Elsevier

Health & Place

Volume 57, May 2019, Pages 74-81
Health & Place

Population density is beneficially associated with 12-year diabetes risk marker change among residents of lower socio-economic neighborhoods

https://doi.org/10.1016/j.healthplace.2019.02.006Get rights and content

Highlights

  • Neighborhood SES moderated longitudinal relationships of density with diabetes risk.

  • Higher density relates to lower diabetes risk in low SES neighborhoods.

  • Initiatives to reduce health inequalities should take density into account.

Abstract

We examined associations of neighborhood population density with 12-year changes in diabetes risk (post-challenge plasma glucose), and potential moderation by neighborhood socio-economic status (SES) among 4,816 Australians. In lower SES neighborhoods, post-challenge plasma glucose increased by 6% in low-density, remained stable in medium-density and decreased by 3% in high-density neighborhoods. In medium SES neighborhoods, glucose remained stable in high-density, but increased by 2% and 3% in medium- and low-density neighborhoods, respectively. In higher SES neighborhoods, no significant interaction effect between time and density was observed. Densification may make protective contributions for diabetes risk in lower and medium SES neighborhoods.

Introduction

Worldwide, there are 415 million adults aged 20–79 years (8.8%) with diabetes and about three quarters of these are aged under 65 years (Diabetes Atlas 7, 2015). Diabetes is the eighth leading contributor to mortality with 1.5 million annual deaths worldwide (World Health Organization, 2016) and its prevalence is expected to increase in the coming decade (Diabetes Atlas 7, 2015). Type 2 diabetes, which is characterized by chronically elevated blood glucose levels in the fasting state and following the ingestion of food and drink, represents 90–95% of all diabetes cases (American Diabetes Assocat, 2016). In comparison to fasting plasma glucose levels, postprandial or post-challenge plasma glucose levels are measured two hours after ingestion of a standardized sugary solution (World Health Organization, 1999). Even below the diagnostic threshold for diabetes, fasting and post-challenge plasma glucose are detrimentally associated with risk for cardio- and cerebrovascular disease (Gerstein et al., 2007; Levitan et al., 2004; Quinn et al., 2011; Cavalot et al., 2011) and cardiovascular and all-cause mortality (Barr et al., 2007; de Vegt et al., 1999). Elevated blood glucose levels have been estimated to be responsible for an additional 2.2 million deaths annually, this is in addition to the 1.5 million deaths from type 2 diabetes itself (World Health Organization, 2016). Blood glucose levels increase with age, with greater increases in post-challenge plasma glucose observed after 50 years of age - rendering middle-aged and older adults particularly vulnerable (Tuomilehto et al., 2003).

Environmental and policy initiatives to promote physical activity form one promising strategy for preventing the development of type 2 diabetes (World Health Organization, 2016). Population density is a key attribute in this context, with higher density levels being related to higher levels of physical activity (Giles-Corti et al., 2016), which are in turn related to lower plasma glucose levels (Colberg et al., 2010). Population density is a key component of transport-related walkability, which has been shown to be positively related to physical activity among adults (Sallis et al., 2009; Owen et al., 2007; Van Dyck et al., 2010; Barnett et al., 2017). It is a fundamental attribute related to other components of walkability such as land use diversity and street connectivity. A certain level of neighborhood population density is required to support the viability of shops and services (i.e. divers land uses) (Giles-Corti et al., 2012; Towards, 2017). Areas with higher population density tend to have more connected streets (Koohsari et al., 2016). Population density may also influence diabetes risk through mechanisms other than physical activity. These include diet, sedentary behavior, stress, sleep, air and noise pollution, social contacts and access to health care services (AIHW, 2016; Dendup et al., 2018).

A recent meta-analysis and literature review reported higher walkability to be related to a lower type 2 diabetes risk (den Braver et al., 2018; Mena et al., 2017). The meta-analysis included six studies, from which four were longitudinal with follow-up periods ranging from six to twelve years. All longitudinal studies were conducted in Northern America and only two focused on markers of blood glucose levels (i.e. HOMA-index and fasting glucose). Even below the diagnostic threshold for diabetes, increased glucose levels (measured by HOMA-index, hemoglobin A1c, fasting glucose or post-challenge plasma glucose) are detrimentally associated with risk for cardio- and cerebrovascular disease (Gerstein et al., 2007; Levitan et al., 2004; Quinn et al., 2011; Cavalot et al., 2011) and cardiovascular and all-cause mortality (Barr et al., 2007; de Vegt et al., 1999). In an Australian study, which was not included in the above-described meta-analysis, the relationship between baseline walkability and changes in hemoglobin A1c was examined; greater baseline walkability was found to be related to smaller increases in hemoglobin A1c over 10 years (Carroll et al., 2016). No previous study has examined the relationship between walkability components and post-challenge plasma glucose levels, which better reflect glucose homeostasis and are more predictive of cardiovascular disease risk than fasting glucose levels (Cavalot et al., 2011; Ketema and Kibret, 2015). Furthermore, post-challenge plasma glucose has been shown to be more strongly related to physical activity than is fasting glucose (Healy et al., 2006; Faerch et al., 2009).

The theory of differential susceptibility posits that health outcomes among those of lower socio-economic background may be more susceptible to environmental exposures (Diderichsen et al., 2001). This implies that population density may be expected to have a stronger influence on cardio-metabolic health among residents from low compared to high socio-economic status (SES) neighborhoods. This may be partly explained by residents of lower SES neighborhoods being predisposed to poor cardio-metabolic health as a result of the accumulation of other risk factors (e.g., air pollution, poor social networks) (WHO, 2010). Furthermore, those of lower socio-economic backgrounds can be less likely to participate in organized sports, especially in sports requiring specific (costly) equipment (Eime et al., 2015). This implies that they are more reliant on cost-free forms of PA such as transportation walking, which are more strongly influenced by population density than recreational PA and sports participation. Examining relationships between walkability and diabetes incidence in Canada, stronger relationships were observed in low compared to high SES neighborhoods (Booth et al., 2013). However, such a moderation (or interaction) effect was not replicated in a Swedish study (Sundquist et al., 2015). In this context, there is the need to examine whether population density is more strongly related to post-challenge plasma glucose among inhabitants of low compared to high SES neighborhoods.

In a sample of middle-aged and older Australian adults (≥45 years), we examined how 12-year changes in a key diabetes risk marker (post-challenge plasma glucose) were moderated by neighborhood-level population density and SES. Additionally, we examined whether the changes in post-challenge plasma-glucose were mediated through physical activity. We hypothesized that post-challenge plasma glucose levels would increase because of participants' ageing and that these increases would be greater in residents from neighborhoods with lower compared to higher population density. Additionally, we hypothesized that this difference in increases in plasma glucose levels according to population density would be more pronounced in lower than in higher SES neighborhoods, and that physical activity would partially explain such differential changes.

Section snippets

Protocol and participant recruitment

We used data from the Australian Diabetes, Obesity and Lifestyle (AusDiab) study, a population-wide prospective cohort study consisting of three measurement periods in 1999–2000 (baseline, time 1), 2004–2005 (time 2) and 2011–2012 (time 3) following the same individuals. The AusDiab Study has been described in detail elsewhere (Dunstan et al., 2002; Shibata et al., 2015). In 1999–2000, non-institutionalized adults (≥25 years) were recruited from private dwellings within 42 clusters of 105

Descriptive statistics

Table 1 shows descriptive statistics for the time-constant and time-varying attributes. Compared to non-completers, those completing all three measurement periods were younger, had a higher educational attainment, had higher incomes, were less likely to be retired and widowed, had higher fat, protein, carbohydrate and alcohol intake, were less likely to be a current smoker, were more physically active and had lower post-challenge plasma glucose (see Supplementary File 2).

Changes in post-challenge plasma glucose by neighborhood population density and SES

The final model

Discussion

We examined the relationships of neighborhood-level population density with 12-year changes in a key diabetes risk marker (post-challenge plasma glucose), and the moderating effect of neighborhood SES. Contrary to our hypothesis, glucose levels decreased between times 1 and 2. Between times 2 and 3, glucose levels did increase. Within medium and high SES neighborhoods, these increases occurred independently from neighborhood population density. Within low SES neighborhoods, increases in glucose

Competing interests

None declared.

Funding

JVC is supported by a postdoctoral fellowship of the Research Foundation Flanders (FWO, 12I1117N). EC is supported by an Australian Research Council Future Fellowship FT3 140100085. NO was supported by a National Health and Medical Research Council (NHMRC) of Australia Program Grant (#569940), a Senior Principal Research Fellowship (#1003960) and by the Victorian Government's Operational Infrastructure Support program. DD was supported by a NHMRC Senior Research Fellowship (NHMRC 1078360) and

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