Natasha J. Howard

Effects of area deprivation on health risks and outcomes: a multilevel, cross-sectional, Australian population study

Objectives:Our aim was to examine the effect of local area socio-economic disadvantage after accounting for individual socio-economic status (SES), and to determine if these differ between various health and risk factor variables.Methods:The North West Adelaide Health Study (NWAHS) is a biomedical representative population study of adults. The Index of Relative Socio-Economic Disadvantage (IRSD), produced from the Australian Bureau of Statistics (ABS) Census data at the level of Collector Districts (200 dwellings) was used as an indicator of local area disadvantage. Multi-level modeling techniques examined the effects of IRSD level on a variety of health outcomes and risk factors, after accounting for individual socio-economic factors.Results:Significant, independent associations were seen between IRSD and obesity, smoking, and health-related quality of life, with 5 % to 7.2 % of the variance located at the neighborhood level. No independent associations were seen between IRSD and estimated cardiovascular disease risk, diabetes, physical activity, or at-risk alcohol use.Conclusions:Aggregated area-level characteristics make modest, but significant independent contributions to smoking, obesity and quality of life, but not for other health outcomes.

Is walkability associated with a lower cardiometabolic risk

Walkability of residential environments has been associated with more walking. Given the health benefits of walking, it is expected that people living in locations with higher measured walkability should have a lower risk of cardiometabolic diseases. This study tested the hypothesis that higher walkability was associated with a lower cardiometabolic risk (CMR) for two administrative spatial units and three road buffers. Data were from the North West Adelaide Health Study first wave of data collected between 2000 and 2003. CMR was expressed as a cumulative sum of six clinical risk markers, selected to reflect components of the metabolic syndrome. Walkability was based on an established methodology and operationalised as dwelling density, intersection density, land-use mix and retail footprint. Walkability was associated with lower CMR for the three road buffer representations of the built environment but not for the two administrative spatial units. This may indicate a limitation in the use of administrative spatial units for analyses of walkability and health outcomes.

Food environment, walkability, and public open spaces are associated with incident development of cardio-metabolic risk factors in a biomedical cohort

We investigated whether residential environment characteristics related to food (unhealthful/healthful food sources ratio), walkability and public open spaces (POS; number, median size, greenness and type) were associated with incidence of four cardio-metabolic risk factors (pre-diabetes/diabetes, hypertension, dyslipidaemia, abdominal obesity) in a biomedical cohort (n=3205). Results revealed that the risk of developing pre-diabetes/diabetes was lower for participants in areas with larger POS and greater walkability. Incident abdominal obesity was positively associated with the unhealthful food environment index. No associations were found with hypertension or dyslipidaemia. Results provide new evidence for specific, prospective associations between the built environment and cardio-metabolic risk factors.

Relative residential property value as a socio-economic status indicator for health research

BackgroundResidential property is reported as the most valuable asset people will own and therefore provides the potential to be used as a socio-economic status (SES) measure. Location is generally recognised as the most important determinant of residential property value.Extending the well-established relationship between poor health and socio-economic disadvantage and the role of residential property in the overall wealth of individuals, this study tested the predictive value of the Relative Location Factor (RLF), a SES measure designed to reflect the relationship between location and residential property value, and six cardiometabolic disease risk factors, central obesity, hypertriglyceridemia, reduced high density lipoprotein (HDL), hypertension, impaired fasting glucose, and high low density lipoprotein (LDL). These risk factors were also summed and expressed as a cumulative cardiometabolic risk (CMR) score.MethodsRLF was calculated using a global hedonic regression model from residential property sales transaction data based upon several residential property characteristics, but deliberately blind to location, to predict the selling price of the property. The predicted selling price was divided by the actual selling price and the results interpolated across the study area and classified as tertiles. The measures used to calculate CMR were collected via clinic visits from a population-based cohort study. Models with individual risk factors and the cumulative cardiometabolic risk (CMR) score as dependent variables were respectively tested using log binomial and Poisson generalised linear models.ResultsA statistically significant relationship was found between RLF, the cumulative CMR score and all but one of the risk factors. In all cases, participants in the most advantaged and intermediate group had a lower risk for cardio-metabolic diseases. For the CMR score the RR for the most advantaged was 19% lower (RR = 0.81; CI 0.76-0.86; p <0.0001) and the middle group was 9% lower (RR = 0.91; CI 0.86-0.95; p <0.0001) than the least advantaged group.ConclusionsThis paper advances the understanding of the nexus between place, health and SES by providing an objective spatially informed SES measure for testing health outcomes and reported a robust association between RLF and several health measures.

Do Relationships Between Environmental Attributes and Recreational Walking Vary According to Area-Level Socioeconomic Status?

Residents of areas with lower socioeconomic status (SES) are known to be less physically active during leisure time. Neighborhood walkability has been shown to be related to recreational walking equally in low and high SES areas. This cross-sectional study tested whether associations of specific environmental attributes, measured objectively and subjectively, with walking for recreation were moderated by area-level SES. The data of the North West Adelaide Health Study collected in 2007 (n = 1500, mean age 57) were used. Self-reported walking frequency was the outcome of the study. Environmental exposure measures included objectively measured walkability components (residential density, intersection density, land use mix, and net retail area ratio) and perceived attributes (access to destinations, neighborhood esthetics, walking infrastructure, traffic/barriers, and crime safety). Participants’ suburbs were categorized into low and high SES areas using an indicator of socioeconomic disadvantage. Low SES areas had lower scores in residential density, neighborhood esthetics, walking infrastructure, traffic/barriers, and crime safety. Recreational walking was associated with residential density, access to destinations, esthetics, traffic/barriers, and crime safety. Effect modification was observed for two attributes (out of nine): residential density was associated with walking only in low SES areas, while walking infrastructure was associated with walking only in high SES areas. The associations of neighborhood environmental attributes with recreational walking were largely consistent across SES groups. However, low SES areas were disadvantaged in most perceived environmental attributes related to recreational walking. Improving such attributes in low SES neighborhoods may help close socioeconomic disparities in leisure time physical activity.

Severe obesity: Investigating the socio-demographics within the extremes of body mass index

SUMMARY OBJECTIVE To examine the trends in the prevalence of classes I, II and III obesity between 1991 and 2006 among the South Australian adult population. In addition, to explore the association of severe (class II and III) obesity with a range of socio-demographics, chronic conditions and risk factor variables. METHOD Trends of self-reported obesity prevalence were examined using representative, annual, face-to-face South Australian Health Omnibus Surveys from 1991 to 2006 (n ≈ 3000 per year). Biomedical data, including measured height and weight, were collected in the North West Adelaide Health (cohort) Study (NWAHS), a representative random adult sample selected from the electronic white pages (EWP) (n = 4060). RESULTS The age standardised prevalence of self-reported class II and III obesity among those aged 18 years and over increased from 2.4% in 1991 to 8.1% in 2006. The greatest relative percentage increase over this time was seen amongst those with class III obesity (452.3%). Using biomedical data, multivariate analysis results indicated that among those who were obese, women were more than two and a half times more likely than men to be of class II and III. Among those who were obese, those aged 20-54 years and living in the low/lowest quintiles of Socioeconomic Indexes for Areas, Index of Relative Socioeconomic Disadvantage (SEIFA IRSD) were statistically significantly more likely to be class II or III obese when compared to those in the highest categories. CONCLUSION The prevalence of class II and III obesity increased significantly between 1991 and 2006. The socio-demographics of those who are class II and III obesity are different from those that are normally described for obesity as a whole especially in regard to the younger age cohort. The current prevalence of severe obesity within Australia is probably underestimated and these results highlight the need to address this sub-group of the population.

Illuminating the lifecourse of place in the longitudinal study of neighbourhoods and health.

Place and health are inextricably entwined. Whilst insights have been gained into the associations between places, such as neighbourhoods, and health, the understanding of these relationships remains only partial. One of the reasons for this relates to time and change and the inter-relationships between the dynamic nature of both neighbourhoods and health. This paper argues that the lifecourse of place can be used as a conceptual framework to understand the evolution and ongoing development of neighbourhoods, and their impact on the geographies of health, past, present and future. Moreover, this paper discusses the capacity of a longitudinal form of enquiry - latent transition analysis - that is able to operationalise conceptual models of the lifecourse of place. To date, latent transition analysis has not been applied to the study of neighbourhoods and health. Drawing on research across a range of disciplines including developmental psychology, sociology, geography and epidemiology, this paper also considers praxis-based implications and recommendations for applications of latent transition analysis that aim to advance understanding of how neighbourhoods affect health in and over time.

Validation of continuous clinical indices of cardiometabolic risk in a cohort of Australian adults

BackgroundIndicators of cardiometabolic risk typically include non-clinical factors (e.g., smoking). While the incorporation of non-clinical factors can improve absolute risk prediction, it is impossible to study the contribution of non-clinical factors when they are both predictors and part of the outcome measure. Metabolic syndrome, incorporating only clinical measures, seems a solution yet provides no information on risk severity. The aims of this study were: 1) to construct two continuous clinical indices of cardiometabolic risk (cCICRs), and assess their accuracy in predicting 10-year incident cardiovascular disease and/or type 2 diabetes; and 2) to compare the predictive accuracies of these cCICRs with existing risk indicators that incorporate non-clinical factors (Framingham Risk Scores).MethodsData from a population-based biomedical cohort (n = 4056) were used to construct two cCICRs from waist circumference, mean arteriole pressure, fasting glucose, triglycerides and high density lipoprotein: 1) the mean of standardised risk factors (cCICR-Z); and 2) the weighted mean of the two first principal components from principal component analysis (cCICR-PCA). The predictive accuracies of the two cCICRs and the Framingham Risk Scores were assessed and compared using ROC curves.ResultsBoth cCICRs demonstrated moderate accuracy (AUCs 0.72 – 0.76) in predicting incident cardiovascular disease and/or type 2 diabetes, among men and women. There were no significant differences between the predictive accuracies of the cCICRs and the Framingham Risk Scores.ConclusionscCICRs may be useful in research investigating associations between non-clinical factors and health by providing suitable alternatives to current risk indicators which include non-clinical factors.

Area-level socioeconomic characteristics, prevalence and trajectories of cardiometabolic risk.

This study examines the relationships between area-level socioeconomic position (SEP) and the prevalence and trajectories of metabolic syndrome (MetS) and the count of its constituents (i.e., disturbed glucose and insulin metabolism, abdominal obesity, dyslipidemia, and hypertension). A cohort of 4,056 men and women aged 18+ living in Adelaide, Australia was established in 2000–2003. MetS was ascertained at baseline, four and eight years via clinical examinations. Baseline area-level median household income, percentage of residents with a high school education, and unemployment rate were derived from the 2001 population Census. Three-level random-intercepts logistic and Poisson regression models were performed to estimate the standardized odds ratio (SOR), prevalence risk ratio (SRR), ratio of SORs/SRRs, and (95% confidence interval (CI)). Interaction between area- and individual-level SEP variables was also tested. The odds of having MetS and the count of its constituents increased over time. This increase did not vary according to baseline area-level SEP (ratios of SORs/SRRs ≈ 1; p ≥ 0.42). However, at baseline, after adjustment for individual SEP and health behaviours, median household income (inversely) and unemployment rate (positively) were significantly associated with MetS prevalence (SOR (95%CI) = 0.76 (0.63–0.90), and 1.48 (1.26–1.74), respectively), and the count of its constituents (SRR (95%CI) = 0.96 (0.93–0.99), and 1.06 (1.04–1.09), respectively). The inverse association with area-level education was statistically significant only in participants with less than post high school education (SOR (95%CI) = 0.58 (0.45–0.73), and SRR (95%CI) = 0.91 (0.88–0.94)). Area-level SEP does not predict an elevated trajectory to developing MetS or an elevated count of its constituents. However, at baseline, area-level SEP was inversely associated with prevalence of MetS and the count of its constituents, with the association of area-level education being modified by individual-level education. Population-level interventions for communities defined by area-level socioeconomic disadvantage are needed to reduce cardiometabolic risks.

Investigating Individual- and Area-Level Socioeconomic Gradients of Pulse Pressure among Normotensive and Hypertensive Participants

Socioeconomic status is a strong predictor of cardiovascular disease. Pulse pressure, the difference between systolic and diastolic blood pressure, has been identified as an important predictor of cardiovascular risk even after accounting for absolute measures of blood pressure. However, little is known about the social determinants of pulse pressure. The aim of this study was to examine individual- and area-level socioeconomic gradients of pulse pressure in a sample of 2,789 Australian adults. Using data from the North West Adelaide Health Study we estimated the association between pulse pressure and three indices of socioeconomic status (education, income and employment status) at the area and individual level for hypertensive and normotensive participants, using Generalized Estimating Equations. In normotensive individuals, area-level education (estimate: −0.106; 95% CI: −0.172, −0.041) and individual-level income (estimate: −1.204; 95% CI: −2.357, −0.050) and employment status (estimate: −1.971; 95% CI: −2.894, −1.048) were significant predictors of pulse pressure, even after accounting for the use of medication and lifestyle behaviors. In hypertensive individuals, only individual-level measures of socioeconomic status were significant predictors of pulse pressure (education estimate: −2.618; 95% CI: −4.878, −0.357; income estimate: −1.683, 95% CI: −3.743, 0.377; employment estimate: −2.023; 95% CI: −3.721, −0.326). Further research is needed to better understand how individual- and area-level socioeconomic status influences pulse pressure in normotensive and hypertensive individuals.