Housing Type and Neighbourhood Safety Behaviour Predicts Self-rated Health, Psychological Well-being and Frequency of Recent Unhealthy Days: A Comparative Cross-sectional Study of the General Population in Sweden

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Planning Practice & Research

ISSN: 0269-7459 (Print) 1360-0583 (Online) Journal homepage: http://www.tandfonline.com/loi/cppr20

Housing Type and Neighbourhood Safety

Behaviour Predicts Self-rated Health, Psychological

Well-being and Frequency of Recent Unhealthy

Days: A Comparative Cross-sectional Study of the

General Population in Sweden

Erik Berglund, Ragnar Westerling & Per Lytsy

To cite this article: Erik Berglund, Ragnar Westerling & Per Lytsy (2017) Housing Type and Neighbourhood Safety Behaviour Predicts Self-rated Health, Psychological Well-being and Frequency of Recent Unhealthy Days: A Comparative Cross-sectional Study of the General Population in Sweden, Planning Practice & Research, 32:4, 444-465, DOI: 10.1080/02697459.2017.1374706

To link to this article: https://doi.org/10.1080/02697459.2017.1374706

Published online: 25 Sep 2017.

Submit your article to this journal Article views: 209

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https://doi.org/10.1080/02697459.2017.1374706

Housing Type and Neighbourhood Safety Behaviour Predicts

Self-rated Health, Psychological Well-being and Frequency of

Recent Unhealthy Days: A Comparative Cross-sectional Study

of the General Population in Sweden

Erik Berglund , Ragnar Westerling and Per Lytsy

Department of Public health and caring sciences, Uppsala University, Uppsala, sweden

ABSTRACT

This study aimed at analysing associations among housing type, neighbourhood safety behaviour, self-rated health (SRH), psychological well-being and unhealthy days in the general population. From 2004 to 2013, 90,845 Swedes completed a questionnaire about their health, number of days with poor health, psychological well-being, housing type, and whether they refrained from going out based on perception of neighbourhood safety. People not living in private housing and those who did not go out for safety reasons reported lower SRH and psychological well-being and higher frequency of recent unhealthy days and days without work capacity due to poor health.

1. Introduction

The majority of the world’s population lives in cities and urban environments (United-Nations, 2016), the predicted future urbanization has led to an increased focus on healthy planning. Cities and towns are complex systems and a planning process that takes the inhabitant’s health into account often needs to be based on an approach that recognizes multidirectional causality, feedback loops and unintended consequences (Rydin et al.,

2012). City/urban planning, in the broadest sense, may play a crucial role in creating

environments that improve or deteriorate people’s health and well-being, which includes areas such as land-use, dwellings, infrastructure, design and community building (Corburn,

2005; WHO, 2016). The home environment and local settlement are of great significance

to human beings. Housing can be considered on a four-level scale, on which each level

has the potential to impact the health of residents (WHO, 2016): the physical structure

of the house (or dwelling); the home (psychosocial, economic and cultural construction created by the household); the neighbourhood infrastructure (physical conditions of the

KEYWORDS

self-rated health; well-being; housing; perception of safety; healthy time; population health

this is an Open access article distributed under the terms of the creative commons attribution-noncommercial-noDerivatives license (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

CONTACT erik Berglund erik.berglund@pubcare.uu.se

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immediate housing environment); and the community (social environment and the pop-ulation and services).

People in the western world spend most of their time indoors (Leech et al., 2002). Indoor environments include homes and other residences, workplaces, transportation vehicles and other diverse-enclosed settings where individuals spend a portion of their day (Wu

et al., 2007). The exposure to built environments and housing are associated with health (Wu et al., 2007), mental illness (Fanning, 1967), and mortality (Phillimore et al., 1994; Sloggett & Joshi, 1994; Sundquist & Johansson, 1997). Built environments and housing may affect health in several ways; characteristics of importance include housing type, architec-ture, ergonomics, crowding, indoor chemistry, air quality, noise and lighting. The built environment may also indirectly have an impact on health by altering psychosocial processes with known health consequences (Evans, 2003). Build environment, high residential density, street traffic, and configuration, furthermore, are factors associated with social interaction and supportive relationships (Appleyard & Lintell, 1972; Evans, 2003).

Neighbourhoods are often defined as census tracts or by characteristics known both to affect and be affected by its residents (Sampson et al., 2002). Neighbourhood quality depends on characteristics of the neighbourhood unit. Physical neighbourhood quality factors, potentially relevant and attributed to health, include land use, density, street connectivity, transportation availability and infrastructure; access to nature and green space, public and open spaces, and resources (e.g. public services, health care, healthy food, schools, playgrounds, commercial functions, recreational opportunities); building and street condition, cleanliness/garbage, and maintenance; and traffic volume, air quality, and noise (Rollings et al., 2015). Social aspects of neighbourhoods are often determined by residents’ characteristics (Cutrona et al., 2006). Crime has been recognized as a key stressor in the neighbourhood environment and an important predictor for

neighbour-hood satisfaction (McCrea et al., 2005). Fear of crime also has been found to have a

significant negative influence on residential quality and may have a negative impact on residents’ mental health comparable to crime itself (Baumer, 1985; Green et al., 2002; Kullberg et al., 2009). Fear of crime is often distinguished from perceived risk and defined as ‘emotional response of dread or anxiety to crime or symbols that a person associates with crime’ (Ferraro, 1995). Fear of crime affects routine activities, thereby affecting the exposure to potentially victimizing situations by encouraging avoidant behaviour and influencing people to remain in their home as opposed to venturing out onto the streets (Cashmore, 2014).

Both housing and neighbourhoods consist of space used for living and homes; this space becomes a specific place of psychological, social, and emotive meaning for individuals and groups (Easthope, 2004). The concept of place ties the physical world with the social, cultural,

and emotive worlds (Easthope, 2004) and may be crucial to production and reproduction

of social identity, which affects other people and institutes behaviour against residents of certain neighbourhoods (Evans et al., 2003). Housing and local environment may constitute a basis for identity, security and togetherness, and some research findings indicate that these

functions become more important over time (Easthope, 2004).

Self-rated health (SRH) is one of the most widely used measurements of personal health. SRH, when measured via one question, is a robust predictor of several health outcomes, such as functional ability (Idler & Kasl, 1995; Idler et al., 2000), diseases (Kaplan et al., 1996;

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Emmelin et al., 2003) and mortality (Mossey & Shapiro, 1982; Idler & Benyamini, 1997; Siegel et al., 2003; DeSalvo et al., 2006; Singh-Manoux et al., 2007).

Poor psychological health is one of the chronic health problems with the highest prev-alence globally (Steel et al., 2014). Psychological health can be addressed in several ways including psychological well-being, which often is defined as a state of being balanced both emotionally and intellectually.

In 1948, the World Health Organization (WHO) defined health as a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity. One way to capture this concept of health is to measure a specific time period without health and limited activity due to poor health (Moriarty et al., 2003).

Physical and social features of housing and neighbourhoods are considered to be impor-tant, but sometimes elusive determinants of health and well-being (Rauh et al., 2008). From a healthy planning perspective environmental factors are important to investigate as indicators of poor public health, and for gaining knowledge about which factors that urban planning can manage that have the potential to improve public health. The potential associations among housing type and behaviour based on perceived neighbourhood safety and SRH, psychological well-being and time lost to poor health have not been fully explored. Losses in healthy time, such as frequency of unhealthy days due to housing and neighbourhood safety, in particular, require more research. This study aimed at analysing associations among housing type, behaviour based on perceived neighbourhood safety, SRH, psychological well-being and recent unhealthy days in the general Swedish population.

2. Methods

2.1. Study Population

Data from the Swedish national public health survey ‘Health on equal terms’ carried out from 2004 to 2013 were used for this study (Boström & Nyqvist, 2010). The national public health survey is a repeated, cross-sectional, postal questionnaire that has been distributed annually since 2004 by Statistics Sweden on behalf of the Public Health Agency of Sweden (previously the Swedish National Institute of Public Health). Each year, 20,000 people aged 16–84 are selected randomly from the Swedish national population registry (from 2005 to 2007, 10,000 people were selected), for a total of 170,000 persons. The questionnaires were returned by 90,845 individuals, making the response rate 53.4%. The study population consisted of slightly more women than men. Dropout analyses have been made on parts of the material, but not every year. The dropout analyses were carried out using telephone interviews with non-responders, and the main result showed that non-responders do not seem to have different response patterns than respondents.

2.2. Predictor Variables

The questionnaire contained about 85 questions, including type of housing, neighbourhood safety, demographics, financial status, social support and health.

Variation in housing type and living conditions can be categorized in several ways, such as house type, floor level and housing quality (Evans et al., 2003). In this study, type of housing consisted of four categories: private house, condominium, rented apartment, and

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lodger, dorm or other. Similar categorization of housing type has been used in previous research regarding predictors of health (Chung et al., 2015). The private house category included bungalows and townhouses, and the condominium category included apartments in housing cooperatives (bostadsrätt) and actual condominiums. Housing cooperatives are the traditional form of owner-occupied apartment housing in Sweden; members of the cooperative formally own the right to a specific apartment and inhabit it for an unlimited time, a right that can be bought and sold on the open real estate market. Membership in a housing cooperative is generally held to be the same thing as owning (as opposed to renting) an apartment.

The concept of behaviour generated from perceived neighbourhood safety and fear of crime was defined as the behaviour based on the perception of out-of-home environment safety. It was assessed by the question: ‘Do you ever refrain from going out alone for fear of being attacked, robbed or otherwise molested?’ Possible answers were ‘No’, ‘Yes, sometimes,’ and ‘Yes, regularly.’ This question was not included in the questionnaire in 2004. In the current study, the question was dichotomized into ‘Yes’ or ‘No’ answers. Similar questions have been used in studies regarding perception of neighbourhood (Ferraro & Grange, 1987; Kullberg et al., 2009). Personal perceptions of environment in some cases are known to better predict outcomes than several non-subjective, environment measures (Stiffman et al., 1999).

Demographic data were collected using questions that assessed the respondent’s gender, age and educational level (categorized as compulsory school, secondary school or equiv-alent, or university).

Financial status was used as a marker of socio-economic status (SES) and assessed by the question: ‘During the last 12 months, have you had difficulties managing your current expenses for food, rent, bills, etc.?’ Answers were pre-drafted as: ‘No,’ ‘Yes, at one time,’ and ‘Yes, several times.’ In the current study, the question was dichotomized into ‘Yes’ or ‘No’ answers.

Social support is seen as an important factor for health outcomes (Wheeler et al., 2008;

Rosell-Murphy et al., 2014). The questionnaire asked the following regarding perceived

emotional social support: ‘Do you have someone you can share your innermost feelings with and feel confident in?’ The following question was used to assess perceived instrumental social support: ‘Can you get help from someone/some people if you have practical problems or are ill?’ These two questions were dichotomized into ‘Yes’ or ‘No’ answers.

Information about long-term illness was collected by asking: ‘Do you have any long-term illness, problems following an accident, any disability or other long-term health problem?’ Replies were either ‘Yes’ or ‘No.’

2.3. Health Outcome Variables

SRH has also been identified as a valid predictor of various health outcomes, independent of other covariates (Benyamini & Idler, 1999), and a reliable predictor for future health (Strawbridge & Wallhagen, 1999). Several factors have been shown to be associated with

SRH, among them low income (Heritage et al., 2008), work-related factors (Cannuscio

et al., 2002), pension status (Westerlund et al., 2009), and psychological and social factors

(Berglund et al., 2014). General SRH was assessed with the question: ‘How do you rate

your general state of health?’ Answers on a five-point scale (very good, good, neither good nor poor, poor, very poor) were dichotomized into either good (very good or good) or less

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than good (neither good nor poor, poor, very poor). In population studies, SRH generally is accepted by researchers as a valid measure for determining health status, and it is common

to dichotomize SRH to demonstrate persons with non-good SRH (Johansson et al., 2015).

The total score of the General Health Questionnaire (GHQ12) was used to measure psy-chological well-being (Goldberg et al., 1997). Each symptom on the scale has four responses ranging from better than usual to much less than usual. For this study, the GHQ12 was scored using a four-point Likert format. The scores of all items were added up, resulting in a total ranging from 0 to 36, in which a higher score indicates lower psychological well-being.

No standard cut-off values exist for dividing the GHQ12 score threshold (Russ et al.,

2012). In this study, the total GHQ12 score was dichotomized with a cut-off score of 12;

into good psychological well-being and poor psychological well-being for the binary logis-tic regression analysis. The cut-off score was chosen due to a low mean score in the study population, which is often taken into account when selecting a cut-off for the GHQ12 measurement (Goldberg et al., 1998).

We used the brief standard Health-Related Quality of Life (HRQOL–4) from the Centres for Disease Control and Prevention (CDC) to measure unhealthy days and days with work incapacity (Moriarty et al., 2003). Recent days with poorer physical and mental health were assessed with the following questions: ‘Considering your physical health, how many days, during the last 30-day period, would you say it was not good?’ and ‘Considering your mental health, how many days, during the last 30-day period, would you say it was not good?’ Days with work incapacity and activity limitation were assessed with the following question: ‘On how many days, during the last 30-day period, did your physical or mental health disable you from work or daily activities?’ Models with days without work capacity as outcome included only people of working age (18–65).

2.4. Analysis

Bivariate associations were sought using chi-square tests, Mann–Whitney U tests, Spearman’s correlations and binary logistic regression models. Multiple binary logistic regression anal-yses were used to estimate associations among housing type, refraining from going out, demographic information (gender, age, education level), financial status, social support, long-term illness, and SRH or psychological well-being. Negative binomial regression mod-els were used to analyse the association among housing type, refraining from going out, demographics (gender, age, education level), financial status, social support, long-term illness and recent unhealthy days or days without work capacity. The negative binomial model is suitable for count variables (such as days) and produce an incident rate ratio (IRR) for the predictor (IRR > 1 indicates that the factor is associated with more days).

A stepwise approach was performed in the logistic and negative binomial regression models using sets of independent variables. Model 1 included housing type and refraining from going out. Model 2 included Model 1 and demographics. Model 3 included Model 2, financial status, and social support. Model 4 included Model 3 and long-term illness. All tests were two-sided, and a p value ≤0.05 was considered statistically significant. For all statistical analyses, we used the Package for the Social Sciences (SPSS)®_{version 22 (IBM} SPSS Statistics for Windows, Armonk, NY, USA).

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2.5. Ethical Considerations

The research ethics committee at the Swedish National Board of Health and Welfare approved of the Swedish National Public Health Survey (8 December 2003). The present study was approved by the regional ethical committee in Uppsala (11 November 2013). Both committees have conformed to the principles embodied in the Declaration of Helsinki.

3. Results

The study population was on average 50.4 years old and consisted of slightly more women (54.8%) than men (45.2%). Compulsory school was the most common level of highest com-pleted education (45.0%). Table 1 shows the distribution of demographics and key variables.

3.1. Housing Type and Behaviour Based on Perceived Neighbourhood Safety

Private house was the most common housing type (51.3%), followed by rented apartment (25.2%), condominium (17.4%), and lodger, dorm or other (6.1%). The average age varied between residents in different housing types; from 53.1 years in people living in private houses to 34.2 years in people living in lodger dorms or other (see Table 1).

A majority (76.9%) of the respondents reported not refraining from going out, but 23.1% did refrain from going out for safety reasons. The highest correlation with refraining from going out for safety reasons was with female gender (Spearman’s ρ = 0.30, p ≤ 0.01) in the correlation matrix (Table 2).

3.2. SRH, Psychological Well-being and Recent Unhealthy Days and Work Incapacity

Table 1 presents the relationship between housing type and SRH, psychological

well-be-ing and unhealthy days. A majority (70.2%) of respondents reported good or very good general SRH. Less than good SRH was reported by 35.8% of the people living in rented apartments, 29.5% of condominium residents, 28.2% of people living in lodger, dorm or other, and 27.1% of the people living in private houses. The difference in SRH between the groups was significant (p ≤ 0.01).

In distribution, 24.5% people living in lodger, dorm or other, 24.1% of apartment tenants, and 18.7% condominium residents reported poor psychological well-being. The lowest prevalence of poor psychological well-being was found in people living in private houses (14.4%).

On average, apartment tenants reported 7.8 days (SD = 10.0) with poor physical health in the past month, those living in lodger, dorm or other reported an average of 6.9 days (SD = 9.2) with poor physical health in the past month, and people living in condominium reported an average of 6.5 days (SD = 9.5) with poor physical health in the past month. People living in private homes reported the least loss of days with poor physical health in the past month with an average of 6.1 unhealthy days (SD = 9.4). The same pattern between groups of people living in different housing types was observed for the number of days in the past month with poorer mental health and days with work incapacity as well.

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Table 1.

Distribution of char

ac

teristics [%, median or mean ±

sD ] of study par ticipan ts b y housing t ype . n ot es: F igur es as per cen

tages if not sta

ted other wise . P earson chi-squar e t est w as used f or distributions and M ann– W hitney U test w as used f or median. ag ener al h ealth Q uestionnair e ( gh Q12) in inde x-f orm, r ang ing fr om 0 t o 36, wher e a higher sc or e indica tes lo w er psy cholog ical w ell-being . bgh Q12 w as dichot omiz ed , with a cut -off sc or e of 12, in to good psy cholog ical w

ell-being and poor psy

cholog ical w ell-being . **p ≤ 0.01. Pr iv at e house ( n = 45,752) Condominium (n = 15,515) Ren ted apar tmen t (n = 22,493) Lodger , dor m or other (n = 5,445) Total g ender M ale 47.0** 43.2** 41.9** 48.7** 45.2 Female 53.0** 56.8** 58.1** 51.3** 54.8 ag e M ean ( sD ) 53.1 (15.6)** 52.1 (18.4)** 47.6 (19.0)** 34.2 (19.5)** 50.4 (17.9) educa tion compulsor y school 47.0** 38.1** 45.6** 45.8** 45.0 sec ondar y school or equal 31.2** 34.2** 35.3** 43.6** 33.5 Univ ersit y 21.8** 27.7** 19.2** 10.6** 21.5 Financial sta tus n o pr oblems 90.9** 88.7** 74.3** 76.4** 85.5 h av e pr oblems 9.1** 11.3** 25.7** 23.6** 14.5

emotional social suppor

t n o 8.1** 11.4** 14.8** 16.1** 10.9 Ye s 91.9** 88.6** 85.2** 83.9** 89.1 instrumen

tal social suppor

t n o 3.1** 5.6** 8.4** 5.6** 5.0 Ye s 96.9** 94.4** 91.6** 94.4** 95.0 long-t erm illness n o 63.4** 62.0** 59.6** 66.3** 62.4 Ye s 36.6** 38.0** 40.4** 33.7** 37.6 refr ain fr om going out n o 81.0** 73.2** 70.9** 77.0** 76.9 Ye s 19.0** 26.8** 29.1** 23.0** 23.1 self-r at ed health Poor or v er y poor 4.7** 6.4** 9.1** 6.8** 6.2 n

either good nor poor

22.4** 23.1** 26.7** 21.4** 23.6 g ood or v er y good 72.9** 70.5** 64.2** 71.8** 70.2 Psy cholog ical w ell-being gh Q12 a MD , M ean ( sD ) 8, 9.1 (4.4)** 8, 9.6 (4.8)** 9, 10.4 (5.5)** 9, 10.4 (5.7)** 8, 9.6 (4.9) Poor psy cholog ical w ell-being b 14.4** 18.7** 24.1** 24.5** 18.2 g ood psy cholog ical w ell-being b 85.6** 81.3** 75.9** 75.5** 81.8 Fr equenc y of r ec en t da ys

with poor health or with work incapacit

y

Da

ys with poor ph

ysi

-cal health, mean (

sD ) 6.10 (9.4)** 6.54 (9.5)** 7.80 (10.0)** 6.87 (9.2)** 6.65 (9,6) Da

ys with poor men

tal health, mean ( sD ) 4.43 (7.8)** 5.36 (8.4)** 7.17 (9.4)** 7.12 (9.0)** 5.44 (8.5) Da ys with w ork inca -pacit y, mean ( sD ) 3.70 (8.1)** 4.24 (8.4)** 5.61 (9.5)** 4.77 (8.5)** 4.34 (8.6)

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Table 2. corr ela tion ma trix among v ariables . n ot es: M

aximum (max), minimum (min), standar

d devia tions ( sD ) and c orr ela tions indica ted . t he ma

trix has been calcula

ted with spearman ’s c orr ela tion c oefficien t. *c orr ela tion is sig nifican t a t the 0.05 lev el; ** corr ela tion is sig nifican t a t the 0.01 lev el . M in M ax SD 1 2 3 4 5 6 7 8 9 10 11 12 13 1. g ender 1 2 0.50 2. age 16 84 17.96 −0.03** 3. educa tion 1 3 0.78 −0.04** 0.20** 4. F inancial sta tus 0 1 0.35 0.05** −0.19** 0.03** 5.

t 0 1 0.31 −0.06** 0.00 0.05** 0.10** 6. instrumen tal social suppor t 0 1 0.22 −0.03** 0.05** 0.05** 0.13** 0.32** 7. long-t erm illness 0 1 0.49 0.01** 0.21** 0.09** 0.08** 0.05** 0.06** 8. h ousing t ype 1 4 0.99 0.03** −0.20** −0.00 0.19** 0.10** 0.09** 0.02** 9. refr ain fr om going out 0 1 0.42 0.30** 0.01** 0.06** 0.08** 0.03** 0.06** 0.07** 0.09** 10. self-r at ed health 1 5 0.85 0.03** 0.23** 0.18** 0.15** 0.14** 0.13** 0.46** 0.06** 0.13** 11. P sy cholog ical w ell-being 0 36 4.93 0.10** −0.01** 0.03** 0.20** 0.18** 0.14** 0.21** 0.11** 0.15** 0.47** 12. Da ys with poor ph ysical health 0 30 9.61 0.11** 0.04** 0.11** 0.14** 0.08** 0.08** 0.40** 0.09** 0.13** 0.53** 0.34** 13. Da

ys with poor men

tal health 0 30 8.52 0.15** −0.20** 0.01 0.23** 0.15** 0.12** 0.18** 0.16** 0.16** 0.38** 0.58** 0.43** 14. Da ys with w ork inca -pacit y 0 30 8.62 0.08** 0.01* 0.08** 0.17** 0.09** 0.10** 0.35** 0.12** 0.13** 0.47** 0.39** 0.62** 0.42**

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3.3. Logistic and Negative Binomial Regression Models

The significant differences between different housing types and refraining from going out in SRH in the univariate analyses remained significant also in the adjusted logistic regres-sion models (Table 3). People living in rented apartments had higher odds of having less than good SRH than people living in private houses in models adjusted for demographic, financial status, social support and long-term illness (OR 1.30, 95% CI 1.24–1.36). Living in lodger, dorm or other was associated with less than good SRH in adjusted models (OR 1.31, 95% CI 1.20–1.43). Refraining from going out was associated with reporting less than good SRH (OR 1.44, 95% CI 1.37–1.50).

Housing type and refraining from going out were significantly associated with lower psy-chological well-being (Table 4). Living in condominiums had a significant association with the risk of reporting poor psychological well-being in models adjusted for demographic, financial status, social support and long-term illness (OR 1.23, 95% CI 1.16–1.31) as did living in rented apartments (OR 1.27, 95% CI 1.21–1.34) and in lodger, dorm or other (OR 1.28, 95% CI 1.17–1.39). Refraining from going out was associated with the risk of reporting poor psychological well-being (OR 1.47, 95% CI 1.39–1.54).

Negative binomial regression models were calculated to predict unhealthy days and days with work incapacity based on housing type and refrain from going out. Living in a rented apartment, lodger, dorm or other was associated with the risk of more days with poorer physical health in the past month in the univariate analysis as well as in the adjusted

models (Table 5). Refraining from going out for safety reasons was associated with more

recent days with poor physical health (adjusted IRR = 1.17, 95% CI: 1.13–1.20, p ≤ 0.01). Living in a rented apartment was associated with more recent days with poor mental health in the adjusted models (adjusted IRR = 1.25, 95% CI: 1.21–1.30, p ≤ 0.01) as was living in a condominium, lodger, dorm or other. Refraining from going out for safety reasons was a predictor for having more recent days with poor mental health. See Table 6.

Living in a rented apartment was associated with more recent days with work incapacity in the adjusted models (adjusted IRR = 1.36, 95% CI: 1.29–1.43, p ≤ 0.01), as was living in a condominium, lodger, dorm or other. Refraining from going out was associated with more days with work incapacity in the adjusted models (adjusted IRR = 1.27, 95% CI: 1.22–1.33,

p ≤ 0.01). See Table 7.

4. Discussion

This study aimed to investigate whether, in the general population, housing type and behav-iours based on perception of neighbourhood safety were associated with SRH, psychological well-being, and time lost to poorer health or without work capacity. The main findings were that living in other housing types than private houses and refraining from going out was associated with reporting low SRH, poor well-being, and a higher frequency of unhealthy days or days with work incapacity due to health disability. Demographic, socio-economic, social support and long-term illness were associated with health, but although the effect of housing type and behaviour based on neighbourhood safety diminished when adjusting for these factors, they still remained statistically significant in the adjusted models.

In this study, as well as in previous studies, multi-dwelling units (compared to pri-vate houses) seem to be associated with worse health status, the results also indicates that

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Table 3.

results of log

istic r

eg

ression models of fac

tors pr

edic

ting less than good self-r

at ed health. n ot es: O dds r atio ( O r), sig nifican t lev el and c onfidenc e in ter val ( ci ) f or ha

ving less than good self-r

at ed health. srh w as dichot omiz ed t o lo w

er than good (=1) and good or v

er y good (=0). M odel 1 = h ousing t ype + r efr ain fr om going out , M odel 2 = M odel 1 + D emog raphic , M odel 3 = M odel 2 + socio -ec onomic + social suppor t, M odel 4 = M odel 3 + illness . **p ≤ 0.01. Crude M odel 1 M odel 2 M odel 3 M odel 4 OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI h ousing t

ype and beha

v-iour based on per

cep

-tion of neighbourhood safet

y h ousing t ype Priv at e house 1 1 1 1 1 condominium 1.12** (1.08–1.17) 1.07** (1.03–1.12) 1.13** (1.07–1.18) 1.07** (1.02–1.12) 1.05 (1.00–1.11) ren ted apar tmen t 1.50** (1.45–1.55) 1.43** (1.37–1.48) 1.66** (1.59–1.73) 1.34** (1.28–1.39) 1.30** (1.24–1.36) lodger , dorm or other 1.06 (0.99 t o1.13) 1.00 (0.93–107) 1.62** (1.50–1.75) 1.39** (1.28–1.51) 1.31** (1.20–1.43) refr ain fr om going out n o 1 1 1 1 1 Ye s 1.76** (1.70–1.83) 1.72** (1.65–1.78) 1.62** (1.55–1.68) 1.50** (1.44–1.57) 1.44** (1.37–1.50) D emog raphic g ender _Male 1 1 1 1 Female 1.17** (1.13–1.20) 1.07** (1.03–1.11) 1.11** (1.07–1.16) 1.13** (1.08–1.17) ag e 1.03** (1.03–1.03) 1.03** (1.03–1.03) 1.03** (1.03–1.03) 1.02** (1.02–1.02) educa tion lev el Univ ersit y 1 1 1 1 sec ondar y school or equal 1.36** (1.30–1.42) 1.42** (1.35–1.49) 1.36** (1.29–1.43) 1.36** (1.29–1.44) compulsor y school 2.47** (2.37–2.58) 2.04** (1.95–2.14) 1.93** (1.84–2.03) 1.99** (1.89–2.10) socio -ec onomic Financial sta tus n o pr oblems 1 1 1 h av e pr oblems 2.13** (2.05–2.22) 2.61** (2.49–2.74) 2.23** (2.12–2.36) social suppor t

t Ye s 1 1 1 n o 2.31** (2.22–2.42) 1.93** (1.83–2.04) 2.00** (1.89–2.13) instrumen tal social suppor t Ye s 1 1 1 n o 3.08** (2.89–3.27) 1.74** (1.60–1.88) 1.81** (1.66–1.98) illness long-t erm illness n o 1 1 Ye s 7.71** (7.46–7.96) 6.55** (6.30–6.81)

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Table 4.

results of log

istic r

eg

ression models of fac

tors pr

edic

ting poor psy

cholog ical w ell-being . n ot es: g ener al h ealth Q uestionnair e ( gh Q12) w as dichot omiz ed , with a cut -off sc or e of 12, in to good psy cholog ical w

ell-being (=0) and poor psy

cholog ical w ell-being (=1). O dds r atio ( O r), sig nifican t lev el and c onfidenc e in ter val ( ci ) f or the binar y log istic r eg ressions . M odel 1 = h ousing t ype + r efr ain fr om going out , M odel 2 = M odel 1 + D emog raphic , M odel 3 = M odel 2 + socio -ec onomic + social suppor t, M odel 4 = M odel 3 + illness . *p ≤ 0.05; **p ≤ 0.01. Crude M odel 1 M odel 2 M odel 3 M odel 4 OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI h ousing t

ype and beha

v-iour based on per

ception of neighbourhood saf et y h ousing t ype Priv at e house 1 1 1 1 1 condominium 1.37** (1.30–1.43) 1.33** (1.26–1.41) 1.33** (1.26–1.41) 1.25** (1.18–1.32) 1.23** (1.16–1.31) ren ted apar tmen t 1.88** (1.80–1.96) 1.78** (1.70–1. 86) 1.70** (1.62–1.78) 1.30** (1.24–1.37) 1.27** (1.21–1.34) lodger , dorm or other 1.92** (1.80–2.06) 1.86** (1.72–2.00) 1.59** (1.47–1.73) 1.33** (1.22–1.45) 1.28** (1.17–1.39) refr ain fr om going out n o 1 1 1 1 1 Ye s 1.95** (1.87–2.03) 1.85** (1.78–1.93) 1.67** (1.60–1.75) 1.52** (1.45–1.60) 1.47** (1.40–1.54) D emog raphic g ender _Male 1 1 1 1 Female 1.57** (1.51–1.62) 1.33** (1.27–1.38) 1.44** (1.38–1.50) 1.45** (1.38–1.51) ag e 0.99** (0.99–0.99) 0.99** (0.99–0.99) 0.99** (0.99–0.99) 0.99** (0.98–0.99) educa tion lev el Univ ersit y 1 1 1 1 sec ondar y school or equal 1.12** (1.06–1.17) 1.06* (1.00–1.12) 0.98 (0.93–1.04) 0.96 (0.90–1.01) compulsor y school 1.06* (1.01–1.11) 1.09** (1.03–1.15) 0.98 (0.93–1.03) 0.93* (0.88–0.98) socio -ec onomic Financial sta tus n o pr oblems 1 1 1 h av e pr oblems 3.36** (3.22–3.50) 2.57** (2.44–2.70) 2.31** (2.19–2.43) social suppor t

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Table 5. incidenc e r at e r atios ( irr ) pr edic ting fr equenc y of r ec en t da ys with poor ph ysical health. n ot es: irr , sig nifican t lev el and c onfidenc e in ter val ( ci ) f

or the risk of mor

e r ec en t da ys with poor ph ysical health. M odel 1 = h ousing t ype + r efr ain fr om going out , M odel 2 = M odel 1 + D emog raphic , M odel 3 = M odel 2 + socio -ec onomic + social suppor t, M odel 4 = M odel 3 + illness . *p ≤ 0.05; **p ≤ 0.01. Crude M odel 1 M odel 2 M odel 3 M odel 4 IRR 95% CI IRR 95% CI IRR 95% CI IRR 95% CI IRR 95% CI h ousing t

ype and beha

v-iour based on per

ception of neighbourhood saf et y h ousing t ype Priv at e house 1 1 1 1 1 condominium 1.07** (1.04–1.11) 1.04 (1.00–1.07) 1.07** (1.03–1.10) 1.05* (1.01–1.09) 1.04* (1.00–1.08) ren ted apar tmen t 1.28** (1.24–1.32) 1.23** (1.19–1.27) 1.28** (1.24–1.32) 1.17** (1.13–1.21) 1.15** (1.11–1.19) lodger , dorm or other 1.13** (1.07–1.19) 1.09** (1.03–1.15) 1.27** (1.19–1.34) 1.20** (1.14–1.28) 1.15** (1.08–1.21) refr ain fr om going out n o 1 1 1 1 1 Ye s 1.39** (1.35–1.43) 1.36** (1.32–1.40) 1.25** (1.21–1.29) 1.20** (1.16–1.24) 1.17** (1.13–1.20) D emog raphic g ender M ale 1 1 1 1 Female 1.24** (1.21–1.27) 1.22** (1.19–1.25) 1.23** (1.20–1.27) 1.21** (1.18–1.25) ag e 1.01** (1.01–1.01) 1.01** (1.01–1.01) 1.01** (1.01–1.01) 1.00 (1.00–1.00) educa tion lev el Univ ersit y 1 1 1 1 sec ondar y school or equal 1.21** (1.17–1.26) 1.23** (1.19–1.28) 1.20** (1.16–1.24) 1.15 (1.11–1.19) compulsor y school 1.65* (1.60–1.70) 1.54** (1.49–1.59) 1.50** (1.45–1.55) 1.40 (1.35–1.45) socio -ec onomic Financial sta tus n o pr oblems 1 1 1 h av e pr oblems 1.50** (1.45–1.55) 1.50** (1.45–1.56) 1.29** (1.24–1.33) social suppor t

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Table 6. incidenc e r at e r atios ( irr ) pr edic ting fr equenc y of r ec en t da

ys with poor men

tal health. n ot es: irr , sig nifican t lev el and c onfidenc e in ter val ( ci ) f

or the risk of mor

e r

ec

en

t da

ys with poor men

tal health. M odel 1 = h ousing t ype + r efr ain fr om going out , M odel 2 = M odel 1 + D emog raphic , M odel 3 = M odel 2 + socio -ec onomic + social suppor t, M odel 4 = M odel 3 + illness . **p ≤ 0.01. Crude M odel 1 M odel 2 M odel 3 M odel 4 IRR 95% CI IRR 95% CI IRR 95% CI IRR 95% CI IRR 95% CI h ousing t

ype and beha

v-iour based on per

cep

-tion of neighbourhood safet

y h ousing t ype Priv at e house 1 1 1 1 1 condominium 1.21** (1.16–1.25) 1.18** (1.14–1.23) 1.19** (1.14–1.24) 1.15** (1.11–1.20) 1.13** (1.09–1.17) ren ted apar tmen t 1.62** (1.57–1.67) 1.54** (1.49–1.60) 1.49** (1.44–1.54) 1.29** (1.24–1.33) 1.25** (1.21–1.30) lodger , dorm or other 1.61** (1.52–1.70) 1.57** (1.47–1.66) 1.36** (1.28–1.45) 1.24** (1.17–1.32) 1.19** (1.12–1.27) refr ain fr om going out n o 1 1 1 1 1 Ye s 1.60** (1.55–1.66) 1.54** (1.49–1.59) 1.41** (1.36–1.46) 1.33** (1.29–1.38) 1.29** (1.25–1.34) D emog raphic g ender _Male 1 1 1 1 Female 1.42** (1.38–1.46) 1.30** (1.26–1.34) 1.35** (1.31–1.39) 1.37** (1.32–1.41) ag e 0.99** (0.99–0.99) 0.99** (0.99–0.99) 0.99** (0.99–0.99) 0.99** (0.99–0.99) educa tion lev el Univ ersit y 1 1 1 1 sec ondar y school or equal 1.18** (1.13–1.22) 1.12** (1.08–1.17) 1.08** (1.04–1.12) 1.06** (1.02–1.10) compulsor y school 1.26** (1.22–1.31) 1.29** (1.24–1.34) 1.24** (1.19–1.28) 1.18** (1.14–1.23) socio -ec onomic Financial sta tus n o pr oblems 1 1 1 h av e pr oblems 2.07** (1.99–2.14) 1.70** (1.63–1.77) 1.56** (1.50–1.63) social suppor t

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Table 7. incidenc e r at e r atios ( irr ) pr edic ting fr equenc y of r ec en t da ys without w ork capacit y. n ot es: irr , sig nifican t lev el and c onfidenc e in ter val ( ci ) f

or the risk of mor

e r ec en t da ys without w ork capacit y. M odel 1 = h ousing t ype + r efr ain fr om going out , M odel 2 = M odel 1 + D emog raphic , M odel 3 = M odel 2 + socio -ec onomic + social suppor t, M odel 4 = M odel 3 + illness . *p ≤ 0.05; **p ≤ 0.01. Crude M odel 1 M odel 2 M odel 3 M odel 4 IRR 95% CI IRR 95% CI IRR 95% CI IRR 95% CI IRR 95% CI h ousing t

ype and beha

v-iour based on per

ception of neighbourhood saf et y h ousing t ype Priv at e house 1 1 1 1 1 condominium 1.13** (1.07–1.20) 1.08* (1.01–1.15) 1.21** (1.14–1.29) 1.16** (1.09–1.24) 1.18** (1.11–1.25) ren ted apar tmen t 1.54** (1.47–1.62) 1.45** (1.38–1.54) 1.60** (1.52–1.69) 1.35** (1.28–1.43) 1.36** (1.29–1.43) lodger , dorm or other 1.27** (1.17–1.39) 1.26** (1.15–1.38) 1.58** (1.44–1.74) 1.37** (1.25–1.51) 1.28** (1.17–1.40) refr ain fr om going out n o 1 1 1 1 1 Ye s 1.60** (1.52–1.69) 1.54** (1.46–1.63) 1.42** (1.35–1.50) 1.32** (1.25–1.40) 1.27** (1.21–1.34) D emog raphic g ender _Male 1 1 1 1 Female 1.30** (1.24–1.35) 1.29** (1.23–1.35) 1.31** (1.26–1.38) 1.27** (1.22–1.33) ag e 1.01** (1.01–1.01) 1.01** (1.01–1.02) 1.02** (1.01–1.02) 1.00** (1.00–1.00) educa tion lev el Univ ersit y 1 1 1 1 sec ondar y school or equal 1.30** (1.23–1.37) 1.32** (1.24–1.39) 1.26** (1.19–1.33) 1.15** (1.09–1.22) compulsor y school 1.92** (1.82–2.02) 1.83** (1.73–1.94) 1.73** (1.64–1.83) 1.47** (1.39–1.55) socio -ec onomic Financial sta tus n o pr oblems 1 1 1 h av e pr oblems 2.12** (2.01–2.23) 1.91** (1.79–2.02) 1.59** (1.50–1.68) social suppor t

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ownership form was associated with health than people living in rental housing reported poorer health than people living in owner-occupied housing types (such as private houses and condominium). Similar results have been found previously both in Sweden (Sundquist & Johansson, 1997) and in ecological studies in Britain (Phillimore et al., 1994; Sloggett & Joshi, 1994). These differences may be to some extent related to housing type as a marker for SES, and SES is well known to have a great impact on health. Research implies, however, that housing type should not only be seen as a marker for SES (Hiscock et al., 2003), instead housing, as included in the habitat, should be considered factors with long-time exposures and impact on residents (Veitch, 2008). The living environment may have several pathways to health, such as different physical and social conditions of the house (Rauh et al., 2008), fragmentation (Allardyce et al., 2005), and psychosocial aspects such as the meaning of home (Hiscock et al., 2003). Additionally, form of tenure is closely connected to residential stability, which has been identified as one of the most important predictors of community health, even

more important than many other commonly used sociological variables (Rauh et al., 2008;

Turney & Harknett, 2010). Further, housing may be considered a mediator between SES and health, and that SES have a link to health through environmental exposers associated with different accommodations (Evans, 2004; Rauh et al., 2008). However these links are in some studies elusive and complex, and sometimes needs several levels of analysis to be detected, such as: Social-structural, the neighbourhood level and the individual level (Rauh et al., 2008).

Different housing types also affect residents’ indoor behaviour. Not living in a private house is associated with more sitting time (Saidj et al., 2015), which is a predictor for health, unhealthy days, and mortality (Katzmarzyk et al., 2009; Owen et al., 2010; Duncan et al.,

2014; Pavey et al., 2015). Several studies have demonstrated an association between natural environment and health (de Vries et al., 2003; Maas et al., 2006; Maller et al., 2006), and associations obtained in this study, to some degree, may be explained by differences in green space that vary between housing types. It is well known that settlements used for bungalows increase access to green areas for residents but require sprawling developments, which also seem to be harmful for society on several levels including public health (Frumkin, 2002), obesity (Ewing et al., 2008), neighbourhood satisfaction (Lovejoy et al., 2010), and also in several other ways (Brueckner, 2000). Consequently, the solution to these problems seems not to a forthright promotion of urban sprawl settlements, but rather the solution may be to closer identify the potentially health-beneficial characteristics associated with some housing types and apply them to multiple municipalities’ areas. Today, these questions are a present dilemma in city planning; when moving to more compact cities, how is it assured that people have access to health-promoting green environments? Perhaps the solution is to establish small but qualitative and highly useful green areas, and former studies have shown that quality of neighbourhood greenspace seems relevant with regard to health regardless of quantity (Annerstedt et al., 2012). There may also be a need for improvement of adap-tation of green space to available areas in the physical landscape, for example, in urban

areas constructing greenways and linear parks (Crewe, 2001; Kullmann, 2011). Another

planning strategy that are promoted for healthier cities is mixed-use communities and how to introduce mixed use in existing single-use communities (Angotti & Hanhardt, 2001), however leaving principals of zoning and separation of uses potentially introduces other problems (Angotti & Hanhardt, 2001).

Regardless of how the areas and housing developments are designed, the outdoor envi-ronment must become available for its residents if it could possibly have an impact on health.

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The results of this study point in the same direction as previous research that identified the context of neighbourhood, such as perception of crime and violence, as predictors of health (Stockdale et al., 2007). Fear of crime is related to risk assessment, which depends on the concentration of objective risk and on the perceived signs hazard. The concept of fear may be divided into two components: cognitive (e.g. risk perception) and emotional (e.g. being afraid).

Fear of crime is influenced by social and physical features in the neighbourhood (Jacobs,

1961). Jacobs (1961) argues that mixed developments have a continual flow of people that ensures informal surveillance such as ‘eyes on the street.’ A similar theory that also includes natural surveillance is Newman’s (1995) theory of defensible space, which focuses on res-idents’ observations over the neighbourhood (Schweitzer et al., 1999); this theory argues that difficulty of escape is also important. These theories have some empirical support (Hillier, 1999; Liggett et al., 2001) and are often promoted by the New Urbanism move-ment, although the principals are not always supported for crime prevention (Schweitzer

et al., 1999). Another perspective derived from the offender search theory is the theory of opportunities for potential criminals developed in the field of environmental criminology (Brantingham & Brantingham, 1981). The opportunity perspective seeks to reduce potential offenders’ opportunities to find targets in a location by designing crime out of

neighbour-hoods by lowering permeability (Cozens, 2008). The relationship between environment

and crime is often discussed within the multi-disciplinary approach of crime prevention through environmental design (CPTED), with the goal to ‘design crime out.’ The disorder of broken windows theory implies that obvious signs of disorder in the local environment are experienced as weakened local control and that a lack of concern about the area leads to fear of crime (Wilson & Kelling, 1982).

Fear of crime and safety-related worries are suggested to have an impact on behaviour and potentially decrease physical activity (Gomez et al., 2004), and limit personal freedom (Kullberg et al., 2009). A result of safety-related worry, therefore, may be time-space inequal-ities with implications for health (Whitley & Prince, 2005). Refraining from going out was associated with female gender in this study, a result seen previously (Gomez et al., 2004), the results of this study also indicate that the prevalence of refraining from going out was higher among those who lived in multi-dwelling units. Earlier studies have connected hous-ing and neighbourhood satisfaction (includhous-ing safety) with health (Dunn & Hayes, 2000). Most people in this study rated their health as good or very good, regardless of hous-ing type and behaviour based on neighbourhood safety. From the perspective of public health planning, however, the differences due to housing type and behaviour based on neighbourhood safety are certainly of substantial importance. At the same time, a broad variation in housing types and tenure in the accommodation supply is a resource for an individual’s life journey and for society, and this study’s results imply that society needs more knowledge about housing circumstances and neighbourhood characteristics that have associations with health and how to address them. Sometimes differences in health out-comes are explained with differences in health care, including access, quality and delivery of medical services. As noted by Corburn (2005) health care is likely a contributor to health disparities, but the health care hypothesis does not explain why disparities exist in countries with universal access to health care (Corburn, 2005). This study was performed in Sweden, which is known for universal access to health care and a broad social security system, and the results indicate that the health care system may not fully be able to balance out health

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inequalities between people in living in different housing types and neighbourhoods. The results highlight the importance of factors in the anthroposphere for population health, and that even in presence of a universal health care system, the local environment is important for the maintenance of health. The field of urban planning has several strategies and tools which may be suitable to address health issues as complex and overlapping interactions of conditions, such as; master plans, land-use surveys, community visioning exercises,

spatial analytic techniques/GIS and health impact assessments (Corburn, 2005; Harris et

al., 2010). In practice, the integration of public health and planning can appear in several ways. Barton and Grant (2013) identified three levels of integration in the European Healthy Cities Programme (Barton & Grant, 2013): The first: recognition of the essential life support role of settlements; the second: recognition of the many facets of settlement planning that affect health and the third: where health is fully integrated into the planning process. The authors also point out the need to overcome cooperation difficulties between departments and agencies to get public health units, planners, housing officials, greenspace managers, regeneration and transport planners to work together. Pilkington et al. (2008) demonstrated the potential of using workshops, task-oriented approaches, self-analysis, shared learning and reflection when bringing public health and built environment professionals together in the workforce development initiative (Pilkington et al., 2008).

In this study, individuals’ perceptions on safety of their ambient environment was used, an approach that is known to be effective (White et al., 1987; Stiffman et al., 1999). It has been suggested that subjective measures are effective when they include aspects of under-standing ambient environments that are not captured by observer ratings made by people unfamiliar with the specific environment (Wright & Kloos, 2007). Perceptions of the envi-ronment may also include experiences and memories built up over a long period of time, which might have an impact on emotions related to the stay in the area. This study’s results imply that society needs more knowledge about housing circumstances and neighbourhood characteristics that have associations with health. There is a need for longitudinal studies to investigate this obtained association further and to determine potential causality between different housing configurations, neighbourhood safety and health outcomes. In addition to analytical research on the problem, research in planning is also needed to discuss and evaluate possible interventions and planning policies.

4.1. Strengths and Limitations

The strengths in this study include the large study population and the simple randomly selected sample approach. The response rate is reasonable for questionnaire studies on the population level. This study also has some limitations worth noting. Although we tried to perform adjusted multivariate analysis to assess the sole associations of housing and going-out behaviour, there is a possibility that we have not included some unmeasured but important confounders (e.g. quality of accommodations, various health behaviours associated with housing type).

Being a cross-sectional design, it is not possible to determine cause and effect between predictors and outcomes. It can be discussed if the directions of causality in the underly-ing assumption are appropriate or if there is a possibility of reverse causality. For example, people with poorer health might select housing types that require less work, and healthier people might choose housing types that require more attendance such as keeping a garden.

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We tried to minimize this problem by adjusting the models with long-term illness. More research is needed, however, to determine the causes of these differences in health between types of housing and which factors mediate the role of living in a multi-dwelling unit and the associations with lower health outcomes. There is also a need to investigate specific neighbourhood attributes with regard to particular health outcomes. One further limitation is potential report bias that always may exist due to the nature of self-reported data.

5. Conclusion

This study showed that housing type and behaviour based on perception of neighbourhood safety was associated with SRH and psychological well-being. The results indicated that living in multi-dwelling homes and refraining from going out based on a perception of an unsecure neighbourhood was predictors of ill-health. These associations also appeared in time lost due to poor health and work incapacity. These associations need to be investigated in longitudinal studies. This study suggests that housing and behaviour based on neigh-bourhood safety should be considered important factors of interest to both the individual’s and the population’s health.

Authors’ Contributions

EB designed the study, undertook the statistical modelling, and led the writing. PL and RW designed the study, contributed to data interpretation, and commented on successive drafts of the manuscript and handling of data. All authors approved the final version of the manuscript.

Acknowledgements

We are grateful to all of the respondents for taking time to answer the questionnaire, and we also are thankful to the Public Health Agency of Sweden for providing data for this study.

Disclosure Statement

The authors have no conflicts of interest to declare.

Funding

This work was supported by grants from the Olle Engkvist Byggmästare Foundation.

ORCID

Erik Berglund http://orcid.org/0000-0001-6937-4025

Per Lytsy http://orcid.org/0000-0003-1949-6299

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