring on mentalhealth in Australia

Cait Frasera,*, Henry Jacksonb, Fiona Judda,c, Angela Komitia,d, Garry Robinsb,Greg Murraye, John Humphreysf, Pip Pattisonb, Gene Hodginsa,b

a Centre for Rural Mental Health, Bendigo Health Care Group, PO Box 126, Bendigo, Vic. 3552, Australiab Department of Psychology, The University of Melbourne, Melbourne, Vic., Australia

c School of Psychology, Psychiatry, and Psychological Medicine, Monash University, Melbourne, Vic., Australiad Department of Psychiatry, The University of Melbourne, Melbourne, Vic., Australia

e School of Social and Behavioural Sciences, Swinburne University of Technology, Melbourne, Vic., Australiaf School of Rural Health, Monash University, Bendigo, Australia

Accepted 16 March 2004

Abstract

Significant demographic, social and economic change has come to characterise much of rural Australia, with some

authors arguing there are now two sharply differentiated zones, one of growth and one of decline. This restructuring

process, which has been similar to other western nations, has had a profound impact upon rural places—socially,

economically and physically. Findings from research investigating the relationship between health, place and income

inequality suggest that rural ‘desertification’, which is characterised by decline of the agricultural sector, net population

loss and the deterioration of demographic structures, may negatively influence mental health outcomes in these areas.

By contrast, the growth in rural areas, which is associated with expanding employment opportunities and the movement

of capital and people, may confer positive benefits to mental health. The aim of this study was to investigate differences

in mental health and well-being between rural communities experiencing growth and decline as measured by net

population change. Utilising a survey methodology, questionnaires were distributed to 20,000 people randomly

sampled from the electoral role in rural Australia. We selected four sub-regions from the sample area that were

characteristic of areas experiencing population growth and decline in Australia and analysed the results of respondents

from these four regions (n ¼ 1334). The analysis provided support for our hypothesis that living in a declining area is

associated with poorer mental health status; however, the factors that underpin growth and decline may also be

important in influencing mental health. Discussed are the mechanisms by which demographic and social change

influence mental health. The findings of this study highlight the diversity of health outcomes in rural areas and suggest

that aspects of place in declining rural areas may present risk factors for mental health.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Rural; Mental health; Place

Introduction

There is an increasing body of empirical and

theoretical work examining the link between health

ing author. Tel.: +61-03-5454-7753; fax: +61-

ess: [email protected] (C. Fraser).

e front matter r 2004 Elsevier Ltd. All rights reserve

althplace.2004.03.003

and place. Whilst this work has yielded significant

insights into the mechanisms through which place

influences health (Curtis and Rees Jones, 1998; Macin-

tyre et al., 2002; Sampson et al., 2002), there have been

few studies that have investigated place and health in

rural contexts (Kobetz et al., 2003). Compared with

urban neighbourhoods, which have tended to be the

focus of urban health and place studies, there are

d.

ARTICLE IN PRESSC. Fraser et al. / Health & Place 11 (2005) 157–171158

well-documented differences in rural towns, such as

geographical isolation, and lower rates of health service

per capita, that may be important to health outcomes. In

addition, characteristics of communities that are known

to be important to poor health, such as high levels of

socio-economic disadvantage, poor quality housing and

limited access to labour markets, may have differential

impacts across urban and rural settings as they are

mediated by distinct sets of cultural and social condi-

tions.

The aim of this study is to investigate the relationship

of one feature of rural places, population growth and

decline, to mental health. Population loss and popula-

tion growth are salient features of rural areas in

Australia and many other developed nations

(McKenzie, 1994a b; Stockdale et al., 2000). Examining

population growth is relevant to health as it is both an

indicator of, and contributor to, changes to broader

social and economic conditions of an area. Rural decline

or ‘desertification’ is not only characterised by net

population loss but also the undermining of demo-

graphic structures, the withdrawal and closure of

services and declining employment opportunities (Caw-

ley, 1994). Similarly, population growth or ‘counter-

urbanisation’ is associated not only with the movement

of people to an area but also capital and employment

(Curry et al., 2001; Stockdale et al., 2000). Population

growth, and the processes that underpin it, therefore has

the potential to result in significant changes to the nature

of places and potentially impact upon health outcomes.

This paper will draw upon research investigating

health, place and income inequality, which provides a

basis for understanding how living in areas of growth

and decline can influence health. These studies have

identified a range of features of local areas that can

influence health outcomes including the ‘neo-material

environment’ (Lynch et al., 2000), access to labour

markets, increased exposure to environmental stressors

(Macintyre et al., 1993) and the psychological impact of

living in a deprived area (Wilkinson, 1997).

Background: population change in rural areas

In Australia rural population change has generally

reflected international trends. Like many other devel-

oped nations the movement of people from rural to

urban areas has been occurring in Australia for much of

this century (Productivity Commission, 1999). Typically,

this type of decline led to an aging and relatively

impoverished rural population, with access to limited

services (Stockdale et al., 2000). However, by the 1970s a

process of net migration gain to rural areas—‘counter-

urbanisation’—was found to be taking place in a

number of western countries including the US (Beale,

1977) Australia (Hugo, 1994), Canada (Dahms and

McComb, 1999) and the UK (Champion, 1989; Stock-

dale et al., 2000). By the 1980s, however, many authors

were asserting that the ‘rural renaissance’ as it was

described, had ended, and that rural areas would

continue to decline as metropolitan areas grew (Dahms

and McComb, 1999). Data from Australia suggest a

more complex pattern of population change in rural

areas, with two sharply differentiated zones of growth

and decline emerging in rural Australia (Hugo, 2001).

Whilst some of the strongest growth rates in the country

are being recorded in the attractive coastal and

mountain environments, and urban fringe areas (Hugo,

1994), widespread decline is continuing in inland

agricultural regions, and to a lesser extent mining towns

(McKenzie, 1994a b).

Drivers of growth and decline in rural Australia

Negative and positive population growth in rural

Australia is essentially a symptom of more widespread

political, economic and social change. The factors that

underpin rural decline and growth are often exogenous

to rural areas themselves and are tied to national and

international economic and political dynamics (Cloke,

1996). This has clearly been true in the Australian

setting. Rural decline has been strongly tied to the

diminishing terms of trade for primary produce,

particularly agriculture, within global markets

(McKenzie, 1994b). The 1980s and 1990s proved to be

one of the most difficult periods for farmers in Australia

with the collapse of the wheat, wool and barley markets

(Budge, 1996), a dramatic fall in land values and

simultaneous increase in interest rates (Smailes, 2000)

and widespread drought (Budge, 1996). During this time

the process of agro-industrialisation was occurring,

leading to a centralisation of agricultural production

(Burch and Rickson, 2001), and a drive towards

economically competitive production (McMichael and

Lawrence, 2001). The result has been farm amalgama-

tions, fewer farming families (a decline of 22% since

1986) (Australian Bureau of Statistics, 2003a, b), and

reduced employment of paid labour (Lawrence and

Williams, 1990). The declining number of farms and the

wider sourcing of farm inputs (Lawrence, 1996) also had

a detrimental effect upon the economy and population

of the towns servicing farming areas (Budge, 1996). As

Lawrence and Williams (1990, p. 40) have noted: ‘‘more

productive agriculture is coming to mean less productive

and viable rural communities’’.

Mining areas have also been vulnerable to substantial

population decline due to the unstable nature of

many mineral markets and their economic depen-

dence on a single, exhaustible resource (McKenzie,

1994b). As the majority of mines operate in remote

ARTICLE IN PRESSC. Fraser et al. / Health & Place 11 (2005) 157–171 159

areas and these towns tend to have an undiversified

economy, mine closure can result in rapid population

decline.

As Walmsley and Sorensen (1990) have outlined, a

significant fall in population can lead to a ‘vicious cycle’

of decline. Falling population leads to a reduction in the

demand for goods, a subsequent decline in the produc-

tion of goods and services, decreased employment

opportunities and further out-migration in rural areas

(Fincher, 1999; McKenzie, 1994a). In Australia this

cycle was compounded by a trend towards the centra-

lisation of public and private sector services (particularly

health, education and financial services) taking jobs,

capital and people out of rural areas to large regional or

metropolitan cities (Lawrence, 1999; Raiston and Beal,

1997). The withdrawal of public sector employment,

which accounts for up to one-third of employment in

small country towns (Jones and Tonts, 1995), also

removed the regular ‘drought-proof’ income from

communities, which can be heavily affected by the

seasonal variations in the agricultural economy

(Smailes, 1997). Whilst this process of decline leads to

out-migration, it can also lead to ‘entrapment’; as

property values fall and there are few buyers, house-

holds may be unable to sell their property at prices that

allows them to relocate to more prosperous areas

(Budge, 1996).

A number of studies have shown that the loss of

amenities such as schools, hospitals and banks can also

have a social and psychological impact upon the town’s

remaining residents (Argent and Rolley, 2000; Raiston

and Beal, 1997) and reduce the sense of cohesion and

participation of community members (Witten et al.,

2001). Smailes (2000) argues that the removal of services,

such as banks and shops, to larger centres, undermines

community cohesion, as the mutually reinforcing nexus

between social and commercial activity is removed.

Another social dimension to depopulation is the

characteristics of those who stay and those who leave

declining rural areas. The majority of out-migrants are

between 15 and 35 years of age (Black et al., 2000;

Productivity Commission, 1999). The loss of this age

group often leads to a shortage of individuals who have

the capacity for maintaining and participating in

sporting clubs, recreational and volunteer organisations

which are often a strong component of a town’s sense of

identity and community (Productivity Commission,

1999). This group also represents the ‘child bearing’

section of the community, pointing to further popula-

tion decline (Hugo, 1994). Some studies from the US

and Australia have found that people who leave

declining rural areas are more likely to be better

educated and have greater job prospects (Fitchen,

1995), whilst those who move into these areas are

overwhelmingly from low-income groups (Fincher and

Wulff, 2001). Another feature of declining areas is the

migration of low-income families attracted by depressed

rural property prices and lower costs of living (Fincher

and Wulff, 1997). In some instances this has led to a high

concentration of low-income families in areas creating

an ‘us’ and ‘them’ mentality between newer and older

residents and inadequate levels of service provision for

new households (Fincher and Wulff, 1997).

The rural renaissance

Whilst rural decline is widespread, it is far from

uniform across all rural areas. The contrasting picture in

rural Australia, and many developed counties, is of

significant population growth and migration to pictur-

esque and coastal rural towns in close proximity to

major urban centres (known as exurban areas). No

single theory has been developed to explain rural

repopulation (Champion, 1989)—however, a number

of reasons have been suggested including the high value

placed on the amenity of rural areas, the desire for the

‘rural lifestyle’, the role of planning policies, regional

restructuring, the flexibility of labour and the flexibility

of capital in post-Fordist economies (Dahms and

McComb, 1999). In Australia, research suggests that

changing employment patterns have underpinned coun-

terurbanisation trends—with a range of industries

including transport, utilities and tertiary sectors all

experiencing growth in rural areas. Those employed in

these occupations, the 25–39 age group with young

families, dominated internal migration flows to rural

areas (Hugo, 1989). The movement of retirees and

others to coastal communities for lifestyle reasons and

the reduced cost of living has also been a driver of

growth in these regions (Curry et al., 2001). Other areas

of population growth are large inland regional centres

that are not dependent upon agriculture and mining

(Eves, 1998).

The reported impacts of counterurbanisation have

been mixed. The migration of sections of the urban

population to rural areas has been identified as bringing

a range of opportunities to local communities as there is

increased demand for new functions and service

(Paquette and Domon, 2003). These changes can lead

to increased rural income levels, employment opportu-

nities and net investment in the rural housing stock

(Stockdale et al., 2000). However, counterurbanisation

has been described as a double-edged sword (Fielding,

1990) creating tensions between ‘old’ and ‘new’ residents

in some communities (Curry et al., 2001; Jones and

Tonts, 2003), increased demand for limited housing

stock and reduced affordability for ‘locals’ (Jones and

Tonts, 2003), the impact of changing social structures on

local decision-making processes (Greive and Tonts,

1996), and the environmental impact of new housing


and agricultural pursuits (Black et al., 2000). US studies

that have examined rapid population growth have found

that it has been associated with a range of negative

social impacts such as rising crime rates, domestic

violence and increased use of alcohol and illicit drugs

(Broadway, 2000; Gouveia and Stull, 1995). This

population growth, however, has been the result of high

demand for (predominantly male) semi-skilled labour in

industries such as meatpacking (Broadway, 2000).

Population growth in rural Australia is not typically

driven by the same factors as the areas examined in US

research and there are no data to indicate rising crime

and social disorder in growing rural areas.

Mental health and population change

Mental health in rural areas has been poorly

investigated (Fraser et al., 2002). Most of the research

has focused on determining whether disorders are more

or less prevalent in rural or urban settings (Judd et al.,

2002). Little attention has been directed to investigating

how rural place and its economic, social, environmental

and socio-cultural components, might influence mental

health. We have proposed population growth and

decline as one index of place, and as described above,

it is of particular relevance to rural places. Population

change in rural areas is associated with two significant

factors that may influence mental health outcomes—

changes to population composition and changes to local

environments. We have previously argued (Judd et al.,

2002) that any investigation of the mental health of rural

residents needs to examine variables that relate to place

as well as to individual differences between people. Most

obviously, population change alters the composition of

a community, typically increasing the concentration of

low-income families in declining rural areas (Dear and

Wolch, 1987; Dembling et al., 2002; Fitchen, 1995;

Leighton et al., 1963). Numerous studies have shown

rates of almost all disorders decline monotonically with

increased income and education (Bruce et al., 1991;

Kessler et al., 1994), suggesting there may be a higher

prevalence of mental health problems in declining rural

areas, indicating that those who move to, or remain in,

declining rural areas are at greater risk of mental health

problems.

Secondly, population growth or decline is associated

with changes to the social, economic and physical

characteristics of a town (McKenzie, 1994a). Research

investigating the relationship between health, place and

income inequality provides a theoretical (Macintyre

et al., 2002) and empirical basis for understanding how

area interacts with individual-level variables to influence

health outcomes, with a range of studies demonstrating

the effect of local environments on mortality (Kaplan,

1996; Waitzman and Smith, 1998), chronic illness

(Robert, 1998), mental illness (Halpern, 1995) and

health behaviours such as smoking and alcohol use

(Curry et al., 1993; Reijneveld, 1998; Stead et al., 2001).

Whilst the relationship between health and place is

complex, a range of hypotheses has been proposed to

explain the mechanisms through which area influences

health. We draw upon two of these theories in

examining health and place in declining rural areas.

Lynch et al. (2001) have described the importance of

the ‘neo-material’ environment to health. This concept

refers to the ‘neo-material matrix of contemporary

life’—housing, health services, education, access to

healthy foodstuffs and recreational opportunities. The

authors argue that in disadvantaged communities the

neo-material environment is diminished due to the

limited resources of individuals and the systematic

underinvestment in community infrastructure by gov-

ernment (Lynch, 2000). A number of studies have found

that community infrastructures, such as transport,

schools and health centres, are required not only to

deliver health care or education but also as venues for

community interaction (Warin et al., 2000; Witten et al.,

2001). Such interaction can generate social capital, social

support and social inclusion (Warin et al., 2000), which

have a well-recognised health benefit (Kawachi and

Berkman, 2003). The importance of the neo-material

environment to health is also supported by the work of

Macintyre et al. (1993, 2002), who have argued that

living in particular areas can impact upon health

outcomes through access to resources important for

health and exposure to environmental stressors. In

declining and deprived areas there are fewer of the

resources required for residents to live a healthy life,

such as job opportunities, access to health care services,

adequate transport and good quality housing stock

(Black et al., 2000; Jones and Tonts, 2003; McKenzie,

1994a). Environmental stressors may be in the form of

crime and violence (which tend to be higher in deprived

areas) (Stead et al., 2001) or from air and water

pollutants (Halpern, 1995). However, there are no data

to indicate that these characteristics are more prevalent

in declining rural areas.

The present study examined how population growth

and decline is related to self-reported mental health in

rural settings. Using a case study approach, four local

government areas (LGAs) were selected which are

typical of the types of communities experiencing

population change (declining agricultural regions, de-

clining mining communities, growing exurban areas and

growing regional cities). LGAs refer to the catchment

area of local councils. These LGAs also represent the

extremes of population growth and decline in our

sample. We tested three hypotheses regarding popula-

tion change and mental health: (1) that those in

declining areas would be older, less educated and more

likely to be unemployed; (2) that those in declining areas

ARTICLE IN PRESS

Fig. 1. Case study areas.

C. Fraser et al. / Health & Place 11 (2005) 157–171 161

would score higher (i.e., poorer) on measures of mental

ill-health and higher on measures of substance use; and

(3) that those in declining areas would score lower on

measures of mental well-being. We believed that the

demographic characteristics of declining towns and the

characteristics of the places themselves would explain

the poorer mental health status of residents in these

areas.

We selected measures of anxiety, depression and

substance misuse (smoking, drinking and drug use) as

measures of mental ill-health, as they are the most

common disorders in the western societies, including the

USA (Kessler et al., 1994), UK (Paykel et al., 2000),

Australia (Andrews et al., 1999) and New Zealand

(Oakley-Browne et al., 1989). We extended previous

work reviewed by Judd et al. (2002) by including

measures of disability and well-being alongside measures

of mental health symptoms. This allowed us to take a

broader perspective looking beyond morbidity alone

by examining measures of functioning and satisfaction

with life.

Method

This study was part of a larger project investigating

the mental health of rural Australians in which 20,000

residents were randomly sampled from the electoral roll

in Victoria and New South Wales (NSW)—Australia’s

two most populous states. The geographic area of the

sampling frame covered approximately 540,000 km2,

beginning in southern Victoria and extending to north-

ern NSW (or the NSW/Queensland border). The total

population of the area was approximately 400,000

people. The population was predominantly Australian-

born and included relatively large indigenous commu-

nities in central and northern NSW and northern

Victoria. The average unemployment rate for the region

was approximately 10%; however, this varies across the

region. Agriculture, forestry and fishing and government

services are the region’s major employers. Significant

proportions of the workforce are also employed in retail,

manufacturing, education and health and community

services sectors.

From this sample four LGAs were selected. The areas

were selected because they are characteristic of commu-

nities experiencing population growth and decline across

rural Australia. Therefore, we selected a declining

agricultural region, a declining mining region, a growing

regional city and a picturesque growing exurban area.

The communities

The communities selected were the shire of Broken

Hill, located in the far west of NSW, the shire of Dubbo

located in central NSW, the shire of Buloke in central

Victoria and the shire of Macedon Ranges in southern

Victoria and are shown in Fig. 1. Demographic details

of the shires are listed in Table 1.

The demographic characteristics of the four regions

vary considerably, with the declining communities

having a greater proportion of older residents—a typical

difference between declining and growing communities.

The history and economic base of the areas also vary

considerably.

The city of Broken Hill is located in outback NSW

and covers an area of approximately 70 km2. This city

has a prominent place in Australian history due to its

mining wealth and strong history of union activity. In

1985, four mines were operating in Broken Hill,

employing approximately 4000 people, but by 1995

three mines had closed due to the high cost of extraction

(New South Wales Department of Mineral Resources,

2002). The remaining working mine in the town is

scheduled to close in less than 15 years. Without the

discovery of further mineral deposits in the area, the

future for Broken Hill appears bleak (New South Wales

Department of Mineral Resources, 2002).

The Shire of Buloke is located in North Western

Victoria and encompasses five major towns and five

smaller townships. Agriculture accounts for 65% of

businesses in the shire; however, it only accounts for

10% of employment. Government sector services

(health, community services and education), retail trade

and manufacturing are the largest employers in the

region (Department of Education and Training, 2003).

Since the 1980s, the shift to a post-industrial economy

has led to changes in the region including a decrease in

the number of people employed in the agriculture,

forestry and fishing, public administration and

defence sectors (Department of Education and Training,

2003).

ARTICLE IN PRESS

Table 1

Australian bureau of statistic regional statistics data—Victoria and New South Wales 1999

Variables Growing communities Declining communities

Macedon Ranges Dubbo Buloke Broken Hill

Estimated resident population 35,844 37,396 7518 20,934

Annual population growth 1991–2001 1.9% 1.35% �1.85% �1.45%

Percentage persons aged 0–14 years 25.00% 25.50% 19.50% 20.60%

Percentage persons aged 65+ 8.80% 10.50% 19.50% 17.20%

Unemployment rate 5.90% 4.80% 4.70% 9.30%

Centrelink customersa 2003 7819 1863 7425

Centrelink customers as a % of population (15 years+) 20.62% 28.10% 30.81% 44.70%

Average annual taxable income $35,071 $32,370 $24,532 $33,581

a Centrelink is the government agency responsible for the distribution of social security payments including unemployment and

disability and sickness benefits and pensions.

C. Fraser et al. / Health & Place 11 (2005) 157–171162

Macedon Ranges Shire is located in central Victoria

and is within close proximity to the state’s capital city.

The region is picturesque and one of the fastest growing

shires in the state (Australian Bureau of Statistics,

2003a, b). Growth in the region is principally due to the

migration from the state’s capital Melbourne, and 51%

of the Shire’s working population commute to Mel-

bourne for work (an increase from 14% since 1991)

(Department of Human Services, 1999). There have

been changes in employment in the region, with an

increase in employment in the service industries and a

decline in the number of people employed in the primary

industries (Department of Human Services, 1999).

The city of Dubbo is located in central NSW and is a

service centre for the agricultural towns in the region

(Eves, 1998). Much of the area surrounding Dubbo is

used to farm cattle, sheep and wheat. Light industry and

the service sector also make up a significant proportion

of economic activity within the city itself. The popula-

tion growth that has been experienced in the city is

strongly associated with the decline of smaller towns and

the population shift to Dubbo (Eves, 1998).

Measures

Socio-demographic variables

Nine socio-demographic variables were measured

essentially for descriptive purposes: age, gender, rela-

tionship status, highest level of education (a surrogate

measure of SES, as previously employed by, for

example, Blazer et al., 1985), employment, nationality,

indigenous heritage, duration of local residence and

household composition. However, as specified pre-

viously, for three of the variables, namely age, education

and employment, we hypothesised differences between

growing and declining areas.

Mental health measures

Three scales were used to measure anxiety and

depressive symptoms: the positive and negative affect

scale (PANAS) (Watson et al., 1988), the K10 (Kessler

and Mroczec, 1994; Kessler and Mroczec, 1992) and the

SPHERE-12 (Hickie et al., 2001).

The 20-item PANAS was used to measure mood

characterised in terms of two quasi-orthogonal mea-

sures: positive affect (PA) and negative affect (NA).

Extensive research suggests that PANAS is a reliable

and valid measure of the two fundamental constructs

(Watson et al., 1988). Given that the authors of the

PANAS did not provide cut-points, based on the

distributions of the current data set, a score of 26 or

more was accepted as indicative of high scores on PA,

whereas a score of 21 or more was accepted as indicative

of high scores on NA (Murray et al., in press).

The K10 is a 10-question screening scale of psycho-

logical distress, containing items about the experience of

anxiety and depressive symptoms over the preceding 4

weeks (Kessler and Mroczec, 1994). High scores are

associated with the presence of diagnosed anxiety and

mood disorders as well as consultations for a mental

problem (Andrews and Slade, 2001). Internal reliability

for the K10 has been reported as 0.93, and the

instrument strongly discriminates between community

cases and non-cases of DSM-IV disorders (Kessler et al.,

2002). Given the distribution of the data in the current

study, a score of 21 and above was accepted as indicative

of high levels of distress (see Murray et al., in press).

SPHERE-12 is a short (12-item) form of the SPHERE

questionnaire, designed to measure psychological and

somatic symptoms in a primary care population (Hickie

et al., 2001). Two scores are derived from the scale.

‘‘Psych6’’ refers to psychological symptoms (both

anxiety and depressive) and ‘‘Soma6’’ refers to somatic

symptoms. The internal reliability for Psych6 and


Soma6 has been reported as 0.90 and 0.80, respectively

(Hickie et al., 2001). The authors of the scale stipulated a

cut-point of three or more on the Soma6 as indicative of

caseness for somatic symptoms, and two or more on the

Psych6 as indicative of caseness for psychological

symptoms (Hickie et al., 2001).

For substance use, participants were assessed on their

use of alcohol, cigarettes, marijuana and amphetamines

by single-item questions. Participants were asked to

choose the option that best described their typical use of

substances. For the purposes of this report, we

dichotomised the responses as follows: for alcohol, the

options were: alcohol 1=never or rarely drinks versus

2=drinks 1or 2 or more standard drinks per day. For

cigarette use: 1=non-smoker (plus ex-smoker) versus

2=smokes. For marijuana use: 1=never used versus

2=used, whilst for amphetamine use: 1=never used

versus 2=used.

Disability

The SF-12 is an abbreviated (12-item) version of the

Medical Outcomes Study 36-Item Short Form Health

Survey (Ware et al., 1996)—a multipurpose short-form

measure of disability. Two subscale scores (physical

component summary (PCS) and mental component

summary (MCS)) are calculated using weights designed

to maximise separation between the two factors. Given

the distribution of the scores in the current study, scores

of 41 or more for the PCS, and 46 or more for the MCS,

respectively, were indicative of good functioning (see

Murray et al., in press).

Well-being

Satisfaction with life was measured on the five-item

satisfaction with life scale (Diener et al., 1985), for which

a range of validating data exists (Pavot and Diener,

1993). Internal consistency has been reported as

0.87, and 2-month test–retest reliability at 0.82

(Diener et al., 1985). Again, given the absence of a

recognised cut-point, based on the distribution of the

current data set, a score of 21 or more was accepted as

indicative of greater satisfaction with life (see Murray

et al., in press).

Results response rate

Of the initial 20,000 individuals contacted, 3106 were

identified as ineligible (2823 incorrect addresses,1 155

deceased, 128 incapable). Of these, 1922 could be

1 The high number of incorrect addresses was due to the age

and accuracy of the electoral roles used for sampling. Whilst the

most up-to-date version of the roles were obtained for the study

they were still approximately 3 years old.

replaced in the sampling frame during the surveying

period, leaving 1184 individuals from the original frame

who were ineligible and not replaced. Response rate was

calculated as (n returned)/(n mailed�n ineligible) (de

Vaus, 1991). Using this formula, the response rate was

40.5% (N ¼ 7615).

From the sample 1334 respondents were identified as

living in the four LGAs selected. Using the formula

mentioned above, response rates were calculated for

each area with the Shire of Buloke having a significantly

higher response rate (54.4%) compared with Macedon

Ranges (32.4%), Dubbo (31.7%) and Broken Hill

(31.3%).

Socio-demographic variables

Inspection of Table 2 shows that the overall sig-

nificance value for all eight variables achieved statistical

significance.

The mean age of Macedon Ranges residents in our

sample was significantly lower than those of Broken

Hill and Buloke as confirmed by direct contrasts,

although the comparison between Macedon and

Dubbo showed a trend towards significance in the

same direction (p ¼ 0:058). Broken Hill citizens in

our sample were significantly younger than those

living in Buloke (p ¼ 0:03). Dubbo residents in our

sample were also found to be younger than residents

living in Buloke (p ¼ 0:004). There were no differences

between Broken Hill and Dubbo on this variable

(p ¼ 1:00).

As regards gender, a greater percentage of respon-

dents in Dubbo was female than was the case in

the other three LGAs. For marital status, inspection

of the adjusted residuals found that the proportion

of Broken Hill and Buloke residents who were

married was significantly less than the proportion

of residents of Dubbo who were married. Macedon

Ranges had the smallest proportion of people living

alone, followed by Dubbo (see Table 2). The adjusted

residuals found that the proportion of people alone in

Macedon Ranges was smaller than expected by chance,

whereas the percentage living alone in Buloke was

greater than expected by chance. There were no other

significant differences between the four LGAs for

this variable.

Adjusted residuals indicated that individuals living in

Macedon Ranges and Dubbo were more likely to be

employed compared with individuals living in Buloke.

The data in Table 2 also show differences in the

percentages of people with post-secondary qualifications

training. Inspection of adjusted residuals shows that

Macedon Ranges’ residents were more likely to have

post-secondary qualifications compared with residents

in Buloke. There were no other differences between the

four LGAs.

ARTICLE IN PRESS

Table 3

Mental health, disability and satisfaction with life variables

Variable Growing Declining

Macedon Ranges n ¼ 210 Dubbo n ¼ 540 Buloke n ¼ 196 Broken Hill n ¼ 388

No. % No. % No. % No. %

SWL 169 82.0 379 74.0 128 68.1 258 70.7

MCS 153 73.2 401 74.5 136 69.7 281 72.4

PCS 176 84.2 393 73.0 134 68.7 266 68.6

Positive affect 157 77.3 383 75.1 133 72.3 252 70.2

Negative affect 154 75.9 403 78.9 141 77.0 258 72.3

Psych6 151 72.2 371 70.7 128 68.1 229 60.6

Soma6 138 66.7 346 65.4 117 61.6 196 52.0

K-10 155 73.8 395 73.6 131 67.2 265 68.5

Note: All numbers and percentages shown represent people who are better functioning and are non-cases.

Table 2

Socio-demographic variables for the four local governmental areas


Macedon

Ranges

n ¼ 210

Dubbo

n ¼ 540

Buloke

n ¼ 196

Broken Hill

n ¼ 388

Chi-Square or

ANOVA

Significance

value

Age (age in years) means (SDs) 47.64 (15.43) 51.07 (16.18) 55.73 (17.04) 51.76 (16.12) F ð3Þ ¼ 8:51 0.000

Gender (no. and % of females) 109 (52%) 333 (62%) 106 (55%) 207 (55%) X 2ð3Þ ¼ 8:58 0.04

Married (no. and %) 156 (75%) 410 (77%) 125 (64%) 262 (67%) X 2ð3Þ ¼ 14:18 0.003

Living alone (no. and % alone) 19 (9%) 77 (14%) 51 (26%) 70 (18%) X 2ð3Þ ¼ 25:01 0.000

Employment (no. and

% employed)

141 (67%) 321 (60%) 95 (49%) 179 (46%) X 2ð3Þ ¼ 33:10 0.000

Education (no. and

% tertiary educated)

121 (57%) 227 (42%) 56 (29%) 153 (40%) X 2ð3Þ ¼ 34:33 0.000

Nationality (no. and

% Australian born)

176 (84%) 511(94%) 186 (96%) 368 (95%) X 2ð3Þ ¼ 36:30 0.000

Aboriginal or Torres Street

Islander (no. and %)

2 (1.0%) 14 (2.6%) 1 (0.5%) 8 (2.1%) X 2ð3Þ ¼ 4:49 0.21

Duration of residence in the

area (no. and % with less than 5

years in area)

22 (10.5%) 38 (7.1%) 19 (4.9%) 7 (3.6%) X 2ð3Þ ¼ 10:06 0.02


Table 2 indicates that Macedon Ranges had the

lowest proportion of Australian-born citizens. The

adjusted residuals data confirmed that this was indeed

the only significant difference for the four LGAs.

Differences in the proportions of people of Aboriginal

or Torres Street Islander descent were not significantly

different across the four LGAs.

Finally, as regards the duration of time a person was

resident in the area (coded more than 5 years in the area

versus 5 years or less in the area), adjusted residuals

showed that Macedon Ranges contained a greater

proportion of new residents than would be expected by

chance.

Mental health, disability and satisfaction with life

variables

Table 3 shows the numbers and percentages of people

within each of the four LGAs who have high levels of

satisfaction with life and do not have mental (MCS) or

physical (PCS) disability. For all three variables, both

growing areas obtained higher percentages than the two

declining areas and for two of those three variables,

Macedon Ranges contained higher percentages of

‘‘better functioning’’ people than Dubbo.

Table 3 indicates that for the two measures of

affect—positive and negative, both growing areas

ARTICLE IN PRESS

Table 4

Logistic loglinear models for psychological variables and local government area: contrasts for growth/decline

Variable Comparison of growth/decline model

with saturated model: difference in

likelihood ratio statistics (df=2)

Comparison of growth/decline model

with main effects model: difference in

likelihood ratio statistics (df=1)

Odds ratios for growth/decline

contrast (with 95% confidence

intervals)

SWL 5.82 (p ¼ 0:054) 6.78 (po0:05) 1.18 (1.04–1.34)

MCS 0.59 (p > 0:05) 1.15 (p > 0:05) 1.07 (0.95–1.20)

PCS 10.97 (po0:05) 9.41 (po0:05) 1.21 (1.07–1.37)

PA 0.66 (p > 0:05) 3.70 (p > 005) 1.13 (0.998–1.28)

NA 2.21 (p > 0:05) 2.87 (p > 005) 1.12 (0.98–1.28)

Psych6 3.25 (p > 0:05) 9.41 (po0:05) 1.20 (1.07–1.35)

Soma6 4.84 (p > 0:05) 15.0 (po0:05) 1.25 (1.12–1.40)

K10 0.11 (p > 0:05) 4.96 (po0:05) 1.15 (1.02–1.29)

C. Fraser et al. / Health & Place 11 (2005) 157–171 165

contained a higher percentage of people who scored

over the cut-score in terms of PA and under the cut-

point for NA.

Table 3 shows that the two growing areas have higher

proportions of people who are non-cases on the two

SPHERE subscales and the K-10 than the two declining

areas. For the Somatic subscale of the SPHERE, Broken

Hill contained a comparatively low number of non-cases.

Table 4 provides results for logistic regression models

for each of the dichotomised psychological variables

depicted in Table 3, using a growth/decline contrast. The

saturated model includes the interaction between the

psychological variable and the categorical variable

specifying the four LGAs. The growth/decline model

includes the interaction between the psychological

variable and a categorical variable pertaining to

growth/decline (in fact, a contrast on the four-category

LGA variable). The main effects model includes only the

main effect for LGA. If there is a significant difference

between the growth/decline model and the saturated

model (column 2 in the table), then the growth/decline

contrast does not explain all of the distribution of the

psychological variable across the four LGAs. If there is

a significant difference between the growth/decline

model and the main effects model, however, (column

3) the growth/decline contrast explains some of the

distribution of the psychological variable. The fourth

column provides 95% confidence intervals for the odds

ratio for the growth/decline contrast in the growth/

decline model. As usual, a confidence interval for the

odds ratio that does not contain 1 represents a

significant result. The coding of the variables is such

that a confidence interval above 1 indicates a better

outcome in growing LGAs.

Accordingly, a pattern with po0:05 in column 3 and

p > 0:05 in column 2 provides evidence that variation in

the psychological measures across the four LGAs is

significant and that this variation can be more parsimo-

niously explained by growth/decline rather than by the

four-fold categorisation. We see this result for Psych6,

for Soma6 and for K10, and marginally for SWL. A

pattern with po0:05 in column 3 and po0:05 in column

2 provides evidence that variation in the psychological

across the four LGAs is significant, that this variation

can be partly explained by growth/decline, but there is

still some variation attributable to differences among the

individual LGAs. We see this pattern for PCS. Finally, a

pattern with p > 0:05 in both columns 2 and 3 suggests

that the psychological variable is independent of LGA

and of growth/decline. We see this for MCS, PA and

NA.

For the variables Psych6, Soma6, K10 and SWL, we

investigated a logistic regression model controlling for

sex, age category (less than 30, 30–50, 50–70, over 70),

education (tertiary or not), nationality (country of

origin), duration of time domiciled in the area and

household arrangements (living alone or not). In each of

these models controlling for demographic characteris-

tics, the odds ratio for the growth contrast remained

significant (for K10, 1.14, 95% CIs=1.00–1.29; for

Psych6, 1.19, 95% CIs=1.05–1.34; for Soma6, 1.22,

CIs=1.08–1.37; for SWL, 1.15, CIs=1.01–1.31).

Employment status was not included in the model as it

may be either an outcome or a compositional factor

(part of the growth/decline construct).

Substance use

Substance use across the four LGAs is shown in

Table 5 whilst Table 6 contains the logistic regressions

for substance abuse variables and the LGA contrasts for

growth/decline.

The two tables for dichotomous substance abuse

variables show no evidence that growth/decline relates

to substance use. The coding of the variables in Table 4

is such that a confidence interval above 1 indicates

proportionately lower substance use in growing areas.

The 95% confidence interval for alcohol suggests the

possibility of a trend for proportionately greater alcohol

ARTICLE IN PRESS

Table 6

Logistic loglinear models for substance abuse variables and local government area: contrasts for growth/decline

Variable Comparison of growth/decline

model with saturated model:

difference in likelihood ratio

statistics (df=2)

Comparison of growth/decline

model with main effects model:

difference in likelihood ratio

statistics (df=1)

Generalised odds ratios for

growth/decline contrast (with

95% confidence intervals)

Alcohol 4.56 (p > 0:05) 2.41 (p > 0:05) 0.92 (0.82–1.02)

Smoking 5.65 (p ¼ 0:059) 0.11 (p > 0:05) 0.98 (0.86–1.12)

Marijuana 0.45 (p > 0:05) 0.67 (p > 0:05) 0.88 (0.64–1.20)

Note: Because of the very small number of amphetamine users within each of the four local government areas, loglinear modelling was

not conducted for amphetamine use.

Table 5

Numbers (and percentages) of people engaging in substance use across the four local government areas


Macedon Ranges

n ¼ 210

Dubbo

n ¼ 540

Buloke

n ¼ 196

Broken Hill

N ¼ 388

Smoking (no. and % of smokers) 42 (20%) 111 (21%) 28 (14%) 87 (23%)

Alcohol use (no. and % who drink alcohol regularly) 96 (46%) 202 (37%) 70 (36%) 137 (35%)

Amphetamine use (no. and % of amphetamine users) 2 (1%) 4 (0.7%) 0 (0%) 3 (0.8%)

Marijuana use (no. and % of marijuana users) 8 (3.8%) 20 (3.7%) 7 (3.6%) 10 (2.6%)


use in growing areas (in fact, this trend is largely being

driven by greater alcohol usage in Macedon shown in

Table 3).

We also investigated logistic regressions for a three-

level categorisation of alcohol and smoking behaviours

(no use, occasional or light use, heavy use). Again, there

was no evidence for the importance of a growth/decline

contrast for alcohol use. For cigarette use, an interaction

effect between smoking and growth suggested evidence

for proportionately more heavy, rather than light,

smoking in declining areas, with the odds-ratio esti-

mated at 1.33, with 95% confidence intervals from 1.04

to 1.71.

Discussion

Few studies have examined the impact of local

environments outside urban settings and as Kobetz

et al. (2003) have argued, there are limited theoretical

perspectives to assist in understanding how rural settings

influence health. We have drawn upon the health and

place literature to understand how one aspect of place in

rural environments, population growth and decline, can

influence mental health. In addition, we have also

examined migration as a factor that may influence the

prevalence of mental health problems in rural areas.

There have been numerous studies into migration and

mental illness (Dear and Wolch, 1987; Dembling et al.,

2002; Gleeson et al., 1998). These studies have tended to

focus on the migration and concentration of people

experiencing mental illness in inner-city ‘service depen-

dent ghettos’. In contrast to these studies we focused on

the movement of people to, and within, rural areas and

its impact upon the prevalence of mental health

problems in these communities.

This study found clear demographic differences

between growing and declining areas that support our

initial hypothesis that residents in growing areas would

be more likely to be younger, employed and have higher

levels of education; however, the pattern was more

complex than initially anticipated. The disparity be-

tween growing and declining regions was driven

primarily by differences between the Shires of Macedon

Ranges and Buloke. On a number of variables (e.g., age,

education, living alone and marital status) there were no

significant differences between the LGAs of Dubbo and

Broken Hill. There were also differences within growing

and declining areas. There was a higher proportion of

people who had tertiary education and were employed in

Macedon Ranges, compared with Dubbo, and the

average age of Dubbo residents was higher. In the

declining regions a lower proportion of those living in

Buloke had tertiary qualifications, whilst the average age

of Buloke residents was also higher compared with

Broken Hill. These findings indicate that population

growth and decline did not lead to uniform demographic

changes, rather it produced different outcomes in


different areas. In addition to examining growth or

decline it may also be necessary to understand the

drivers of these changes. In this study, growth in

Macedon Ranges is predominantly due to exurban

migration, bringing younger, more affluent families into

the region (Department of Human Services, 1999). The

growth of Dubbo, by contrast, has been attributed to

the decline of smaller rural towns in the region and the

subsequent migration of residents from these declining

towns to Dubbo (Eves, 1998). Decline in Buloke is due

to the downturn in agriculture (Department of Educa-

tion and Training, 2003) whilst the decline in Broken

Hill is primarily due to the closure of the major mines in

the area (New South Wales Department of Mineral

Resources, 2002).

In addition to examining compositional differences

between growing and declining regions, we also looked

at contextual differences that were likely to occur in

these areas. Population decline has clear impacts upon

place in terms of both the built environment and the

socio-cultural aspects of the environment (Black et al.,

2000; Jones and Tonts, 2003; McKenzie, 1994a). Lynch

et al.’s (Lynch, 2000) concept of neo-material environ-

ment is particularly useful in understanding how these

environmental characteristics can influence health out-

comes. This concept has particular resonance for

declining rural towns, where communities have experi-

enced deterioration in their material living condition

through growing unemployment and the concurrent

withdrawal of community infrastructure such as schools,

transport and hospitals. Again, however, our findings

only provide some support for these arguments. On the

measures of mental health and mental well-being there

were a number of small but significant differences (with

odds ratios indicating about 15–25% difference) be-

tween growing and declining areas and across the four

regions. In general, the data tended to indicate that

those living in declining areas were less happy (lower

PA), less satisfied with life, and had a greater number of

psychological and physical symptoms. However, there

were a number of measures on which there were no

differences between growing and declining communities.

Drug and alcohol use does not vary across the regions.

There was a trend, although not significant, for people

who lived in growing areas being more likely to report

alcohol use. This trend may in part be due to the

younger average age of people living in Macedon

Ranges, as in Australia, older age groups (50 years

and over) are less likely to be smokers and drinkers

(AIHW, 2002; Fleming, 2003).

In general, the findings of this study of population

growth and decline have some relevance to health that

may be due to the spatial redistribution of those at risk

of mental illness, or due to changes in the neo-material

environment, or social comparisons. Future research

investigating the spatial variations in mental health and

well-being across rural communities requires a more

fine-grained, longitudinal approach that examines the

physical and social properties of rural communities and

the processes of change in these areas.

Limitations

First, the research was not conducted longitudinally

and as such a causal relationship cannot be drawn from

the associations identified between declining rural

communities and the socio-demographic characteristics

and psychological measures. The second limitation

concerns the representativeness of the sample. Although

potential participants were randomly selected from the

electoral roll in each area, the design cannot guarantee

that respondents were representative of these commu-

nities and the communities selected for the study may

not reflect the experiences of all declining and growing

rural areas. It is also possible that a bias was produced

by the reliance upon self-report questionnaires, as those

with severe mental health problems may be less likely to

participate.

Another limitation of the study was the geographic

areas sampled and the geographic units of analysis used.

The study did not include a growing coastal town.

Coastal towns are some of the fastest growing rural

regions in Australia. Unlike the growing exurban area

that was sampled in this study, which predominantly

attracts high-income families, studies of coastal towns

have found that they can attract both high- and low-

income households (Beer et al., 1994). This factor limits

the applicability of findings of the study to growing

coastal regions. The study also used LGAs. LGAs are

useful in that data are routinely collected at the LGA

level. However, these districts can be both spatially and

compositionally diverse. Thus, one town’s experiences

may be different from those of another town even

though they ‘‘placed’’ within the same LGA. This

suggests the importance of the concept of ‘‘place’’ and

use of smaller geographic units of analysis.

Conclusion

The current study is important because to the best of

our knowledge this is the first study to empirically

examine mental health, substance use, well-being and

disability across four areas indicative of either popula-

tion growth or decline. The study used measures with

good psychometric properties to index the various

constructs of interest. A large population was surveyed

via a postal survey and we obtained a good response rate

for such a survey.

This study found that there is some relationship

between mental health and living in a community with a


declining population. Importantly, our significant

results for three of the mental heath measures

(i.e., Psych6, Soma6, K10) and the SWL—a measure

of well-being, held up even when six demographic

variables were entered into the regression models as

covariates. This suggests, that at least for the demo-

graphic variables we investigated, it is simply not an

effect of composition of these areas that accounts for

the results. There is evidence that the very fact of

living in declining areas might lead to some vulnerability

in terms of mental health and reduced well-being.

However, the study also raised questions about the

causal mechanisms between population change and

mental health. The findings suggest that drivers

behind population growth and decline could play a

role in influencing the level of mental health problems

in a community; nonetheless, further longitudinal

studies are required to develop a better understanding

of this.

Whilst this study has focused on the Australian

context, the findings have broader relevance. The

experiences of rural desertification and counterurbanisa-

tion are not unique to Australia, but continue to occur

in many developed nations including the US (Beale,

1977), UK (Cawely, 1984; Stockdale et al., 2000), parts

of Europe including France (Dean, 1988), Germany

(Kontuly and Vogelsang, 1988), Italy (Dematteis, 1986)

and Norway (Illeris, 1984). Based on this study,

generalisations about the specific impacts of mental

health on these changes cannot be made. However,

data strongly suggest that changes to place that are

important to health have occurred in many communities

that have experienced population growth or decline.

Impacts that have been reported in studies from the US,

the UK and Europe include increased fear of crime

(Hunter et al., 2002) increased rates of crime and

other social problems (Broadway, 2000), hostility

between ‘locals’ and ‘newcomers’ (Murdoch and Day,

1998), rising property prices (Gilligan, 1987), changes

in local employment patterns and the abandonment

of formerly productive land (Cawley, 1994). As popula-

tion growth is a symptom of broader dynamics,

the drivers behind growth and decline in these commu-

nities will also influence how these changes are played

out at the local level and the impact they may have on

health.

What is clearly demonstrated in the study is that there

are different patterns of mental health among rural

communities. This finding is important in terms of rural

mental health policy and research, which tends to have

an unsophisticated conceptualisation of what is ‘rural’

and treats all areas beyond the metropolitan fringe as

homogeneous. The study also suggests that population

growth and decline is an important aspect of place that

may warrant further investigation in terms of its impact

on health outcomes.

Acknowledgements

The authors gratefully acknowledge the financial

support provided by beyondblue: The National Depres-

sion Initiative.

References

Andrews, G., Slade, T., 2001. Interpreting scores on the Kessler

psychological distress scale (K10). Australian and New

Zealand Journal of Public Health 25, 494–497.

Andrews, G., Hall, W., Teeson, M., Henderson, S., 1999. The

National Survey of Mental Health and Wellbeing: the

Mental Health of Australians, Mental Health Branch.

Commonwealth Department of Health and Aged Care,

Canberra.

Argent, N., Rolley, F., 2000. Financial exclusion in rural and

remote New South Wales, Australia: a geography of bank

branch rationalisation, 1981–1998. Australian Geographical

Studies 38, 182–203.

Australian Bureau of Statistics, 2003a. Australian Social

Trends. Family and Community—Living Arrangements:

Farming Families. Australian Bureau of Statistics,

Canberra.

Australian Bureau of Statistics, 2003b. Estimated Resident

Population. Local Government Areas, Victoria (Table 1b)

[Cat. No. 3218.0.55.001].

Australian Institute of Health and Welfare (AIHW), 2002. 2001

National Drug Strategy Household Survey First

Results. Australian Institute of Health and Welfare,

Canberra, PHE 35.

Beale, C., 1977. The recent shift of United States population to

non-metropolitan areas. International Regional Science

Review 2, 113–122.

Beer, A., Bolam, A., Maude, A., 1994. Beyond the Capitals:

Urban Growth in Regional Australia. Australian Govern-

ment Publishing Service, Canberra.

Black, A., Duff, J., Saggers, S., Bains, P., Jennings, A., Bowen,

P., 2000. Rural Communities and Rural Social Issues:

Priorities for Research. Rural Industries Research and

Development Corporation, Canberra.

Blazer, D., George, L.K., Landerman, R., Pennybacker, M.,

Melville, M.L., Woodbury, M., Manton, K.G., Jordan, K.,

Locke, B., 1985. Psychiatric disorders. A rural/urban

comparison. Archives of General Psychiatry 42, 651–656.

Broadway, M.J., 2000. Planning for change in small towns or

trying to avoid the slaughterhouse blues. Journal of Rural

Studies 16, 37–46.

Bruce, M.L., Takeuchi, D.T., Leaf, P.J., 1991. Poverty and

psychiatric status: longitudinal evidence from the new haven

epidemiologic catchment area study. Archives of General

Psychiatry 48, 470–474.

Budge, T., 1996. Population decline in Victoria and Tasmania.

In: Newton, P., Bell, M. (Eds.), Population Decline in

Victoria and Tasmania. Australian Government Publishing,

Canberra, pp. 192–204.

Burch, D., Rickson, R., 2001. Industrialised agriculture:

agribusiness, input-dependency and vertical integration.

In: Lockie, S., Bourke, L. (Eds.), Rurality Bites: the Social


and Environmental Transformation of Rural Australia.

Pluto, Annandale, pp. 165–178.

Cawley, M.E., 1994. Desertification: measuring population

decline in rural Ireland. Journal of Rural Studies 10,

395–407.

Champion, A., 1989. Counterurbanization. Edward Arnold,

London.

Cloke, P., 1996. Rural life-styles: material opportunity, cultural

experience, and how theory can undermine policy. Econom-

ic Geography 72, 433–449.

Curry, G., Koczberski, G., Selwood, J., 2001. Cashing out,

cashing in: rural change on the south coast of Western

Australia. Australian Geographer 32, 109–124.

Curry, S.J., Wagner, E.H., Cheadle, A., Diehr, P., Koepsell, T.,

Psaty, B., McBride, C., 1993. Assessment of community-

level influences on individuals’ attitudes about cigarette

smoking, alcohol use, and consumption of dietary fat.

American Journal of Preventive Medicine 9, 78–84.

Curtis, S., Rees Jones, I., 1998. Is there a place for geography in

the analysis of health inequality. In: Bentley, M., Blanc, D.,

Dovey Smith, G. (Eds.), The Sociology of Health Inequal-

ities. Blackwell Publishers, Oxford, pp. 85–111.

Dahms, F., McComb, H., 1999. Counterurbanisation, interac-

tion and functional change in a rural amenity area—a

Canadian example. Journal of Rural Studies 15, 129–146.

Dean, K.G., 1988. Inter-regional flows of economically active

persons in France 1975–1982. Demography 25, 81–98.

Dear, M., Wolch, J., 1987. Landscapes of Despair: From

Deinstitutionalization to Homelessness. Polity, Cambridge.

Dematteis, G., 1986. Urbanisation and counterurbanisation in

Italy. Ekistics 316/317, 26–33.

Dembling, B.P., Rovnyak, V., Mackey, S., Blank, M., 2002.

Effect of geographic migration on SMI prevalence esti-

mates. Mental Health Services Research 4, 7–12.

Department of Education and Training, 2003. Local learning

and employment network statistics resource. Department of

Education and Training, Victoria.

Department of Human Services, 1999. A Profile of the Shire

of Macedon Ranges. Department of Human Services,

Bendigo.

Diener, E., Emmons, R.A., Larsen, R.J., Griffin, S., 1985. The

satisfaction with life scale. Journal of Personality Assess-

ment 49, 71–75.

de Vaus, D.A., 1991. Surveys in Social Research. Allen &

Unwin, St Leonards.

Eves, C., 1998. A comparison of agricultural, residential and

commercial property in rural regions. Australian Property

Journal, August, 273–278.

Fielding, A., 1990. Counterurbanisation: threat or blessing? In:

Pinter, D. (Ed.), Western Europe: Challenge and Change.

Belhaven, London, pp. 226–239.

Fincher, R., 1999. New geographies of disadvantage: implica-

tions for service delivery. Australian Journal of Public

Administration 58, 55–60.

Fincher, R., Wulff, M., 1997. Locations of poverty and

disadvantage. In: Fincher, R., Nieuwenhuysen, J. (Eds.),

Australian Poverty Now and Then. Melbourne University

Press, Melbourne, pp. 144–164.

Fincher, R., Wulff, M., 2001. Moving in and out of

disadvantage: population mobility and Australian places.

In: Fincher, R., Saunders, P. (Eds.), Creating Unequal

Futures?: Rethinking Poverty, Inequality and Disadvan-

tage. Allen & Unwin, Crows Nest, pp. 158–193.

Fitchen, J.M., 1995. Spatial redistribution of poverty through

migration of poor people to depressed rural communities.

Rural Sociology 60, 181–202.

Fleming, J., 2003. The epidemiology of alcohol use in

Australian women: findings from a national survey of

women’s drinking. Addiction 91, 1324–1334.

Fraser, C., Judd, F., Jackosn, H., Murray, G., Humphrey, J.,

Hodgins, G., 2002. Does one size really fit all? Why the

mental health of rural Australians requires further research.

Australian Journal of Rural Health 10, 288–296.

Gilligan, J., 1987. Visitors, tourists, and outsiders in a Cornish

town. In: Bouquet, M., Winter, M. (Eds.), Who from their

Labours Rest? Conflict and Practice in Rural Tourism.

Aldershot, Avebury, pp. 65–82.

Gleeson, B., Hay, C., Law, R., 1998. The geography of mental

health in Dunedin, New Zealand. Health and Place 4, 1–14.

Gouveia, L., Stull, D.D., 1995. Dances with cows: beefpack-

ing’s impact on garden city, Kansas and Lexington,

Nebraska. In: Stull, D.D., Broadway, M.J., Griffith, D.

(Eds.), Any Way You Cut It: Meat Processing and

Smalltown America. University Press of Kansas, Lawrence,

pp. 85–107.

Greive, S., Tonts, M., 1996. Regulation, land development

and the contested countryside: reflections of Bridge-

town, western Australia. New Zealand Geographer 52,

18–23.

Halpern, D., 1995. Mental Health and the Built Environment:

More than Bricks and Mortar? Taylor & Francis, London.

Hickie, I.B., Davenport, T.A., Hadzi-Pavlovic, D., Koschera,

A., Naismith, S.L., Scott, E.M., Wilhelm, K.A., 2001.

Development of a simple screening tool for common mental

disorders in general practice. Medical Journal of Australia

175, S17.

Hugo, G., 1989. Australia: the spatial concentration of the

turnaround. In: Champion, A. (Ed.), Counterurbanization:

the Changing Pace and Nature of Population Deconcentra-

tion. Edward Arnold, London, pp. 62–82.

Hugo, G., 1994. The turnaround in Australia: some first

observations from the 1994 census. Australian Geographer

25, 1–17.

Hugo, G., 2001. What is really happening in regional and rural

populations. In: Rogers, M.F., Collins, Y.MJ. (Eds.), The

Future of Australia’s Country Towns. La Trobe University,

Bendigo, pp. 57–71.

Hunter, L., Krannich, R.S., Smith, M.D., 2002. Rural

migration, rapid growth, and fear of crime. Rural Sociology

67, 71–89.

Illeris, S., 1984. Danish regional development during economic

crisis. Geografiske Tidsskrift 84, 53–62.

Jones, R., Tonts, M., 1995. Rural restructuring and social

sustainability: some reflections on the western Australian

Wheatbelt. Australian Geographer 26, 133–140.

Jones, R., Tonts, M., 2003. Transition and diversity in

rural housing provision. Australian Geographer 34,

47–59.

Judd, F.K., Jackson, H.J., Komiti, A., Murray, G., Hodgins,

G., Fraser, C., 2002. High prevalence disorders in urban and

rural communities. Australian and New Zealand of

Psychiatry 36, 104–113.


Kaplan, G.A., 1996. People and places: contrasting perspectives

on the association between social class and health. Interna-

tional Journal of Health Services 26, 507–519.

Kawachi, I., Berkman, L., 2003. Neighborhoods and Health.

Oxford University Press, Oxford pp. 20–40.

Kessler, R., Mroczec, D., 1992. An Update of the Development

of Mental Health Screening Scales for the US National

Health Interview Survey. Ann Arbor Science, Ann

Arbor, MI.

Kessler, R., Mroczec, D., 1994. Final Versions of Our Non-

Specific Psychological Distress Scale. Ann Arbor Science,

Ann Arbor, MI.

Kessler, R.C., McGonagle, K.A., Zhao, S., Nelson, C.B.,

Hughes, M., Eshleman, S., Witchen, H., Kendler, K.S.,

1994. Lifetime and 12 month prevalence of DSM-III R

psychiatric disorders in the United States. Results from the

national comorbidity survey. Archives of General Psychia-

try 51, 8–19.

Kessler, R.C., Andrews, G., Colpe, L.J., Hiripi, E., Mroczek,

D.K., Normand, S.-L.T., Walters, E.E., Zaslavsky, A.M.,

2002. Short screening scales to monitor population pre-

valences and trends in non-specific psychological distress.

Psychological Medicine 32, 959–976.

Kobetz, E., Daniel, M., Earp, J.A., 2003. Neighbourhood

poverty and self-reported health among low-income, rural

women, 50 years and older. Health and Place 9, 263–271.

Kontuly, T., Vogelsang, R., 1988. Explanations for the

intensification of counterurbanisation in the Federal Re-

public of Germany. Professional Geographer 40, 42–54.

Lawrence, G., 1996. The future of agriculture and rural society

in central Queensland. In: Cryle, D., Griffin, G., Stehlik, D.

(Eds.), Future for Central Queensland, Rockhampton:

Rural and Social Economic Research Centre. University

of Queensland, Rockhampton, pp. 53–58.

Lawrence, G., 1999. Agri-food restructuring: a synthesis of

recent Australian research. Rural Sociology 64, 186–202.

Lawrence, G., Williams, C., 1990. The dynamics of decline:

implications for social welfare delivery in rural Australia.

In: Cullen, T., Dunn, P., Lawrence, G. (Eds.), Wagga

Wagga: Centre for Rural Welfare Research. Charles Sturt

University, Wagga Wagga, pp. 38–59.

Leighton, D.C., Harding, J.S., Macklin, D.B., MacMillan,

A.M., Leighton, A.H., 1963. The Character of Danger:

Psychiatric Symptoms in Selected Communities. Basic

Books, New York.

Lynch, J., 2000. Income inequality and health: expanding the

debate. Social Science & Medicine 51, 1001–1005.

Lynch, J., Due, P., Muntaner, C., Smith, G.D., 2000. Social

capital—is it a good investment strategy for public health?’.

Journal of Epidemiology and Community Health 54, 404–

408 (comment).

Lynch, J., Smith, G.D., Hillemeier, M., Shaw, M., Raghu-

nathan, T., Kaplan, G., 2001. Income inequality, the

psychosocial environment, and health: comparisons of

wealthy nations. Lancet 358, 194–200.

Macintyre, S., Maciver, S., Sooman, A., 1993. Area, class and

health: should we be studying people or places? Journal of

Social Policy 22, 213–234.

Macintyre, S., Ellaway, A., Cummins, S., 2002. Place effects on

health: how can we conceptualise, operationalise and

measure them. Social Science & Medicine 55, 125–139.

McKenzie, F., 1994a. Population decline in non-metropolitan

Australia: impacts and policy implications. Urban Policy

and Research 12, 253–263.

McKenzie, F., 1994b. Regional Population Decline in Aus-

tralia: Impacts and Policy Implications. Australian Govern-

ment Publishing Service, Canberra.

McMichael, P., Lawrence, G., 2001. Globalising agriculture:

structures of constraint for rural farmers. In: Lockie, S.,

Bourke, L. (Eds.), Rurality Bites: the Social and Environ-

mental Transformation of Rural Australia. Pluto, Annan-

dale, pp. 153–165.

Murdoch, J., Day, G., 1998. Middle class mobility, rural

communities and the politics of exclusion. In: Boyle, P.,

Halfacree, K. (Eds.), Migration into Rural Areas. Wiley,

Chichester, pp. 186–199.

Murray, G., Judd, F., Jackson, H., Fraser, C., Komiti, A.,

Hodgins, G., Pattison, P., Humpheys, J., Robins, G., in

press. A community survey of mental health and well-being

in rural Australia: the role of remoteness. Australian and

New Zealand Journal of Psychiatry.

New South Wales Department of Mineral Resources, 2002.

Broken Hill–Silver City. New South Wales Department of

Mineral Resources. (http://www.minerals.nsw.gov.au/mine/

silver.htm accessed 24/06/2003).

Oakley-Browne, M.A., Joyce, P.R., Wells, J.E., Bushnell, J.A.,

Hornblow, A.R., 1989. Christchurch psychiatric epidemiol-

ogy study, Part 2: six month and other period prevalences of

specific psychiatric disorders. Australian and New Zealand

Journal of Psychiatry 23, 327–340.

Paquette, S., Domon, G., 2003. Changing ruralities, changing

landscapes: exploring social recomposition using a multi-

scale approach. Journal of Rural Studies 19, 425–444.

Pavot, W., Diener, E., 1993. Review of the satisfaction with life

scale. Psychological Assessment 5, 164–172.

Paykel, E.S., Abbott, R., Jenkins, R., Brugha, T.S., Meltzer,

H., 2000. Urban–rural mental health differences in Great

Britain: findings from the national morbidity survey.

Psychological Medicine 30, 269–280.

Productivity Commission, 1999. Impact of Competition Policy

Reforms on Rural and Regional Australia. Australian

Government Publishing, Canberra.

Raiston, D., Beal, D., 1997. Bank branch closures in rural

areas. Australian Banker 3, 126–129.

Reijneveld, S.A., 1998. The impact of individual and area

characteristics on urban socioeconomic differences in health

and smoking. International Journal of Epidemiology 27, 33–40.

Robert, S.A., 1998. Community-level socioeconomic status

effects on adult health. Journal of Health and Social

Behavior 39, 18–37.

Sampson, R.J., Morenoff, J.D., Gannon-Rowley, T., 2002.

Assessing ‘‘neighborhood effects’’ social processes and new

directions in research. Annual Review of Sociology 28, 443–478.

Smailes, P., 1997. Socioeconomic change and rural morale in

South Australian 1982–1993. Journal of Rural Studies 13,

19–42.

Smailes, P., 2000. The diverging geographies of social and

business interaction patterns: a case study of rural south

Australia. Australian Geographical Studies 38, 158–181.

Stockdale, A., Findlay, A., Short, D., 2000. The repopulation

of rural Scotland: opportunity and threat. Journal of Rural

Studies 16, 243–257.

http://www.minerals.nsw.gov.au/mine/silver.htmaccessed24/06/2003

http://www.minerals.nsw.gov.au/mine/silver.htmaccessed24/06/2003


Stead, M., MacAskill, S., Mackintosh, A., Reece, J., Eadie, D.,

2001. It’s as if your’re locked in: qualitative explanations for

area effects on smoking in disadvantaged communities.

Health and Place 7, 333–343.

Waitzman, N.J., Smith, K.R., 1998. Phantom of the

area: poverty-area residence and mortality in the

United States. American Journal of Public Health 88,

973–976.

Walmsley, D.J., Sorensen, A.D., 1990. Contemporary Austra-

lia: Explorations in Economy, Society and Geography.

Longman Cheshire, Melbourne.

Ware, J.E.J., Kosinski, M., Keller, S., 1996. A 12-item

short-form health survey: construction of scales and

preliminary tests of reliability and validity. Medical Care

34, 220–233.

Warin, M., Baum, F., Kalucy, E., Murray, C., Veale, B., 2000.

The power of place: space and time in women’s and

community health centres in south Australia. Social Science

& Medicine 50, 1863–1875.

Watson, D., Clark, L.A., Tellegen, A., 1988. Development and

validation of brief measures of positive affect and negative

affect: the Panas scales. Journal of Personality and Social

Psychology 54, 1063–1070.

Wilkinson, R.G., 1997. Socioeconomic determinants of health:

health inequalities: relative or absolute material standards?

British Medical Journal 314, 591–595.

Witten, K., McCreanor, T., Kearns, R., Ramasubramanian, L.,

2001. The impacts of a school closure on neighbourhood

social cohesion: narratives from Invercargill, New Zealand.

Health and Place 7, 307–317.

Changing places: the impact of rural restructuring on mental health in Australia

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