A cross-sectional and longitudinal study of neighbourhood disadvantage and cardiovascular disease and the mediating role of physical activity

https://doi.org/10.1016/j.ypmed.2021.106506Get rights and content

Highlights

  • Characteristics of disadvantaged neighbourhoods increase the risk of CVD.

  • Indirect effect of PA on CVD is higher in most disadvantaged neighbourhoods.

  • About 10% of the increased risk of CVD is mediated through lower levels of PA.

Abstract

We investigate the prospective association between neighbourhood-level disadvantage and cardiovascular disease (CVD) among mid-to-older aged adults and whether physical activity (PA) mediates this association. The data come from the HABITAT project, a multilevel longitudinal investigation of health and wellbeing in Brisbane. The participants were 11,035 residents of 200 neighbourhoods in 2007, with follow-up data collected in 2009, 2011, 2013 and 2016. Multilevel binomial regression was used for the cross-sectional analysis and mixed-effect parametric survival models were used for the longitudinal analysis. Models were adjusted for age, sex, education, occupation, and household income. Those with pre-existing CVD at baseline were excluded from the longitudinal analyses. The mediated effect of PA on CVD was examined using multilevel generalized structural equation modelling. There was a total of 20,064 person-year observations across the five time-points clustered at three levels. Results indicated that the incidence of CVD was significantly higher in the most disadvantaged neighbourhoods (OR 1.50; HR 1.29) compared with the least disadvantaged. Mediation analysis results revealed that 11.5% of the effect of neighbourhood disadvantage on CVD occurs indirectly through PA in the most disadvantaged neighbourhoods while the corresponding figure is 5.2% in the more advantaged areas. Key findings showed that neighbourhood disadvantage is associated with the incidence of CVD, and PA is a significant mediator of this relationship. Future research should investigate which specific social and built environment features promote or inhibit PA in disadvantaged areas as the basis for policy initiatives to address inequities in CVD.

Introduction

Cardiovascular disease (CVD) is one of the main causes of the death and disease burden in Australia (Health TDo, 2018). In 2014–15, approximately 4.2 million Australian adults (18.3%) reported having a disease of the circulatory system, and this included around 1.2 million people with cardiovascular conditions such as heart disease and stroke. Also, nearly 2.6 million Australians reported having hypertension (high blood pressure) and 430,000 indicated that they had experienced a heart attack at some point in their life (Statistics ABo, 2015).

A number of studies have found that individual indicators of socioeconomic position (SEP) often measured via educational attainment (Correa-Burrows et al., 2019), occupational class (Leyland, 2005) and household income (Sundquist et al., 2004) are associated with cardiovascular morbidity and mortality (Rachele et al., 2016a). In addition to individual-level measures of SEP as risk factors for CVD (Diez Roux et al., 2004), increased attention is now being given to the characteristics of neighbourhoods. Measures of socioeconomic disadvantage can be captured at the neighbourhood level using various indices, typically created using census data, and include variables such as education, occupation, and household income (Turrell et al., 2014). Further, neighbourhoods also have built and social environment characteristics that may contribute to observed outcomes (Rachele et al., 2016a). For example, neighbourhoods with greater levels of disadvantage often have higher levels of crime (Burton et al., 2009; Loh et al., 2018), poorer access to health-promoting amenities such as green space and water bodies (Schultz et al., 2017; Foley and Kistemann, 2015), and poorer access to higher quality public transport (Knuiman et al., 2014). This is evidenced by the growing body of research on the role of neighbourhood environments in CVD prevention (Correa-Burrows et al., 2019; Rachele et al., 2016a). It is important however to identify behavioural factors that mediate relationships between the neighbourhood environment and cardio-metabolic risk markers (Chandrabose et al., 2019) and hence increase the incidence of CVD. Physical activity (PA) has been found to be inversely associated with risk of cardiovascular disease (Kraus et al., 2019). Previous cross-sectional research has indicated that the neighbourhood environment is associated with the level of residents' PA (Turrell et al., 2013), and regular participation in PA reduces the risk of CVD (Wilmore and Costill, 2004). However, few studies have examined the longitudinal mediating role of PA in the relationships between neighbourhood disadvantage and CVD. The aims of this study are two-fold: first, to examine the total effect of neighbourhood disadvantage on CVD; and second, to address the limitations of previous research by examining the indirect effects of neighbourhood disadvantage on CVD, mediated through PA at five time-points between 2007 and 2016. It is hypothesized that those living in more disadvantaged neighbourhoods are more likely to have lower levels of PA while reporting one or more heart related diseases or risk factors.

Section snippets

Methods

The HABITAT study received ethical clearance from the Queensland University of Technology Human Research Ethics Committee (Ref. Nos. 3967H & 1,300,000,161).

Results

Table 1 presents the proportion of participants classified as experiencing CVD, and the mean total Met-min of PA, by neighbourhood disadvantage and individual-level SEP, in 2007, 2011, and 2016. The probability of being classified as having CVD was highest among residents of socioeconomically disadvantaged neighbourhoods, the least educated, the retired, and members of lower-income families. Moreover, a similar trend can be seen in the total Met-min of PA; each of the above-mentioned groups

Discussion

This study contributes to the growing evidence that a neighbourhood's socioeconomic environment plays an important role in the incidence of CVD, independent of individual level socioeconomic factors. Adjustment for a range of confounders only partially explained these associations, suggesting that other underlying behavioural pathways may be involved. A review of the literature showed that higher levels of PA are associated with a lower risk of experiencing heart disease (Kraus et al., 2019),

Conclusion

Our study suggests that some characteristics of disadvantaged neighbourhoods are directly and causally associated with the prevalence and incidence of CVD. Moreover, more deprived neighbourhoods appear to cause residents of these environments to be less physically active which contributes to their increased risk of CVD. Improvement to disadvantaged neighbourhoods may be a potential strategy to enhance population health by encouraging more PA. Further studies are recommended to examine specific

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The HABITAT study is funded by the National Health and Medical Research Council (NHMRC) (ID 497236, 339718, 1047453).

References (57)

  • D. Aggio et al.

    Trajectories of physical activity from midlife to old age and associations with subsequent cardiovascular disease and all-cause mortality

    J. Epidemiol. Community Health

    (2019)
  • P.D. Allison

    Missing Data

    (2001)
  • S.S. Anand et al.

    Social disadvantage and cardiovascular disease: development of an index and analysis of age, sex, and ethnicity effects

    Int. J. Epidemiol.

    (2006)
  • P.C. Austin

    A tutorial on multilevel survival analysis: methods, models and applications

    Int. Stat. Rev.

    (2017)
  • E.L. Barr et al.

    Validity of self-reported cardiovascular disease events in comparison to medical record adjudication and a statewide hospital morbidity database: the AusDiab study

    Intern. Med. J.

    (2009)
  • R.A. Block et al.

    Prospective and retrospective duration judgments: a meta-analytic review

    (1997)
  • Z. Bonyadi et al.

    Cardiovascular, respiratory, and total mortality attributed to PM 2.5 in Mashhad, Iran

    Environ. Monit. Assess.

    (2016)
  • M.L. Booth et al.

    Perceived barriers to physical activity among older Australians

    J. Aging Phys. Act.

    (2002)
  • N.W. Burton et al.

    HABITAT: a longitudinal multilevel study of physical activity change in mid-aged adults

    BMC Public Health

    (2009)
  • M. Chandrabose et al.

    Neighborhood walkability and 12-year changes in cardio-metabolic risk: the mediating role of physical activity

    Int. J. Behav. Nutr. Phys. Act.

    (2019)
  • P. Correa-Burrows et al.

    Cardiometabolic health in adolescence and its association with educational outcomes

    J. Epidemiol. Community Health

    (2019)
  • M.J. Crowther et al.

    Multilevel mixed effects parametric survival models using adaptive Gauss–Hermite quadrature with application to recurrent events and individual participant data meta-analysis

    Stat. Med.

    (2014)
  • Barreto P. de Souto et al.

    Physical activity and incident chronic diseases: a longitudinal observational study in 16 European countries

    Am. J. Prev. Med.

    (2017)
  • A.V. Diez Roux et al.

    Neighbourhood environments and mortality in an elderly cohort: results from the cardiovascular health study

    J. Epidemiol. Community Health

    (2004)
  • D.A. Dillman et al.

    Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method

    (2014)
  • I. Do Ha et al.

    Mixed-Effects Survival Models. Statistical Modelling of Survival Data with Random Effects

    (2017)
  • G.M. Fitzmaurice et al.

    Applied Longitudinal Analysis

    (2012)
  • F. Hayes
  • Cited by (8)

    • Lifestyle factors as mediators of area-level socio-economic differentials in cardiovascular disease risk factors. The Tromsø Study

      2022, SSM - Population Health
      Citation Excerpt :

      There are few studies which clearly link ASES to metabolic CVD risk factors. However, the role of mediators is not clearly understood, and only few studies have identified lifestyle behaviors as mediators of the association between ASES and CVD (Saghapour et al., 2021; Zhang et al., 2021) but not metabolic CVD risk factors. To our knowledge, the potential impact of ASES on cardiometabolic risk factors via various lifestyle behaviors independent of individual-level SES has not previously been investigated.

    • Predictors and Patterns of Physical Activity From Transportation Among United States Youth, 2007-2016

      2021, Journal of Adolescent Health
      Citation Excerpt :

      However, physical activity levels are shown in prior research studies to be lower in minority/low-income subgroups [8]. Prior studies also show that for disadvantaged populations, physical activity is a particularly important driver of cardiovascular health disparities [24], and that lower neighborhood support of physical activity is associated with higher youth obesity [25]. Given that social and structural drivers of health are critical determinants of youth physical activity participation [2,19], promoting active transportation may present an opportunity to increase physical activity and reduce youth health disparities in under resourced settings [17].

    View all citing articles on Scopus
    View full text