STUDY DESIGN ARTICLE

Rural–urban differences in health and health behaviour: A baselinedescription of a community health-promotion programme for theelderly

MIKAEL FOGELHOLM1,2, RAISA VALVE3, PILVIKKI ABSETZ4, HEIKKI HEINONEN4,

ANTTI UUTELA4, KRISTIINA PATJA4, ANTTI KARISTO5, RIIKKA KONTTINEN3,

TIINA MAKELA3, AULIKKI NISSINEN4, PIIA JALLINOJA4, OLLI NUMMELA4 &

MARTTI TALJA6

1The UKK Institute for Health Promotion Research, Tampere, Finland, 2Research Unit, Pirkanmaa Hospital District,

Tampere, Finland, 3Palmenia Centre for Continuing Education, University of Helsinki, Lahti, Finland, 4National Public

Health Institute, Helsinki, Finland, 5Department of Social Policy, University of Helsinki, Finland, and 6Paijat-Hame

Hospital District, Lahti, Finland

AbstractStudy objective: To (1) describe the setting and design of the Good Ageing in Lahti Region (GOAL) programme; (2) byusing the baseline results of the GOAL cohort study, to examine whether living in urban, semi-urban, or rural communitiesis related to risk factors for chronic diseases and functional disability in ageing individuals. Design: The baseline data of acohort study of ageing individuals living in three community types (urban, semi-urban, rural). Data were collected by twoquestionnaires and laboratory assessments. Setting: Fourteen municipalities in the Lahti region (Paijat-Hame County) inFinland. Participants: A regionally and locally stratified random sample of men and women born in 1946–50, 1936–40, and1926–30. A total of 4,272 were invited and 2,815 (66%) participated. Main results: Elevated serum cholesterol, obesity,disability, sedentary lifestyle (v2 times/week walking), and high fat intake were more prevalent in rural vs. urban and semi-urban communities. After adjustment for sex, age, education, obesity, diet, physical activity, smoking, and alcohol use, ruralcommunities remained the only community type with increased (pv0.05) probability for high BMI (OR 1.33) and highwaist circumference (OR 1.43). Conclusions: The unfavourable health and lifestyle profile, together with an old population,makes health promotion for elderly citizens a special challenge for rural communities such as those in Paijat-Hame County,Finland. Most, if not all, of the differences in health between the three community types were explained by educationalbackground, physical activity, and smoking.

Key Words: Chronic diseases, disability, elderly, rural, urban, urbanization

Introduction

The demographic shift with a growing proportion of

elderly individuals (w65 years) is expected to

continue for the next 30–50 years [1]. Ageing per

se is associated with an increased prevalence of non-

communicable diseases (e.g. cardiovascular diseases,

type 2 diabetes, hypertension, etc.) and cognitive

and functional disability [2,3], and dependency on

external help [4,5]. The age-related morbidity and

disability may be shifted to a later age and their

impact on quality of life can be reduced [6].

Modification of risk factors (e.g. physical activity

and diet), increased social support, environmental

changes, and more effective medical care have been

potential explanations for improved functioning

during the last two decades [6–8]. Studies from the

US suggest that the positive trend in disability may

even offset the effects of population ageing [9,10].

Among OECD countries, the process of ageing is

the most rapid in Finland [1]. This is due to the

Correspondence: Mikael Fogelholm, The UKK Institute for Health Promotion Research, POB 30, 33501 Tampere, Finland. Tel: +358 3 282 9201. Fax: +358

3 282 9559. E-mail: Mikael.fogelholm@uta.fi

(Accepted 6 February 2006)

Scandinavian Journal of Public Health, 2006; 34: 632–640

ISSN 1403-4948 print/ISSN 1651-1905 online/06/060632-9 # 2006 Taylor & Francis

DOI: 10.1080/14034940600616039

exceptionally large baby boom generation, born after

the Second World War. It has been estimated that

the proportion of elderly (w65 years) in Finland will

increase from 15% in 2000 to about 27% in 2030

[1]. It is doubtful whether the expected decrease in

old-age disability [6] can any further offset an ageing

of this magnitude. Ageing is a major long-term

policy challenge for local communities in Finland,

since they provide and operate most of the health-

care and other welfare services.

The Good Ageing in Lahti Region (GOAL)

programme (2002–12) was initiated in order to

address some of the challenges posed by the ageing

population and increasing demand for health ser-

vices. In order to implement effective health-

promotion policies in different kinds of local

communities, data on health and determinants of

health are required. Therefore, a 10-year cohort

study was initiated as a needs assessment and

evaluation instrument. The main purpose of the

cohort study is to examine chronic diseases and

disability, health behaviour, and other determinants

of health, among ageing populations living in urban,

semi-urban, and rural communities. The data are

used to initiate and direct interventions in different

parts of the GOAL area.

The present paper has two objectives: (1) to

describe the GOAL setting and programme; (2) by

using the baseline results of the cohort study, to

examine how living in different community types is

related to risk factors for chronic diseases and

functional disability. Although the cohort study

was designed to give a comprehensive picture on

health, well-being, and ageing, the present paper

describes only two groups of end points, namely risk

factors for chronic metabolic diseases and functional

disability. These are motivated by a concern about

an increase in chronic diseases in Finland (e.g. type

2 diabetes) [11] and the consequences of disability

for both the individual old person and society in

general [4,5]. Moreover, because of increased

concern regarding inequality of health in different

types of local communities [12,13], the present

paper examines the role of community type as a

determinant of health and health behaviour.

Material and methods

Description of the research environment and network

The Paijat-Hame Hospital District (referred to as

the Lahti region) consists of 14 local communities

(municipalities), a total population of approximately

200,000 and community population sizes from

1,547 to 98,285 (September 2004, Statistics

Finland) (Figure 1). Compared with the national

average, the unemployment rate of the region is

higher (12.6% in November 2004) and educational

level lower. Moreover, the age-adjusted mortality is

higher and the population is ageing more rapidly,

compared with the Finnish average [14,15].

Specialized medical care is provided mainly at the

Paijat-Hame Central Hospital. Primary healthcare is

organized by clusters of local communities or

independently by a single community. Social care

is organized by the individual local communities.

The GOAL collaborating partners include the

Paijat-Hame Hospital District, all 14 local commu-

nities in the region, and a number of research

partners. The latter include the Palmenia Centre for

Continuing Education (programme coordinator)

and the Department of Social Policy, both from

the University of Helsinki, the National Public

Health Institute, the UKK Institute for Health

Promotion Research, and the Faculty of Social and

Health Care at the Lahti Polytechnic. The research

partners contribute by research, design, and devel-

opment of interventions, evaluation, training, and

consultation. The implementation of the interven-

tions is undertaken within the local communities.

Description of the programme

GOAL is a research and development programme

that aims to improve physical and psychosocial well-

being as well as to promote healthy and active living

among ageing citizens in the Lahti region. The aims

of the main interventions are: (a) to initiate

evidence-based preventive measures, e.g. risk-status

screening, personal and group counselling; (b) to

facilitate health promotion by fostering collaboration

both within community sectors and between local

communities; (c) to empower citizens to make

healthy lifestyle choices. Our target population

consists of non-institutionalized individuals, roughly

50 to 80 years of age.

GOAL consists of three parts (Figure 2): (1) a

longitudinal cohort study on health (physical,

mental, social) and determinants of health with a

regionally representative sample of approximately

3,000 individuals, with assessments at 0, 3, 6, and 10

years; (2) community diagnoses: selected local

results of the cohort study are combined with

statistical data on social conditions and policy

documents from each community [15,16]; (3)

community-based programmes including implemen-

tation interventions to develop local policies and

practices, educational campaigns, and other actions

based on the needs identified in the cohort study and

Ageing and health promotion 633

Figure 1. Map of Finland (left) and the Lahti region (right). The town of Lahti is shown in white, the semi-urban communities in light grey,

and the rural communities in dark grey.

Figure 2. Outline of the Good Ageing in Lahti Region (GOAL) programme.

634 M. Fogelholm et al.

community diagnoses. The first intervention was a

programme for type II diabetes prevention [17].

The cohort study: sampling, data collection, and analyses

Sampling. Three cohorts were chosen for the 10-

year follow up (2002–12). A stratified (sex, age

group, municipality) random sample of men and

women born in 1946–50, 1936–40, and 1926–30

was drawn from the population registry. Three age-

cohorts (52–56 years, 62–66 years, and 72–77 years

at baseline) were chosen to reflect the transition

from pre-retirement age to ‘‘early’’ old age. The

choice of a ‘‘young’’ ageing population was also

motivated by our intention to intervene early

before individuals actually are disabled and

institutionalized.

The target total sample size was 3,000. Assuming

a non-response rate of 30%, the sample size was set

to 4,200. The sample was taken from all 14

communities with the minimum sample size set to

102 (17 individuals for each age group and sex). The

final sample sizes and participation rates are given in

Table I.

The cohort study was approved by the Ethical

Committee of the Paijat-Hame Central Hospital,

2002. Written informed consent was obtained from

all cohort participants.

Data collection. Study participants were invited to a

local healthcare centre during March–April 2002.

With the invitation, they received the first

questionnaire to be filled in at home. The second

questionnaire was received and filled in at the

healthcare centre. The questionnaires included

queries on socioeconomic conditions, present and

past (childhood) living conditions, health, health

behaviour (e.g. eating habits and physical activity),

use of healthcare and social services, social networks,

leisure-time activities, lifestyles, and expectations for

the near future.

At the health check-up and after a 10–12 h

overnight fast, a plasma sample was drawn from

the antecubital vein, in vacuum tubes. The speci-

mens were stored in polythene tubes, at 270˚C,

until analysed. Weight was measured on a digital

scale, in light clothes and without shoes. Height was

measured without shoes. Waist circumference was

measured twice midway between the lowest rib and

iliac crest and the mean value was used.

Measures. Functional disability was assessed by the

question ‘‘How does your health restrict the

following daily functions?’’ and the answers were

coded as (1) severe restrictions, (2) some

restrictions, (3) no restrictions [18,19]. The

prevalence of those with severe restrictions in

climbing several flights of stairs, in walking 100 m,

500 m, and 2 km, and in bending or kneeling was

used as an indicator of functional disability. Most of

the markers for disability were related to mobility,

because a loss in physical functioning is likely to be

detected first in tasks requiring moving about [6].

We also used the compound Physical Functioning

Scale of RAND-36 [19].

The following health behaviours were queried:

frequency of walking and vigorous exercise; smok-

ing; changes in consumption of total fat, saturated

fat, and vegetables. Walking less than 2630 min per

week; vigorous exercise less than 2630 min per

week; daily smoking; unchanged fat intake (vs.

decreased); unchanged fat quality (vs. decreased

intake of saturated fat); and unchanged vegetable

consumption (vs. increased) were regarded as

unhealthy habits.

The following methods were used for blood

chemistry: (1) serum total cholesterol: enzymatic,

colorimetric method (Roche Cobas Integra), (2)

HDL cholesterol: homogenous enzymatic colori-

metric assay (Roche Cobas Integra), (3) plasma

glucose: enzymatic reference method with hexoki-

nase (Roche Cobas Integra); (4) plasma insulin:

Table I. Sample sizes, number of participants, and participation rates by community type, sex, and age.

Year of birth

Men Women

Total1946–50 1936–40 1926–30 1946–50 1936–40 1926–30

Sample, n Rural 144 144 144 144 144 144 864

Semi-urban 256 256 256 256 256 256 1536

Urban 312 312 312 312 312 312 1872

Participants, n Rural 81 106 90 109 108 104 599

Semi-urban 164 177 154 182 186 159 1022

Urban 168 218 222 203 231 187 1229

Participation, % Rural 56,4 73,8 62,6 75,7 75,0 72,4 69,3

Semi-urban 64,1 69,0 60,2 71,2 72,7 61,9 66,5

Urban 54,0 70,0 71,0 65,0 74,0 60,0 65,7

Ageing and health promotion 635

immunoluminometric assay (DPC Immulite). The

cut-off points used for increased risk were: plasma

insulin w20 mU/l, serum total cholesterol

w6.5 mmol/l, BMIw30, waist circumference

w100 cm (men) or w90 cm (women).

Statistical analyses. The local communities were

classified as rural, semi-urban, and urban [15].

The division was based on population density and

geographical location, in relation to the city of Lahti

(see Figure 1). The crude differences for prevalence

of risk factors for chronic diseases, severe functional

disability, and unhealthy lifestyle were tested by the

chi-squared test. Because of the sampling procedure,

these analyses were adjusted for age and sex.

Differences between community types were

identified by calculating the 95% confidence

interval for the difference of proportions [20].

We analysed whether community type was asso-

ciated with chronic diseases and physical function-

ing, independently of socioeconomic and/or lifestyle

factors. The occurrences of risk factors for chronic

diseases were used as dependent variables in multi-

ple logistic regressions. Predictors for functional

disability were identified in a similar way, but due to

the three-class outcome a nominal regression model

was used. However, only the results regarding severe

restrictions are presented.

The following factors were used as independent

variables in the regressions: male sex (reference:

female), age (72–76 years or 62–66 years, 52–56

years as reference), education (0–8 years or 9–12

years, higher education as reference), community

type (rural or semi-urban, urban as reference),

normal waist circumference (>90/100 cm as refer-

ence), normal BMI (>30 as reference), decreased fat

intake, improved fat quality, and increased vegetable

consumption (unchanged dietary patterns as refer-

ence), increased exercise (unchanged as reference),

vigorous exercise >2630 min/week (v2 times/week

as reference), daily smoking (less frequent or no

smoking as reference), and alcohol use (never, 1–4

times/month, or 2–3 times/week, >4 times/week as

reference).

P-values v0.05 were regarded as statistically

significant. The data were weighed (SPSS 11.5

weight procedure) to adjust for variation in prob-

abilities of selection, variation in response rates by

primary sampling units and deviation of the popula-

tion by age, sex, and municipality.

Results

Some basic characteristics of the communities are

given in Table II. The rural communities are

characterized by a high proportion of agrarian

population and elderly citizens, and a low proportion

of high-degree education. Compared with the

national average, the rural communities had a higher

proportion of elderly citizens in 2000 (Finland: 65–

74 years: 8.4%; 75+ years: 6.6%) and the population

will also age more rapidly within the next 15-year

period (Finland: 65–74 years: +3.8% units; 75+years: +2.8% units) [21]. The ageing of the popula-

tion is fastest in semi-urban communities.

Plasma cholesterol concentration was lowest in

urban participants (Table III). Both waist circum-

ference and BMI was highest among the rural

communities, without a significant difference

between urban and semi-urban participants. Self-

reported physical disabilities were most frequently

observed among rural participants. Also the Physical

Functioning Scale index was lower in rural (75.9, SE

1.3), compared with semi-urban (81.1, SE 0.7,

p50.001) and urban (79.3, SE 0.7, p50.05)

participants. The same pattern was observed also

for unhealthy lifestyles: all differences indicated

poorer lifestyle in rural communities.

Multivariate regression analyses examined

whether community type would still be associated

with the outcomes after adjustment for individual

risk factors. Rural communities showed increased

Table II. Description (mean, range) of the local communities participating in the study.

Rural Semi-urban Urban

No. of municipalities 8 5 1

Population (2001) 3,823 (1,570–7,415) 15,777 (8,680–20,958) 97 543

Population/km2 7 (5–19) 25 (11–40) 631

Agrarian population (%) 18.8 (11.3–28.6) 6.1 (2.6–11.2) 0.3

High-degree education (%) 13.7 (10.6–15.4) 18.9 (15.7–23.6) 21.6

65–74 years old, 2000 (%) 11.7 (8.5–15.0) 8.6 (6.7–10.5) 8.7

65–74 year increase (% units)a 4.5 (1.8–6.3) 6.0 (5.0–6.9) 4.7

>75 years old in 2000 (%) 9.0 (7.4–10.1) 6.3 (4.4–8.1) 7.0

65–74 year increase (% units)a 3.0 (0.8–5.7) 2.7 (1.4–4.2) 2.1

aPredicted increase between 2000 and 2015 [12,18].

636 M. Fogelholm et al.

probability of high waist circumference (p50.005),

and BMI (p50.06) (Table IV). Semi-urban com-

munity-type was associated with increased risk of

high total cholesterol concentration (p50.003). In

contrast, the community type was neither associated

with the probability of severe restrictions of physical

Table III. Proportion (%) of elderly individuals with risk factors for chronic diseases, self-reported physical disability or unhealthy lifestyle

in three different types of local communities.

Rural (n5405–440)

Semi–urban

(n5982–1,053) Urban (n51,187–1,256) Pearson’s chi-squared

Risk factors

S insulin w20 mU/l 8.0 5.9 5.4 0.13

S cholesterol w6.5 mmol/l 28.7 28.9 22.2 v0.001, ru, su

Waist w90/100 cm 57.3 46 46.8 v0.001, rs, ru

BMIw30 32.5 26.1 25.2 0.009, rs, ru

Disability, severe restrictions

Climbing stairs, several floors 17.9 13.9 15.1 0.16

Walking 100 m 3.7 1.9 2.8 0.14

Walking 500 m 9.3 4.2 5.2 v0.001, rs, ru

Walking 2 km 17.9 10.2 12.1 v0.001, rs, ru

Bending 18.6 12.1 12.1 0.001, rs, ru

Unhealthy lifestyle

Daily smoker 14.0 16.7 17.5 0.253

Walking v2 times weekly 28.9 22.6 19.7 v0.001, rs, ru

Exercise v2 times weekly 43.9 43.2 43.4 0.975

Unchanged total fat intake 26.2 20.5 21.7 0.06

Unchanged fat quality 26.3 19.4 20.2 0.01, rs, ru

Unchanged vegetable use 25.6 22.9 22.2 0.37

Results are adjusted for age and sex as a result of the sampling procedure. The range of participants reflects small variations in obtaining

individual data. ru5rural communities significantly (pv0.05) different from urban community (Lahti). rs5rural communities significantly

(v0.05) different from semi-urban communities.

Table IV. Predictors of risk factors for chronic diseases in a regionally representative sample of ageing (52–76 years) men and women.

High insulin High cholesterol High BMI High waist

Male sex 1.52 0.73 0.70 0.77

Age 72–76 years 1.97 0.67 1.24 1.86

Age 62–66 years 1.55 0.85 1.33 1.45

Age 52–56 years (reference) 1.00 1.00 1.00 1.00

Education (0–8 years) 0.96 0.99 1.02 1.12

Education (9–12 years) 0.76 1.04 0.84 0.97

Education (w12 years, reference) 1.00 1.00 1.00 1.00

Rural community 1.08 1.28 1.33 1.43

Semi-urban community 0.84 1.54 1.11 1.07

Urban community (reference) 1.00 1.00 1.00 1.00

Normal waist 0.27 0.87 NI NI

Normal BMI 0.46 1.20 NI NI

Decreased fat intake 1.04 0.72 0.55 0.66

Changed fat quality 0.82 0.92 1.49 1.17

Increased vegetables 0.94 1.06 1.21 1.11

Increased exercise 0.96 0.95 0.47 0.54

Exercise >2 times per week 0.71 1.01 0.84 0.79

Daily smoker 1.15 0.86 0.57 0.76

Alcohol, never 0.36 0.61 2.32 1.15

Alcohol, 1–4 times/month 0.29 0.66 1.83 1.32

Alcohol, 2–3 times/week 0.40 0.73 1.78 1.26

Alcohol, >4 times/week (ref.) 1.00 1.00 1.00 1.00

The numbers are exponents in a multiple logistic regression. Exponents in bold are significantly (pv0.05) different from 1.00. NI5not

included in the equation.

Ageing and health promotion 637

functioning (Table V), nor with the Physical

Functioning Scale (results not shown).

Discussion

Differences between the local community types

The GOAL cohort study answers the questions

‘‘what should be done and where?’’ and ‘‘what

happens during the programme?’’. Interventions are

then developed to promote sustainable changes on

the organizational and individual level. The observed

differences between the community types indicated

poorer health in rural communities, as suggested

previously [12,13]. However, these differences

seemed to be explained mainly by socioeconomic

background (education) and health behaviour. The

unexplained variance that could be captured by

the urban–rural gradient remained small after the

adjustments.

We observed slightly better health behaviours in

urban than in rural dwellers. The access to places for

physical exercise (e.g. swimming pool, gym) is better

in urban than in rural surroundings [22,23].

Although possibilities for walking and cycling are

good during summer in all community types,

moving around in rural communities may get

difficult during the wintertime in Finland. For

example, a fear of falling may restrict outdoor

activities. Another potential explanation is that the

social support for physical exercise may be lower in

rural settings [24]. Our data indicated a between-

community difference in walking, but not in

vigorous exercise. This would support the view that

the proximity of services is associated with walking

for a reason, rather than with ‘‘pure’’ exercise for

fitness or health [23]. Our cross-sectional design

does not identify causality. Urban dwellers may be in

good health because they walk more, or they may

walk more because of their good health. Good

health, in turn, may be related to, for example, better

accessibility to health services and better treatment

of diseases in urban settings [25].

Differences in dietary habits between rural and

urban individuals could be affected by access to

different types of food shops [26]. Big shopping

malls in urban and semi-urban areas have a better

choice of products and lower prices, compared with

small rural food shops. Moreover, because of

deteriorating public transportation, many elderly

citizens in rural areas may have difficulties in getting

even to the closest food shops. Another reason for an

unhealthier diet among the rural participants may be

related to traditional eating habits of the agrarian

population.

Some studies have shown that the best health may

be observed in suburban areas, with poorer health

and health behaviour in urban and rural settings

[12,24]. However, the urban structure in Finland is

dissimilar to many metropolitan areas, where

Table V. Predictors of functional disability in a regionally representative sample of ageing (52–76 years) men and women.

Climbing stairs Walking 100 m Walking 500 m Walking 2 km Bending

Male sex 0.61 0.82 1.04 0.97 0.97

Age 72–76 years 13.38 5.61 7.64 9.31 4.28

Age 62–66 years 4.52 1.92 2.04 2.67 1.61

Age 52–56 years (reference) 1.00 1.00 1.00 1.00 1.00

Education (0–8 years) 1.61 1.90 1.64 1.91 1.75

Education (9–12 years) 1.03 0.34 0.54 0.64 0.96

Education (w12 years, reference) 1.00 1.00 1.00 1.00 1.00

Rural community 1.03 1.06 1.66 1.33 1.10

Semi-urban community 1.09 0.88 0.99 1.03 0.99

Urban community (reference) 1.00 1.00 1.00 1.00 1.00

Normal waist 0.50 1.15 0.74 0.57 0.56

Normal BMI 0.45 0.47 0.49 0.53 0.45

Decreased fat intake 0.91 0.76 0.88 1.16 0.89

Changed fat quality 0.93 1.41 1.30 0.98 1.22

Increased vegetables 1.37 1.40 1.09 1.33 0.95

Increased exercise 0.38 0.11 0.20 0.27 0.44

Exercise >2 x/wk 0.44 0.27 0.37 0.35 0.55

Daily smoker 1.86 3.73 2.04 2.19 1.22

Alcohol, never 1.02 3.28 1.65 2.36 1.94

Alcohol, 1–4 times/month 0.95 2.45 1.14 1.41 2.11

Alcohol, 2–3 times/week 0.79 0.40 0.51 1.04 1.60

Alcohol, >4 times/week (ref.) 1.00 1.00 1.00 1.00 1.00

The numbers are exponents in a multiple nominal regression. Exponents in bold are significantly (pv0.05) different from 1.00.

638 M. Fogelholm et al.

crowded inner cities may be surrounded by wealthy

suburbs. Since the urban clutter in Lahti is low by

international standards, the finding of apparently the

best health status and health behaviour in Lahti is

not in conflict with the above results [12].

Individual determinants of risk factors and disability

Obesity remains a significant risk factor for cardio-

vascular morbidity and functional disability even

among elderly individuals [27,28]. Because of

potential loss of muscle mass, body mass index

(BMI) may not always be the best indicator of body

fatness [29]. The prevalence of obesity in our sample

was higher when judged from waist circumference

than from BMI. However, it should be kept in mind

that the general cut-off points for both BMI and

waist circumference may not apply equally well to

elderly individuals [30].

The determinants of risk factors for chronic

diseases and functional disability (e.g. older age,

low education, obesity, physical inactivity, and

smoking) were consistent with earlier data [28,31].

Smoking was negatively associated with functions

that are related to increased oxygen consumption

(cardio-respiratory fitness). It is noteworthy that

both obesity and a healthy lifestyle predicted health,

independently of each other. This has important

implications, since it shows the benefits of a healthy

lifestyle, even if body weight cannot be changed.

Conclusions and potentials

The theoretical framework of GOAL stems from the

social ecological model, integrating multiple levels

and multiple theories [32,33]. Based on a review of

major community interventions, Sorensen et al.

suggested ‘‘new directions for the second generation

of community health promotion programmes’’ [32].

Of the five key directions recommended, the GOAL

programme explicitly addresses four of these: (a)

targeting multiple levels of influence; (b) involving

communities in programme planning and imple-

mentation; (c) incorporating approaches for ‘‘tailor-

ing’’; and (d) utilizing rigorous process tracking and

evaluation. The fifth direction, addressing social

inequalities in disease risk, is also tackled: as the

GOAL cooperates closely with the social and

healthcare system of local communities, it will most

probably reach a major part of the population, also

including those with a lower socioeconomic status.

Compared with the more urban communities

in the Lahti region, the health status and health

behaviours among elderly individuals in rural

communities were poorer. Moreover, since the

proportion of elderly individuals at present is also

already greater, the rural communities face an

evident and immediate challenge. Semi-urban com-

munities, with a rapidly increasing proportion of

elderly individuals, but with presently more favour-

able health and health behaviour profile, have more

time to prepare for the demographic shift. The

distinct between-community differences in health

and health behaviour within a rather small area, such

as the Lahti region, were noteworthy. This has also

practical implications, since all interventions of

GOAL are to be planned, focused, and tailored

according to the local community needs and context.

In terms of research potential, the GOAL pro-

gramme provides excellent opportunities for both

epidemiological analysis of ageing and for evidence-

based interventions tackling questions related espe-

cially to implementation and sustainability.

Comparison of trends in health and health behaviour

in the different community types will be a central

research question. Too high a dropout and potential

intervention effect of the follow-up (individuals in

the cohort change their habits more than the general

population) are the main limitations of the cohort

study.

Acknowledgements

The study was financially supported by the Paijat-

Hame Hospital District, and the European Social

Fund. The authors are grateful to all students and

teachers at the Lahti Polytechnic who assisted with

the data collection. They would also like thank Prof.

Brian Oldenburg, who read the draft and made

incisive and valuable comments.

References

[1] Antolin P, Oxley H, Suyker W. How will ageing affect

Finland? OECD Economics Department Working Papers

295, OECD Economics Department, ECO/WKP(2001)21;

2001:1–43, Available at: http://www.oecd.org/dataoecd/33/

35/1863374.pdf (accessed 5 August 2005).

[2] Allaire SH, LaValley MP, Evans SR, et al. Evidence for

decline in disability and improved health among persons

aged 55 to 70 years: The Framingham Heart Study.

Am J Public Health 1999;89:1678–83.

[3] Ostchega Y, Harris TB, Hirsch R, et al. The prevalence of

functional limitations and disability in older persons in the

US: Data from the National Health and Nutrition

Examination Survey III. J Am Geriatr Soc 2000;48:1132–5.

[4] Laukkanen P, Leskinen E, Kauppinen M, Sakari-Rantala R,

Heikkinen E. Health and functional capacity as predictors of

community dwelling among elderly people. J Clin Epidemiol

2000;53:257–65.

[5] Van Schoor NM, Smit JH, Twisk JWR, Lips P. Impact of

vertebral deformities, osteoarthritis, and other chronic

Ageing and health promotion 639

diseases on quality of life: A population-based study.

Osteoporosis Int 2005;16:749–56.

[6] Freedman VA, Martin LG, Schoeni RF. Recent trends in

disability and functioning among older adults in the United

States: A systematic review. JAMA 2002;288:3137–46.

[7] Burden of Disease Network Project. Disability in old age

(BURDIS). Finnish Centre for Interdisciplinary

Gerontology University of Jyvaskyla Finland, 2004:1–47.

[8] Nelson ME, Layne JE, Bernstein MJ, et al. The effects of

multidimensional home-based exercise on functional perfor-

mance in elderly people. J Gerontol A Biol Sci Med Sci

2004;59:154–60.

[9] Singer BH, Manton KG. The effects of health changes on

projections on health services needs for the elderly popula-

tion in the United States. Proc Natl Acad Sci USA 1998;95:

15618–22.

[10] Waidmann T, Liu K. Disability trends among the elderly

and implications for the future. J Gerontol B Psychol Sci Soc

Sci 2000;55:S298–307.

[11] Aromaa A, Koskinen S, editors, Health and functional

capacity in Finland. Baseline results of the Health 2000

Health Examination Survey. Publications of the National

Public Health Institute B12/2004. Helsinki: National Public

Health Institute; 2004. p. 1–148.

[12] Takano T, Nakamura K. An analysis of health levels and

various indicators of urban environments for Healthy Cities

projects. J Epidemiol Community Health 2001;55:263–70.

[13] Robert SA, Strombom I, Trentham-Dietz A, et al.

Socioeconomic risk factors for breast cancer:

Distinguishing individual- and community-level effects.

Epidemiology 2004;15:442–50.

[14] Blom-Lange M. [Mortality, morbidity and socio-economic

factors in municipalities of Paijat-Hame hospital district, in

the Paijat-Hame hospital district and in large cities (in

Finnish)]. Publications of the Paijat-Hame hospital district

A4/1999. Lahti: Paijat-Hame hospital district; 1999. p.

1–74.

[15] Karisto A, Nummela O, Konttinen R, et al. [The ageing

Paijat-Hame. Report of community well-being (in Finnish)].

University of Helsinki, Palmenia Centre for Research and

Continuing Education, reports and surveys 41/2003.

Helsinki: University of Helsinki, Palmenia Centre for

Research and Continuing Education; 2003. p. 1–149.

[16] Webster P. Review of the ‘‘City Health Profiles’’ produced

by WHO-Healthy Cities – do they present information on

health and its determinants and what are their perceived

benefits? J Epidemiol Community Health 1999;53:125–7.

[17] Uutela A, Absetz P, Nissinen A, et al. Health psychological

theory in promoting population health in Paijat-Hame,

Finland: First steps toward a type 2 diabetes prevention

study. J Health Psychol 2004;9:73–84.

[18] Aalto A-M, Aro AR, Teperi J. RAND-36 terveyteen liittyvan

elamanlaadun mittarina. Mittarin luotettavus ja suomalaiset

vaestoarvot [RAND-36 as an indicator of health-related

quality of life. The validity of the method and Finnish

reference values (in Finnish)]. STAKES tutkimuksia/

Research by the National Research Centre for Welfare and

Health, nr. 101. Helsinki, 1999. p. 1–78.

[19] Hays RD, Sherbourne CD, Mazel RM. The RAND 36-Item

health Survey 1.0. Health Econ 1993;2:213–15.

[20] Gardner MJ, Altman DG. Calculating confidence intervals

for proportions and their differences. In: Gardner MJ,

Altman DG, editors. Statistics with confidence – confidence

intervals and statistical guidelines. London: British Medical

Journal; 1989. p. 28–33.

[21] Statistics Finland. Vaestoennuste kunnittain 2001–2030

[Population projection by municipalities 2001–2030 (in

Finnish with English summary and titles)]. Helsinki:

Statistics Finland; 2001. p. 31, 45.

[22] Troped PJ, Saunders RP, Pate RR, et al. Correlates of

recreational and transportation physical activity among

adults in a New England community. Prev Med 2003;37:

304–10.

[23] Patterson PK, Chapman NJ. Urban form and older

residents’ service use, walking, driving, quality of life, and

neighborhood satisfaction. Am J Health Promotion

2004;19:45–52.

[24] Parks SE, Housemann RA, Brownson RC. Differential

correlates of physical activity in urban and rural adults of

various socioeconomic backgrounds in the United States.

J Epidemiol Community Health 2003;57:29–35.

[25] Shipton D, Glazier RH, Guan J, et al. Effects of use of

specialty services on disease-modifying antirheumatic drug

use in the treatment of rheumatoid arthritis in an insured

elderly population. Med Care 2004;42:907–13.

[26] Turrell G, Blakely T, Patterson C, et al. A multilevel analysis

of socioeconomic (small area) differences in household food

purchasing behaviour. J Epidemiol Community Health

2004;58:208–15.

[27] Visser M, Langlois J, Guralnik JM, et al. High body fatness,

but not low fat-free mass, predicts disability in older men

and women. The Cardiovascular Health Study. Am J Clin

Nutr 1998;68:584–90.

[28] Wannamethee SG, Shaper AG, Whincup PH, et al.

Overweight and obesity and the burden of disease and

disability in elderly men. Int J Obes 2004;28:1374–82.

[29] Turcato E, Bosello O, Di Francesco V, et al. Waist

circumference and abdominal sagittal diameter as surrogates

of body fat distribution in the elderly: Their relation with

cardiovascular risk factors. Int J Obes 2000;24:1005–10.

[30] WHO. Obesity: Preventing and managing the global

epidemic. Report of a WHO consultation on obesity.

WHO Technical Report Series 2000;894:1–253.

[31] Fogelholm M, Kujala U, Kaprio J, Sarna S. Predictors of

weight change in middle-aged and old men. Obes Res

2000;8:367–73.

[32] Sorensen G, Emmons K, Hunt MK, Johnston D.

Implications of the results of community intervention trials.

Ann Rev Public Health 1998;19:379–416.

[33] McLeroy K, Bibeau D, Steckler D, Glanz K. An ecological

perspective on health promotion programs. Health Educ Q

1988;15:351–77.

640 M. Fogelholm et al.