Comparison of a spatial perspective with the multilevel analytical approach in neighborhood studies: the case of mental and behavioral disorders due to psychoactive substance use in Malmo, Sweden, 2001

Nationwide geospatial analysis of county racial and ethnic composition and public drinking water arsenic and uranium

There is no safe level of exposure to inorganic arsenic or uranium, yet recent studies identified sociodemographic and regional inequalities in concentrations of these frequently detected contaminants in public water systems across the US. We analyze the county-level association between racial/ethnic composition and public water arsenic and uranium concentrations from 2000-2011 using geospatial models. We find that higher proportions of Hispanic/Latino and American Indian/Alaskan Native residents are associated with significantly higher arsenic and uranium concentrations. These associations differ in magnitude and direction across regions; higher proportions of non-Hispanic Black residents are associated with higher arsenic and uranium in regions where concentrations of these contaminants are high. The findings from this nationwide geospatial analysis identifying racial/ethnic inequalities in arsenic and uranium concentrations in public drinking water across the US can advance environmental justice initiatives by informing regulatory action and financial and technical support to protect communities of color.

Comparison of spatial approaches to assess the effect of residing in a 20-minute neighbourhood on body mass index

Beliefs that neighbourhood environments influence body mass index (BMI) assume people residing proximally have similar outcomes. However, spatial relationships are rarely examined. We considered spatial autocorrelation when estimating associations between neighbourhood environments and BMI in two Australian cities. Using cross-sectional data from 1329 participants (Melbourne = 637, Adelaide = 692), spatial autocorrelation in BMI was examined for different spatial weights definitions. Spatial and ordinary least squares regression were compared to assess how accounting for spatial autocorrelation influenced model findings. Geocoded household addresses were used to generate matrices based on distances between addresses. We found low positive spatial autocorrelation in BMI; magnitudes differed by matrix choice, highlighting the need for careful consideration of appropriate spatial weighting. Results indicated statistical evidence of spatial autocorrelation in Adelaide but not Melbourne. Model findings were comparable, with no residual spatial autocorrelation after adjustment for confounders. Future neighbourhoods and BMI research should examine spatial autocorrelation, accounting for this where necessary.

MobiliSense cohort study protocol: do air pollution and noise exposure related to transport behaviour have short-term and longer-term health effects in Paris, France?

INTRODUCTION

MobiliSense explores effects of air pollution and noise related to personal transport habits on respiratory and cardiovascular health. Its objectives are to quantify the contribution of personal transport/mobility to air pollution and noise exposures of individuals; to compare exposures in different transport modes; and to investigate whether total and transport-related personal exposures are associated with short-term and longer-term changes in respiratory and cardiovascular health.

METHODS AND ANALYSIS

MobiliSense uses sensors of location, behaviour, environmental nuisances and health in 290 census-sampled participants followed-up after 1/2 years with an identical sensor-based strategy. It addresses knowledge gaps by: (1) assessing transport behaviour over 6 days with GPS receivers and GPS-based mobility surveys; (2) considering personal exposures to both air pollution and noise and improving their characterisation (inhaled doses, noise frequency components, etc); (3) measuring respiratory and cardiovascular outcomes (smartphone-assessed respiratory symptoms, lung function with spirometry, resting blood pressure, ambulatory brachial/central blood pressure, arterial stiffness and heart rate variability) and (4) investigating short-term and longer-term (over 1-2 years) effects of transport.

ETHICS AND DISSEMINATION

The sampling and data collection protocol was approved by the National Council for Statistical Information, the French Data Protection Authority and the Ethical Committee of Inserm. Our final aim is to determine, for communicating with policy-makers, how scenarios of changes in personal transport behaviour affect individual exposure and health.

Associations of socioeconomic status indicators and migrant status with risk of a low vegetable and fruit consumption in children

Background

It is important to provide insight in potential target groups for interventions to reduce socioeconomic inequalities in children's vegetable/fruit consumption. In earlier studies often single indicators of socioeconomic status (SES) or migrant status have been used. However, SES is a multidimensional concept and different indicators may measure different SES dimensions. Our objective is to explore multiple associations of SES indicators and migrant status with risk of a low vegetable/fruit consumption in a large multi-ethnic and socioeconomically diverse sample of children.

Methods

We included 5,010 parents of 4- to 12-year-olds from a Dutch public health survey administered in 2018. Cross-sectional associations of parental education, material deprivation, perceived financial difficulties, neighbourhood socioeconomic status (NSES) and migrant status with low (≤4 days a week) vegetable and fruit consumption in children were assessed using multilevel multivariable logistic regression models. Results are displayed as odds ratios (OR) with 95% confidence intervals (CI).

Results

Of the 4- to 12-year-olds, 22.1% had a low vegetable consumption and 11.9% a low fruit consumption. Low (OR 2.51; 95%CI: 2.05, 3.07) and intermediate (OR 1.83; 95%CI: 1.54, 2.17) parental education, material deprivation (OR 1.45; 95%CI: 1.19, 1.76), low NSES (OR 1.28; 95%CI: 1.04, 1.58) and a non-Western migrant status (OR 1.94; 95%CI: 1.66, 2.26) were associated with a higher risk of a low vegetable consumption. Low (OR 1.68; 95%CI: 1.31, 2.17) and intermediate (OR 1.39; 95%CI: 1.12, 1.72) parental education and material deprivation (OR 1.63; 95%CI: 11.27, 2.08) were also associated with a higher risk of a low fruit consumption.

Conclusion

Our findings indicate associations of multiple SES indicators and migrant status with a higher risk of a low vegetable/fruit consumption in children and thus help to identify potential target groups.

•

Vegetable and fruit consumptions on 4 days or less a week is reported for respectively 22.1% and 11.9% of children.

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Children's vegetable but not fruit consumption differs between neighbourhoods.

•

A non-Western migrant status and a low socio-economic status of the neighbourhood are associated with low vegetable consumption.

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Low/intermediate education and material deprivation are associated with low vegetable and fruit consumption.

•

This study provides clues to potential entry points for reducing inequalities in vegetable and fruit consumption.

Neighborhood socioeconomic deprivation and psychiatric medication purchases. Different neighborhood delineations, different results? A nationwide register-based multilevel study

Previous studies of neighborhood socioeconomic deprivation and mental health treatment have shown mixed results. Multiple reviews have highlighted that the definition and measurement of neighborhoods can lead to different results, providing one explanation for these mixed findings. This study compares the use of micro-areas created using an automated redistricting algorithm and divided by physical barriers with the use of two administrative units, Danish parishes and postal codes. The geographical data are linked to Danish register data of the Danish population from age 16 in 2017, N = 4,347,001, to measure the association between the purchase of psychiatric medication and neighborhood socioeconomic deprivation using logistic multilevel models. Neighborhood socioeconomic deprivation is associated with a slightly increased probability of redeeming prescriptions for psychiatric medication after controlling for individual sociodemographic composition. However, this association was present only for micro-areas and not for parishes or postal codes. The results indicate that neighborhood effects on psychiatric medication purchases are affected by the neighborhood delineations used and that future studies should carefully consider how neighborhoods are defined and measured.

A method to define the relevant ego-centred spatial scale for the assessment of neighbourhood effects: the example of cardiovascular risk factors

Introduction

The neighbourhood in which one lives affects health through complex pathways not yet fully understood. A way to move forward in assessing these pathways direction is to explore the spatial structure of health phenomena to generate hypotheses and examine whether the neighbourhood characteristics are able to explain this spatial structure. We compare the spatial structure of two cardiovascular disease risk factors in three European urban areas, thus assessing if a non-measured neighbourhood effect or spatial processes is present by either modelling the correlation structure at individual level or by estimating the intra-class correlation within administrative units.

Methods

Data from three independent studies (RECORD, DHS and BaBi), covering each a European urban area, are used. The characteristics of the spatial correlation structure of cardiovascular risk factors (BMI and systolic blood pressure) adjusted for age, sex, educational attainment and income are estimated by fitting an exponential model to the semi-variogram based on the geo-coordinates of places of residence. For comparison purposes, a random effect model is also fitted to estimate the intra-class correlation within administrative units. We then discuss the benefits of modelling the correlation structure to evaluate the presence of unmeasured spatial effects on health.

Results

BMI and blood pressure are consistently found to be spatially structured across the studies, the spatial correlation structures being stronger for BMI. Eight to 22% of the variability in BMI were spatially structured with radii ranging from 100 to 240 m (range). Only a small part of the correlation of residuals was explained by adjusting for the correlation within administrative units (from 0 to 4 percentage points).

Discussion

The individual spatial correlation approach provides much stronger evidence of spatial effects than the multilevel approach even for small administrative units. Spatial correlation structure offers new possibilities to assess the relevant spatial scale for health. Stronger correlation structure seen for BMI may be due to neighbourhood socioeconomic conditions and processes like social norms at work in the immediate neighbourhood.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11356-w.

A multi-level spatio-temporal analysis on prostate cancer outcomes

BACKGROUND

Geographic and temporal variation in positive surgical margins (PSM) for prostate cancer after radical prostatectomy (RP) has been observed. However, it is unclear how much of this variation could be attributed to patient, surgeon, institution, or socioeconomic-related factors and the impact of PSM on death among localized prostate cancer patients.

METHODS

This study aimed to assess the independent and relative contribution of the patient, surgeon, institution and area-level risk factors on geographic and temporal variation of PSM and evaluate the impact of PSM on five-year all-cause and prostate cancer-specific mortality among localized prostate cancer patients. Within the hierarchical-related regression approach, we utilised Bayesian spatial-temporal multi-level models to study individual and area-level predictors with the outcomes, while accounting for geographically structured and unstructured correlation and non-linear trends.

RESULTS

Individual-level data included 10,075 localized prostate cancer cases with RP reported to the Prostate Cancer Outcomes Registry Victoria between 2009 and 2018. Area-level data comprised socio-economic disadvantage and remoteness data at the local government area level in Victoria, Australia. 26 % of patients had PSM, and the rates varied across areas by years. This variation was mainly associated with NCCN risk, followed by RP techniques, surgical institution type, surgeon volume and socio-economic disadvantage. Intermediate (Odds ratio/OR = 1.21,95 % credible interval/Crl = 1.05-1.41), high/very-high risk groups (OR = 2.24,95 % Crl = 1.91-2.64) and public surgical institution (OR = 1.64, 95 % Crl = 1.46-1.84) were independently associated with a higher likelihood of PSM. Robot-assisted (OR = 0.61, 95 % Crl = 0.55-0.68), laparoscopic RP (OR = 0.76, 95 % Crl = 0.62-0.93), high-volume surgeon (OR = 0.84, 95 % Crl = 0.76-0.93) and socio-economically least disadvantaged status (OR = 0.78, 95 % Crl = 0.64-0.94) showed a lower likelihood of PSM. PSM was also independently associated with a higher five-year all-cause and prostate cancer-specific mortality.

CONCLUSION

Aggressive tumour characteristics and RP techniques were the main contributors to the likelihood of PSM following RP. Reducing the prevalence of PSM will generally improve prostate cancer-specific and all-cause mortality.

Influence of timeliness and receipt of first treatment on geographic variation in non-small cell lung cancer mortality

Mortality from non-small cell lung cancer (NSCLC) exhibits substantial geographical disparities. However, there is little evidence on whether this variation could be attributed to patients' clinical characteristics and/or socioeconomic inequalities. This study evaluated the independent and relative contribution of the individual- and area-level risk factors on geographic variation in 2-year all-cause mortality among NSCLC patients. In the Hierarchical-related regression approach, we used the Bayesian spatial multilevel logistic regression model to combine individual- and area-level predictors with outcomes while accounting for geographically structured and unstructured correlation. Individual-level data included 3330 NSCLC cases reported to the Victoria Lung Cancer Registry between 2011 and 2016. Area-level data comprised socioeconomic disadvantage, remoteness and pollution data at the postal area level in Victoria, Australia. With the inclusion of significant individual- and area-level risk factors, timely (≤14 days) first definitive treatment (odds ratio [OR] = 0.73, 95% credible interval [Crl] = 0.56-0.94) and multidisciplinary meetings (MDM) (OR = 0.74, 95% Crl = 0.59-0.93) showed an independent association with a lower likelihood of NSCLC 2-year all-cause mortality. Timely and delayed (>14 days) first nondefinitive treatment, no treatment, advanced clinical stage, smoking, poor performance status, public hospital insurance and area-level deprivation were independently associated with a higher likelihood of 2- and 5-year all-cause mortality. NSCLC's 2-year all-cause mortality exhibited substantial geographic variation, mainly associated with timeliness and receipt of first definitive treatment, no treatment followed by patient prognostic factors with some contribution from area-level deprivation, MDM and public hospital insurance. This study highlights NSCLC patients should receive the first definitive treatment within the recommended 14-days from diagnosis.

Association between Receipt of Guideline-Concordant Lung Cancer Treatment and Individual- and Area-Level Factors: A Spatio-Temporal Analysis

BACKGROUND

Guideline-concordant treatment (GCT) of lung cancer has been observed to vary across geographic regions over the years. However, there is little evidence as to what extent this variation is explained by differences in patients' clinical characteristics versus contextual factors, including socioeconomic inequalities.

METHODS

This study evaluated the independent effects of individual- and area-level risk factors on geographic and temporal variation in receipt of GCT among patients with lung cancer. Receipt of GCT was defined on the basis of the National Comprehensive Cancer Network guidelines. We used Bayesian spatial-temporal multilevel models to combine individual and areal predictors and outcomes while accounting for geographically structured and unstructured correlation and linear and nonlinear trends.

RESULTS

Our study included 4,854 non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cases, reported to the Victorian Lung Cancer Registry between 2011 and 2018. Area-level data comprised socioeconomic disadvantage and remoteness data at the local government area level in Victoria, Australia. Around 60.36% of patients received GCT, and the rates varied across geographic areas over time. This variation was mainly associated with poor performance status, advanced clinical stages, NSCLC types, public hospital insurance, area-level deprivation, and comorbidities.

CONCLUSIONS

This study highlights the need to address disparities in receipt of GCT among patients with lung cancer with poor performance status, NSCLC, advanced clinical stage, stage I-III SCLC, stage III NSCLC, public hospital insurance, and comorbidities, and living in socioeconomically disadvantaged areas.

IMPACT

Two-year mortality outcomes significantly improved with GCT. Interventions aimed at reducing these inequalities could help to improve lung cancer outcomes.

Neighborhood Environment and Type 2 Diabetes Comorbidity in Serious Mental Illness

Aim: The aim of this study was to examine the association between neighborhood characteristics and type 2 diabetes (T2D) comorbidity in serious mental illness (SMI). We investigated associations of neighborhood-level crime, accessibility to health care services, availability of green spaces, neighborhood obesity, and fast food availability with SMI-T2D comorbidity. Method: A series of multilevel logistic regression models accounting for neighborhood-level clustering were used to examine the associations between 5 neighborhood variables and SMI-T2D comorbidity, sequentially adjusting for individual-level variables and neighborhood-level socioeconomic disadvantage. Results: Individuals with SMI residing in areas with higher crime rates per 1000 population had 2.5 times increased odds of reporting T2D comorbidity compared to the individuals with SMI residing in lower crime rate areas after controlling for individual and areal level factors (95% CI 0.91-6.74). There was no evidence of association between SMI-T2D comorbidity and other neighborhood variables investigated. Conclusion: Public health strategies to reduce SMI-T2D comorbidity might benefit by targeting on individuals with SMI living in high-crime neighborhoods. Future research incorporating longitudinal designs and/or mediation analysis are warranted to fully elucidate the mechanisms of association between neighborhoods and SMI-T2D comorbidity.

A Spatial Regression Analysis on the Effect of Neighborhood-Level Trust on Cooperative Behaviors: Comparison With a Multilevel Regression Analysis

There is no reason to suppose that neighborhood effects based on residents' trust vary according to administrative boundaries. We examined the relationship between neighborhood trust and cooperative behaviors using the spatial Durbin model which assumed that people are influenced by closer neighbors regardless of administrative boundaries, comparing the results with those of the multilevel model. We used data from 476 residents in Arakawa Ward, Tokyo, Japan. For each respondent, we assigned a unique 'neighborhood trust' value weighted by the inverse distance between the respondent and all other respondents as an independent variable. The dependent variables were perceived neighbors' cooperative behaviors and respondents' own cooperative behaviors. The spatial Durbin model showed that spatially weighted neighborhood trust was positively associated with cooperative behaviors. Meanwhile, the multilevel models did not show the statistically significant effect of neighborhood trust. We concluded that the spatial model might model the neighborhood effects in society more precisely.

Extending multilevel spatial models to include spatially varying coefficients

Multilevel models have long been used by health geographers working on questions of space, place, and health. Similarly, health geographers have pursued interests in determining whether or not the effect of an exposure on a health outcome varies spatially. However, relatively little work has sought to use multilevel models to explore spatial variability in the effects of a contextual exposure on a health outcome. Methodologically, extending multilevel models to allow intercepts and slopes to vary spatially is straightforward. The purpose of this paper, therefore, is to show how multilevel spatial models can be extended to include spatially varying covariate effects. We provide an empirical example on the effect of agriculture on malaria risk in children under 5 years of age in the Democratic Republic of Congo.

Residential environments and smoking behaviour patterns among young adults: A prospective study using data from the Interdisciplinary Study of Inequalities in Smoking cohort

Young adults have the highest prevalence of smoking among all age groups. Studies have shown associations between presence/density of tobacco retail and presence of smoker accommodation and smoking prevalence. However, little is known about their potential to influence different smoking patterns including initiation, maintenance, or cessation. This is important because smoking behaviour patterns in young adults may be subject to ongoing changes. Moreover, smoking pattern determinants may be different to those of smoking prevalence, and feature-pattern associations may be scale-dependent, requiring the consideration of different analytical spatial units. We examined associations between prospectively-measured smoking behaviour patterns and presence/density of tobacco retail, and presence of smoker accommodation facilities across 2 nested spatial units in Montreal, Canada. Data were from 18 to 25 year-old Montreal residents who had participated in the Interdisciplinary Study of Inequalities in Smoking cohort both at baseline in 2011-2012 and follow-up in 2014 and resided in the same area at follow-up. 2-year smoking behaviour patterns were assessed for 2 cohorts based on participants' smoking status at baseline. Associations were examined using multilevel logistic models. Young adults who were smokers at baseline residing in areas with higher local-level presence of tobacco retail were less likely to quit smoking (i.e.: to be non-smokers for fewer than 2 years). Higher presence of smoker accommodation was not associated with smoking patterns at any scale. Findings provide evidence of scale-specific associations between residential environment features and smoking behaviour patterns in young adults, which may point to specific exposure-outcome processes underlying these associations.

The Relative Association of Collective Efficacy in School and Neighborhood Contexts With Adolescent Alcohol Use

Background

It is unclear whether either neighborhood collective efficacy or school collective efficacy is associated with adolescent alcohol use. This study aimed to examine the relative contributions of collective efficacy, both in school and in the neighborhood contexts, to alcohol use among Japanese adolescents.

Methods

A cross-sectional study was conducted in public high schools across Okinawa and Ibaraki Prefectures in Japan in 2016. The study participants consisted of 3,291 students in grades 10 through 12 cross-nested in 51 schools and 107 neighborhoods. Alcohol use was measured as current alcohol drinking, which was defined as self-reported drinking on at least 1 day in the past 30 days. Collective efficacy was measured using scales of social cohesion and informal social control in school and the neighborhood. Contextual-level collective efficacy was measured using aggregated school-level and neighborhood-level individual responses, respectively. We used non-hierarchical multilevel models to fit the cross-nested data.

Results

Significant variation in alcohol use was shown between schools but not between neighborhoods. After adjusting for covariates, school collective efficacy at individual- and contextual-levels was protectively associated with alcohol drinking (odds ratio [OR] for the increase of one standard deviation from the mean 0.72; 95% confidence interval [CI], 0.63–0.82 and OR 0.61; 95% CI, 0.49–0.75, respectively), whereas neighborhood collective efficacy at individual- and contextual-levels was not associated with alcohol consumption.

Conclusion

The school-level associations of collective efficacy with adolescent alcohol use may have the greater impact than the neighborhood-level associations. Adolescent drinking prevention efforts should include enhancing school collective efficacy.

Non-completion of secondary education and early disability in Norway: geographic patterns, individual and community risks

Background

School non-completion and early work disability is a great public health challenge in Norway, as in most western countries. This study aims to investigate how medically based disability pension (DP) among young adults varies geographically and how municipal socioeconomic conditions interact with non-completion of secondary education in determining DP risk.

Methods

The study includes a nationally representative sample of 30% of all Norwegians (N = 350,699) aged 21–40 in 2010 from Statistic Norway’s population registries. Multilevel models incorporating factors at the individual, neighbourhood and municipal levels were applied to estimate the neighbourhood and municipality general contextual effects in DP receipt, and detect possible differences in the impact of municipal socioeconomic conditions on DP risk between completers and non-completers of secondary education.

Results

A pattern of spatial clustering at the neighbourhood (ICC = 0.124) and municipality (ICC = 0.021) levels are clearly evident, indicating that the underlying causes of DP receipt have a systematic neighbourhood and municipality variation in Norway. Non-completion of secondary education is strongly correlated with DP receipt among those younger than 40. Socioeconomic characteristics of the municipality are also significantly correlated with DP risk, but these associations are conditioned by the completion of secondary education. Living in a socioeconomically advantageous municipality (i.e. high income, high education levels and low unemployment and social security payment rates) is associated with a higher risk of DP, but only among those who do not complete their secondary education. Although the proportion of DPs was equal in rural and urban areas, it is evident that young people living in urban settings are more at risk of early DP than their counterparts living in rural parts of the country when controlling for other risk factors.

Conclusion

The association between school non-completion and DP risk varies between municipalities and local socioeconomic environments. The interplay between personal characteristics and the local community is important in DP risk among young adults, implying that preventive measures should be directed not only at the individual level, but also include the educational system and the local community.

The Measurement of Ethnic Segregation in the Netherlands: Differences Between Administrative and Individualized Neighbourhoods

The debate on residential segregation often focuses on the concentration of migrant groups in specific neighbourhoods and its presumed effects on, e.g. personal life chances and social inclusion. However, cross-regional and international comparisons of segregation are hampered by differences in the size and delineation of the spatial units that are used for its measurement: the Modifiable Areal Unit Problem. This paper therefore measures segregation for scalable ‘individualized neighbourhoods’, defined by a predefined number of closest neighbours instead of by administrative or statistical boundaries. This approach allows for measuring segregation levels and patterns across different spatial scales, ranging from the micro-scale (50 neighbours) to larger spatial areas (51,200 neighbours). Using population register data from the Netherlands, we study the segregation of four different migrant origin groups across individualized neighbourhoods at eleven spatial scales. Outcomes are compared to those found using administrative neighbourhoods. We are especially interested in how levels and patterns of segregation change with an increase in scale level. Our findings indicate that segregation levels and patterns are different across various spatial scales, and the most relevant spatial scale is also group-specific. Measuring segregation while using scalable individualized neighbourhoods seems an appropriate way to deal with both the multiscalar nature of segregation and the large within-district variety associated with it.

Multilevel regression modelling to investigate variation in disease prevalence across locations

In this article, we show how to investigate the role of individual (personal) risk factors in outcome prevalence in multicentre studies with multilevel modelling. The variation in outcome prevalence is modelled by introducing a random intercept. In the next step, the empty model is compared with the model containing the risk factor(s). Because the outcome is dichotomous, this comparison can only be carried out after having rescaled the models' parameter values to the variance of an underlying continuous variable. We illustrate this approach with data from Phase Two of the International Study of Asthma and Allergies in Childhood (ISAAC) and provide a corresponding Stata do-file.

The Spatial Scale and Spatial Configuration of Residential Settlement: Measuring Segregation in the Postbellum South

Studies of residential segregation typically focus on its degree without questioning its scale and configuration. We study Southern cities in 1880 to emphasize the salience of these spatial dimensions. Distance-based and sequence indices can reflect spatial patterns but with some limitations, while geocoded 100% population data make possible more informative measures. One improvement is flexibility in spatial scale, ranging from adjacent buildings to whole districts of the city. Another is the ability to map patterns in fine detail. In Southern cities we find qualitatively distinct configurations that include not only black "neighborhoods" as usually imagined, but also backyard housing, alley housing, and side streets that were predominantly black. These configurations represent the sort of symbolic boundaries recognized by urban ethnographers. By mapping residential configurations and interpreting them in light of historical accounts, our intention is to capture meanings that are too often missed by quantitative studies of segregation.

Model-based and design-based inference goals frame how to account for neighborhood clustering in studies of health in overlapping context types

Accounting for non-independence in health research often warrants attention. Particularly, the availability of geographic information systems data has increased the ease with which studies can add measures of the local "neighborhood" even if participant recruitment was through other contexts, such as schools or clinics. We highlight a tension between two perspectives that is often present, but particularly salient when more than one type of potentially health-relevant context is indexed (e.g., both neighborhood and school). On the one hand, a model-based perspective emphasizes the processes producing outcome variation, and observed data are used to make inference about that process. On the other hand, a design-based perspective emphasizes inference to a well-defined finite population, and is commonly invoked by those using complex survey samples or those with responsibility for the health of local residents. These two perspectives have divergent implications when deciding whether clustering must be accounted for analytically and how to select among candidate cluster definitions, though the perspectives are by no means monolithic. There are tensions within each perspective as well as between perspectives. We aim to provide insight into these perspectives and their implications for population health researchers. We focus on the crucial step of deciding which cluster definition or definitions to use at the analysis stage, as this has consequences for all subsequent analytic and interpretational challenges with potentially clustered data.

Disparities in pharmacotherapy for alcohol use disorder in the context of universal health care: a Swedish register study

BACKGROUND AND AIMS

Pharmacotherapy can be an important part of the continuum of care for alcohol use disorder (AUD). The Swedish universal health-care system emphasizes provision of care to marginalized groups. The primary aim was to test associations of neighborhood deprivation and disadvantaged social status with receipt of AUD pharmacotherapy in this context.

DESIGN

Data from linked population registers were used to follow an open cohort over 7 years.

SETTING

Sweden.

PARTICIPANTS

Alcohol-related ICD-10 codes reported for all hospitalizations in the Swedish Hospital Discharge Register and all clinic/office visits in the Outpatient Care Register between 2005 and 2012 were used to identify 62 549 cases with AUD.

MEASUREMENTS

The primary outcome was any AUD pharmacotherapy (naltrexone, disulfiram, acamprosate, nalmefene) picked up by patients between 2005 and 2012 (versus none), based on the Swedish Prescribed Drug Register. Neighborhood deprivation was defined using aggregated data from the Total Population Register; indicators of disadvantaged social status (income, education, country of origin) also came from this source.

FINDINGS

Approximately half the cases (53.7%) picked up one or more AUD pharmacotherapy prescriptions. In adjusted models, people living in neighborhoods with moderate [odds ratio (OR) = 0.90, 95% confidence interval (CI) = 0.86, 0.95] or high levels of deprivation (OR = 0.75, 95% CI = 0.70, 0.79) compared with low deprivation, those with lower incomes (for example, lowest quartile: OR = 0.70, 95% CI = 0.66, 0.73 compared with highest) and less education (for example, < 10 years: OR = 0.82, 95% CI = 0.78, 0.85 compared with 12+ years) and people born outside Sweden (OR = 0.74, 95% CI = 0.71, 0.78 compared with Swedish-born) were significantly less likely to pick up a prescription for AUD pharmacotherapy during the study period.

CONCLUSIONS

There appear to be socio-economic disparities in the receipt of pharmacotherapy for alcohol use disorder in Sweden.

Statistical Methods to Study Variation in Associations Between Food Store Availability and Body Mass in the Multi-Ethnic Study of Atherosclerosis

Research linking characteristics of the neighborhood environment to health has relied on traditional regression methods where prespecified distances from participant's locations or areas are used to operationalize neighborhood-level measures. Because the relevant spatial scale of neighborhood environment measures may differ across places or individuals, using prespecified distances could result in biased association estimates or efficiency losses. We use novel hierarchical distributed lag models and data from the Multi-Ethnic Study of Atherosclerosis (MESA) to (1) examine whether and how the association between the availability of favorable food stores and body mass index (BMI) depends on continuous distance from participant locations (instead of traditional buffers), thus allowing us to indirectly infer the spatial scale at which this association operates; (2) examine if the spatial scale and magnitude of the association differs across six MESA sites, and (3) across individuals. As expected, we found that the association between higher availability of favorable food stores within closer distances from participant's residential location was stronger than at farther distances, and that the magnitude of the adjusted association declined quickly from zero to two miles. Furthermore, between-individual heterogeneity in the scale and magnitude of the association was present; the extent of this heterogeneity was different across the MESA sites. Individual heterogeneity was partially explained by sex. This study illustrated novel methods to examine how neighborhood environmental factors may be differentially associated with health at different scales, providing nuance to previous research that ignored the heterogeneity found across individuals and contexts.

The Long Arm of Poverty: Extended and Relational Geographies of Child Victimization and Neighborhood Violence Exposures

Current models of neighborhood effects on victimization predominantly assume that residential neighborhoods function independent of their surroundings. Yet, a surprising proportion of violence occurs outside of victims' residential neighborhoods. The current study extends on recent advances in spatial dynamics and neighborhood effects to explore the importance of different geographic scales and relational exposures to poverty for child violent victimization. We examine longitudinal data on over 4,400 low-income children from high poverty neighborhoods in five cities, who participated in the Moving to Opportunity randomized intervention. The results suggest that surrounding poverty matters for child victimization beyond the effect of residential poverty. Moreover, moving farther from extreme poverty also seems to buffer against victimization and to amplify the benefits of moving to improved extended (residential and surrounding) neighborhoods. All children in the study, but especially boys older than 10 years of age, seemed to be affected by the long arm of poverty.

Associations of place characteristics with HIV and HCV risk behaviors among racial/ethnic groups of people who inject drugs in the United States

PURPOSE

Investigate whether characteristics of geographic areas are associated with condomless sex and injection-related risk behavior among racial/ethnic groups of people who inject drugs (PWID) in the United States.

METHODS

PWID were recruited from 19 metropolitan statistical areas for 2009 National HIV Behavioral Surveillance. Administrative data described ZIP codes, counties, and metropolitan statistical areas where PWID lived. Multilevel models, stratified by racial/ethnic groups, were used to assess relationships of place-based characteristics to condomless sex and injection-related risk behavior (sharing injection equipment).

RESULTS

Among black PWID, living in the South (vs. Northeast) was associated with injection-related risk behavior (adjusted odds ratio [AOR] = 2.24, 95% confidence interval [CI] = 1.21-4.17; P = .011), and living in counties with higher percentages of unaffordable rental housing was associated with condomless sex (AOR = 1.02, 95% CI = 1.00-1.04; P = .046). Among white PWID, living in ZIP codes with greater access to drug treatment was negatively associated with condomless sex (AOR = 0.93, 95% CI = 0.88-1.00; P = .038).

CONCLUSIONS

Policies that increase access to affordable housing and drug treatment may make environments more conducive to safe sexual behaviors among black and white PWID. Future research designed to longitudinally explore the association between residence in the south and injection-related risk behavior might identify specific place-based features that sustain patterns of injection-related risk behavior.

Social capital and refraining from medical care among elderly people in Japan

BACKGROUND

Refraining from required medical care can worsen health, particularly for the elderly, and increase public medical expenditure, which destabilizes the financial aspect of social security. Social capital, such as trust between residents and the norms of reciprocity in the community, is a possible measure to prevent refraining from medical care.

METHODS

We studied survey data collected in a small area in Japan that included a high response rate (91.6 %) to evaluate refraining from medical care. Self-reported refraining from required medical care from among 1016 elderly people, aged ≥60 (male = 490; female = 526), was used as a dependent variable. Social capital indicators were mean values of people's attitude toward the generalized trust and norms of reciprocity in each community. We estimated the association between community level social capital and individuals' probability of refraining from medical care while controlling individual factors such as age, education, and marital status.

RESULTS

Logit estimation results showed that only generalized trust is associated with low probability of refraining from medical care among the elderly in small communities. The marginal effect for 0.1 increase in community level trust is 4 % decrease in the probability of refraining from medical care. In larger communities, generalized trust is not associated with the probability of refraining from medical care.

CONCLUSIONS

This finding suggests that the generalized trust is effective in smaller communities as far as related to access to medical care. In small communities, policy to increase generalized trust to support medical care for elderly is recommended.

Moving to opportunity and mental health: Exploring the spatial context of neighborhood effects

Studies of housing mobility and neighborhood effects on health often treat neighborhoods as if they were isolated islands. This paper argues that conceptualizing neighborhoods as part of the wider spatial context within which they are embedded may be key in advancing our understanding of the role of local context in the life of urban dwellers. Analyses are based on mental health and neighborhood context measurements taken on over 3000 low-income families who participated in the Moving to Opportunity for Fair Housing Demonstration Program (MTO), a large field experiment in five major U.S. cities. Results from analyses of two survey waves combined with Census data at different geographic scales indicate that assignment to MTO's experimental condition of neighborhood poverty <10% significantly decreased average exposure to immediate and surrounding neighborhood disadvantage by 97% and 59% of a standard deviation, respectively, relative to the control group. Escaping concentrated disadvantage in either the immediate neighborhood or the surrounding neighborhood, but not both, was insufficient to make a difference for mental health. Instead, the results suggest that improving both the immediate and surrounding neighborhoods significantly benefits mental health. Compared to remaining in concentrated disadvantage in the immediate and surrounding neighborhoods, escaping concentrated disadvantage in both the immediate and surrounding neighborhoods (on average over the study duration) as a result of the intervention predicts an increase of 25% of a standard deviation in the composite mental health scores.

Geographic Clustering of Cardiometabolic Risk Factors in Metropolitan Centres in France and Australia

Understanding how health outcomes are spatially distributed represents a first step in investigating the scale and nature of environmental influences on health and has important implications for statistical power and analytic efficiency. Using Australian and French cohort data, this study aimed to describe and compare the extent of geographic variation, and the implications for analytic efficiency, across geographic units, countries and a range of cardiometabolic parameters (Body Mass Index (BMI) waist circumference, blood pressure, resting heart rate, triglycerides, cholesterol, glucose, HbA_1c). Geographic clustering was assessed using Intra-Class Correlation (ICC) coefficients in biomedical cohorts from Adelaide (Australia, n = 3893) and Paris (France, n = 6430) for eight geographic administrative units. The median ICC was 0.01 suggesting 1% of risk factor variance attributable to variation between geographic units. Clustering differed by cardiometabolic parameters, administrative units and countries and was greatest for BMI and resting heart rate in the French sample, HbA_1c in the Australian sample, and for smaller geographic units. Analytic inefficiency due to clustering was greatest for geographic units in which participants were nested in fewer, larger geographic units. Differences observed in geographic clustering across risk factors have implications for choice of geographic unit in sampling and analysis, and highlight potential cross-country differences in the distribution, or role, of environmental features related to cardiometabolic health.

The impact of area residential property values on self-rated health: A cross-sectional comparative study of Seattle and Paris

This study analyzed the impact of area residential property values, an objective measure of socioeconomic status (SES), on self-rated health (SRH) in Seattle, Washington and Paris, France. This study brings forth a valuable comparison of SRH between cities that have contrasting urban forms, population compositions, residential segregation, food systems and transportation modes. The SOS (Seattle Obesity Study) was based on a representative sample of 1394 adult residents of Seattle and King County in the United States. The RECORD Study (Residential Environment and Coronary Heart Disease) was based on 7131 adult residents of Paris and its suburbs in France. Socio-demographics, SRH and body weights were obtained from telephone surveys (SOS) and in-person interviews (RECORD). All home addresses were geocoded using ArcGIS 9.3.1 (ESRI, Redlands, CA). Residential property values were obtained from tax records (Seattle) and from real estate sales (Paris). Binary logistic regression models were used to test the associations among demographic and SES variables and SRH. Higher area property values significantly associated with better SRH, adjusting for age, gender, individual education, incomes, and BMI. The associations were significant for both cities. A one-unit increase in body mass index (BMI) was more detrimental to SRH in Seattle than in Paris. In both cities, higher area residential property values were related to a significantly lower obesity risk and better SRH. Ranked residential property values can be useful for health and weight studies, including those involving social inequalities and cross-country comparisons.

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We studied the impact of area property values on health in Seattle and Paris.

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Higher area property values associated with better SRH in both cities

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Ranked area property values can be useful for health and weight studies.

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BMI was more detrimental to SRH in Seattle than in Paris.

Latino residential segregation and self-rated health among Latinos: Washington State Behavioral Risk Factor Surveillance System, 2012-2014

The relationship between Latino residential segregation and self-rated health (SRH) is unclear, but might be partially affected by social capital. We investigated the association between Latino residential segregation and SRH while also examining the roles of various social capital measures. Washington State Behavioral Risk Factor Surveillance System (2012-2014) and U.S. Census data were linked by zip code and zip code tabulation area. Multilevel logistic regression models were used to estimate odds of good or better SRH by Latino residential segregation, measured by the Gini coefficient, and controlling for sociodemographic, acculturation and social capital measures of neighborhood ties, collective socialization of children, and social control. The Latino residential segregation - SRH relationship was convex, or 'U'-shaped, such that increases in segregation among Latinos residing in lower segregation areas was associated with lower SRH while increases in segregation among Latinos residing in higher segregation areas was associated with higher SRH. The social capital measures were independently associated with SRH but had little effect on the relationship between Latino residential segregation and SRH. A convex relationship between Latino residential segregation and SRH could explain mixed findings of previous studies. Although important for SRH, social capital measures of neighborhood ties, collective socialization of children, and social control might not account for the relationship between Latino residential segregation and SRH.

Geographic Variation in Maternal Smoking during Pregnancy in the Missouri Adolescent Female Twin Study (MOAFTS)

Objective

Despite well-known adverse health effects of maternal smoking during pregnancy (MSP), it is still unclear if MSP varies geographically and if neighborhood socioeconomic deprivation (SED) plays an important role in MSP. This study aims to investigate small-area geographic variation in MSP and examine the association of SED with MSP.

Methods

The Missouri Adolescent Female Twin Study (MOAFTS) is a cohort study of female like-sex twins born in Missouri to Missouri-resident parents during 1975–1985. Biological mothers completed a baseline interview in 1995–1998 and reported MSP with the twins. Residential address of the mother at birth was geocoded. We developed a census tract-level SED index using a common factor approach based on 21 area-level socioeconomic variables from the 1980 Census data. Multilevel logistic regressions estimated geographic heterogeneity (random effect) in MSP and the odds ratios (ORs, fixed effects) of neighborhood SED associated with MSP.

Results

Of 1658 MOAFTS mothers, 35.2% reported any MSP and 21.9% reported MSP beyond the first trimester. Neighborhood SED was associated with any MSP (the highest vs. the lowest quartile: OR = 1.90, 95% confidence interval [CI] = 1.40–2.57, P_trend<0.001) and MSP beyond the first trimester (OR = 1.98, 95% CI = 1.38–2.85, P_trend = 0.002) in unadjusted analyses. After adjusting for individual covariates (demographics, socioeconomic conditions, alcohol use, and parents’ cohabitation), neighborhood SED was not associated with MSP, but geographic variation still persisted in MSP (variance = 0.41, P = 0.003) and in MSP beyond the first trimester (variance = 0.82, P<0.001).

Conclusions

Neighborhood SED was associated with MSP in unadjusted analyses but this association could be explained by individual socioeconomic conditions. Nonetheless, significant geographic variation in MSP persisted and was not accounted for by differences in neighborhood SED. To develop effective interventions to reduce MSP, further studies are necessary to explore underlying reasons for its geographic variation.

Multi-scalar residential context and recovery from illness: An analysis using Swedish register data

The aim of this paper is to analyse if recovery from ill-health is influenced by geographical context using a multi-scalar approach to context measurement and Swedish longitudinal register-based data on sickness benefit recipiency as an indicator of onset of and recovery from illness. Our sample consists of individuals that have stayed healthy and in work for a three-year period (2000-2002) and then falls ill during the fourth year (2003), some of who recover to good health in the fifth year (2004). The results show that in areas with above-average percentages of people receiving sickness-benefit there is a reduced probability of recovery. In contrast, high levels of employment in the neighbourhood and in the local area have a positive effect on the chances of recovery. These contextual effects are statistically significant but relatively weak in comparison to the influence of individual level factors such as age, sex, marital status, and income. Our conclusion is that individualised scalable neighbourhoods constitute a potentially valuable addition to the toolbox used in neighbourhood effect studies.

Measuring physical neighborhood quality related to health

Although sociodemographic factors are one aspect of understanding the effects of neighborhood environments on health, equating neighborhood quality with socioeconomic status ignores the important role of physical neighborhood attributes. Prior work on neighborhood environments and health has relied primarily on level of socioeconomic disadvantage as the indicator of neighborhood quality without attention to physical neighborhood quality. A small but increasing number of studies have assessed neighborhood physical characteristics. Findings generally indicate that there is an association between living in deprived neighborhoods and poor health outcomes, but rigorous evidence linking specific physical neighborhood attributes to particular health outcomes is lacking. This paper discusses the methodological challenges and limitations of measuring physical neighborhood environments relevant to health and concludes with proposed directions for future work.

Statistical Significance of Geographic Heterogeneity Measures In Spatial Epidemiologic Studies

Assessing geographic variations in health events is one of the major tasks in spatial epidemiologic studies. Geographic variation in a health event can be estimated using the neighborhood-level variance that is derived from a generalized mixed linear model or a Bayesian spatial hierarchical model. Two novel heterogeneity measures, including median odds ratio and interquartile odds ratio, have been developed to quantify the magnitude of geographic variations and facilitate the data interpretation. However, the statistical significance of geographic heterogeneity measures was inaccurately estimated in previous epidemiologic studies that reported two-sided 95% confidence intervals based on standard error of the variance or 95% credible intervals with a range from 2.5^th to 97.5^th percentiles of the Bayesian posterior distribution. Given the mathematical algorithms of heterogeneity measures, the statistical significance of geographic variation should be evaluated using a one-tailed P value. Therefore, previous studies using two-tailed 95% confidence intervals based on a standard error of the variance may have underestimated the geographic variation in events of their interest and those using 95% Bayesian credible intervals may need to re-evaluate the geographic variation of their study outcomes.

Investigating 'place effects' on mental health: implications for population-based studies in psychiatry

Background. Interest in features of our local environments that may promote better mental health and wellbeing continues to rise among decision makers. Our purpose was to highlight a selection of these challenges and some promising avenues for enhancing the quality of evidence. Method. An analysis of approximately 267, 000 people was used to test the local relative deprivation hypothesis, wherein the shortfall of a person's socioeconomic circumstances from their neighbours is said to impact negatively upon mental health. This case was used to anchor further discussion of challenges to identifying and interpreting genuine 'place effects' from spurious correlations. Results. A Median Odds Ratio of 1.29 computed via multilevel logistic regression showed that the odds of experiencing psychological distress (as measured by the Kessler score) varied by geographical area. Approximately 67% of this was attributed to a cross-classified measure of household income and neighbourhood deprivation. Compared to people on high incomes living in affluent neighbourhoods, the odds ratio of psychological distress for people on low incomes in affluent areas was 4.73 (95% confidence interval (95% CI) 4.39, 5.09), whereas that for people on low incomes in deprived areas was significantly higher at 5.83 (95% CI 5.41, 6.28). Conclusions. While no evidence was found to support local relative deprivation hypothesis, the pattern suggests that more affluent areas may contain features that are conducive to better mental health. Selection of bespoke geographical boundaries, use of directed acyclic graphs and more evaluations of natural experiments are likely to be important in taking the field of enquiry onwards.

Spatial clustering of physical activity and obesity in relation to built environment factors among older women in three U.S. states

BACKGROUND

Identifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study's aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters.

METHODS

In 2004, Nurses' Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared.

RESULTS

Seven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband's education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters.

CONCLUSIONS

Spatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity.

A conceptual framework for understanding the association between school bullying victimization and substance misuse

This article reviews current research findings and presents a conceptual framework for better understanding the relationship between bullying victimization (hereafter referred to as victimization) and substance misuse (hereafter referred to as SM) among adolescents. Although victimization and SM may appear to be separate problems, research suggests an intriguing relationship between the 2. We present a brief, empirical overview of the direct association between victimization and adolescent SM, followed by a proposed conceptual framework that includes co-occurring risk factors for victimization and SM within family, peer, and school and community contexts. Next, we discuss potential mediators linking victimization and SM, such as internalizing problems, traumatic stress, low academic performance, and school truancy and absence. We then identify potential moderating influences of age, gender and sex, social supports, and school connectedness that could amplify or abate the association between victimization and SM. Finally, we discuss practice and policy implications.

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study

BACKGROUND

Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival.

METHODS

Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20-84 years diagnosed during 1997-2007 from Queensland, Australia.

RESULTS

Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients.

CONCLUSIONS

With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings.

Integrating space with place in health research: a multilevel spatial investigation using child mortality in 1880 Newark, New Jersey

Research on neighborhoods and health increasingly acknowledges the need to conceptualize, measure, and model spatial features of social and physical environments. When ignoring underlying spatial dynamics, we run the risk of biased statistical inference and misleading results. In this article, we propose an integrated multilevel spatial approach for Poisson models of discrete responses. In an empirical example of child mortality in 1880 Newark, New Jersey, we compare this multilevel spatial approach with the more typical aspatial multilevel approach. Results indicate that spatially defined egocentric neighborhoods, or distance-based measures, outperform administrative areal units, such as census units. In addition, although results do not vary by specific definitions of egocentric neighborhoods, they are sensitive to geographic scale and modeling strategy. Overall, our findings confirm that adopting a spatial multilevel approach enhances our ability to disentangle the effect of space from that of place, pointing to the need for more careful spatial thinking in population research on neighborhoods and health.

CREATING MEASURES OF THEORETICALLY RELEVANT NEIGHBORHOOD ATTRIBUTES AT MULTIPLE SPATIAL SCALES

Accurately measuring attributes in neighborhood environments allows researchers to study the influence of neighborhoods on individual-level outcomes. Researchers working to improve the measurement of neighborhood attributes generally advocate doing so in one of two ways: improving the theoretical relevance of measures and correctly defining the appropriate spatial scale. The data required by the first, "ecometric" neighborhood assessments on a sample of neighborhoods, are generally incompatible with the methods of the second, which tend to rely on population data. In this article, the authors describe how ecometric measures of theoretically relevant attributes observed on a sample of city blocks can be combined with a geostatistical method known as kriging to develop city block-level estimates across a city that can be configured to multiple neighborhood definitions. Using a cross-validation study with data from a 2002 systematic social observation of physical disorder on 1,663 city blocks in Chicago, the authors show that this method creates valid results. They then demonstrate, using neighborhood measures aggregated to three different spatial scales, that residents' perceptions of both fear and neighborhood disorder vary substantially across different spatial scales.

Socio-ecological risk factors for prime-age adult death in two coastal areas of Vietnam

BACKGROUND

Hierarchical spatial models enable the geographic and ecological analysis of health data thereby providing useful information for designing effective health interventions. In this study, we used a Bayesian hierarchical spatial model to evaluate mortality data in Vietnam. The model enabled identification of socio-ecological risk factors and generation of risk maps to better understand the causes and geographic implications of prime-age (15 to less than 45 years) adult death.

METHODS AND FINDINGS

The study was conducted in two sites: Nha Trang and Hue in Vietnam. The study areas were split into 500×500 meter cells to define neighborhoods. We first extracted socio-demographic data from population databases of the two sites, and then aggregated the data by neighborhood. We used spatial hierarchical model that borrows strength from neighbors for evaluating risk factors and for creating spatially smoothed risk map after adjusting for neighborhood level covariates. The Markov chain Monte Carlo procedure was used to estimate the parameters. Male mortality was more than twice the female mortality. The rates also varied by age and sex. The most frequent cause of mortality was traffic accidents and drowning for men and traffic accidents and suicide for women. Lower education of household heads in the neighborhood was an important risk factor for increased mortality. The mortality was highly variable in space and the socio-ecological risk factors are sensitive to study site and sex.

CONCLUSION

Our study suggests that lower education of the household head is an important predictor for prime age adult mortality. Variability in socio-ecological risk factors and in risk areas by sex make it challenging to design appropriate intervention strategies aimed at decreasing prime-age adult deaths in Vietnam.

The environmental correlates of overall and neighborhood based recreational walking (a cross-sectional analysis of the RECORD Study)

Background

Preliminary evidence suggests that recreational walking has different environmental determinants than utilitarian walking. However, previous studies are limited in their assessment of environmental exposures and recreational walking and in the applied modeling strategies. Accounting for individual sociodemographic profiles and weather over the walking assessment period, the study examined whether numerous street network-based neighborhood characteristics related to the sociodemographic, physical, service, social-interactional, and symbolic environments were associated with overall recreational walking and recreational walking in one’s residential neighborhood and could explain their spatial distribution.

Methods

Based on the RECORD Cohort Study (Paris region, France, n = 7105, 2007–2008 data), multilevel-spatial regression analyses were conducted to investigate environmental factors associated with recreational walking (evaluated by questionnaire at baseline). A risk score approach was applied to quantify the overall disparities in recreational walking that were predicted by the environmental determinants.

Results

Sixty-nine percent of the participants reported recreational walking over the past 7 days. Their mean reported recreational walking time was 3h31mn. After individual-level adjustment, a higher neighborhood education, a higher density of destinations, green and open spaces of quality, and the absence of exposure to air traffic were associated with higher odds of recreational walking and/or a higher recreational walking time in one’s residential neighborhood. As the overall disparities that were predicted by these environmental factors, the odds of reporting recreational walking and the odds of a higher recreational walking time in one’s neighborhood were, respectively, 1.59 [95% confidence interval (CI): 1.56, 1.62] times and 1.81 (95% CI: 1.73, 1.87) times higher in the most vs. the least supportive environments (based on the quartiles).

Conclusions

Providing green/open spaces of quality, building communities with services accessible from the residence, and addressing environmental nuisances such as those related to air traffic may foster recreational walking in one’s environment.

Physical activity-friendly neighbourhood among older adults from a medium size urban setting in Southern Europe

OBJECTIVE

In this cross-sectional study, we examined the relationship between socio-environmental characteristics of neighborhood of residence and the frequency of leisure-time physical activity (LTPA) among older adults from Porto (Portugal).

METHOD

Data from EpiPorto - a prospective adult cohort study from Porto (Portugal) - were used. Only adults aged ≥ 65 at baseline (1999-2003) were included (n=580). We used a Geographic Information System to objectively measure the neighborhood characteristics and Generalized Additive Models to estimate their effect on participation in LTPA (none vs. some reported) and frequency of LTPA (min/day).

RESULTS

62% of the participants reported no LTPA. Active elderly spent on average 38 (women) and 67 (men) minutes per day exercising. Neighborhood characteristics were unrelated to whether older people exercised or not. However, among active individuals, distance to the nearest destination (β=-0.154, p=0.016), in women, and distance to the nearest park, in men (-0.030, 0.050), were predictors of LTPA frequency.

CONCLUSION

There was almost no association between neighborhood characteristics and whether older adults engaged in LTPA or not, but among those that did engage, neighborhood characteristics were associated with increased frequency of LTPA. The promotion of well distributed destinations and parks might improve physical activity levels among the elderly.

Air pollution and individual and neighborhood socioeconomic status: evidence from the Multi-Ethnic Study of Atherosclerosis (MESA)

BACKGROUND

Although research has shown that low socioeconomic status (SES) and minority communities have higher exposure to air pollution, few studies have simultaneously investigated the associations of individual and neighborhood SES with pollutants across multiple sites.

OBJECTIVES

We characterized the distribution of ambient air pollution by both individual and neighborhood SES using spatial regression methods.

METHODS

The study population comprised 6,140 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Year 2000 annual average ambient PM2.5 and NOx concentrations were calculated for each study participant's home address at baseline examination. We investigated individual and neighborhood (2000 U.S. Census tract level) SES measures corresponding to the domains of income, wealth, education, and occupation. We used a spatial intrinsic conditional autoregressive model for multivariable analysis and examined pooled and metropolitan area-specific models.

RESULTS

A 1-unit increase in the z-score for family income was associated with 0.03-μg/m3 lower PM2.5 (95% CI: -0.05, -0.01) and 0.93% lower NOx (95% CI: -1.33, -0.53) after adjustment for covariates. A 1-SD-unit increase in the neighborhood's percentage of persons with at least a high school degree was associated with 0.47-μg/m3 lower mean PM2.5 (95% CI: -0.55, -0.40) and 9.61% lower NOx (95% CI: -10.85, -8.37). Metropolitan area-specific results exhibited considerable heterogeneity. For example, in New York, high-SES neighborhoods were associated with higher concentrations of pollution.

CONCLUSIONS

We found statistically significant associations of SES measures with predicted air pollutant concentrations, demonstrating the importance of accounting for neighborhood- and individual-level SES in air pollution health effects research.

Spatial Polygamy and Contextual Exposures (SPACEs): Promoting Activity Space Approaches in Research on Place and Health

Exposure science has developed rapidly and there is an increasing call for greater precision in the measurement of individual exposures across space and time. Social science interest in an individual's environmental exposure, broadly conceived, has arguably been quite limited conceptually and methodologically. Indeed, we appear to lag behind our exposure science colleagues in our theories, data, and methods. In this paper we discuss a framework based on the concept of spatial polygamy to demonstrate the need to collect new forms of data on human spatial behavior and contextual exposures across time and space. Adopting new data and methods will be essential if we want to better understand social inequality in terms of exposure to health risks and access to health resources. We discuss the opportunities and challenges focusing on the potential seemingly offered by focusing on human mobility, and specifically the utilization of activity space concepts and data. A goal of the paper is to spatialize social and health science concepts and research practice vis-a-vis the complexity of exposure. The paper concludes with some recommendations for future research focusing on theoretical and conceptual development, promoting research on new types of places and human movement, the dynamic nature of contexts, and on training. "When we elect wittingly or unwittingly, to work within a level … we tend to discern or construct - whichever emphasis you prefer - only those kinds of systems whose elements are confined to that level."Otis Dudley Duncan (1961, p. 141)."…despite the new ranges created by improved transportation, local government units have tended to remain medieval in size."Torsten Hägerstrand (1970, p.18)"A detective investigating a crime needs both tools and understanding. If he has no fingerprint powder, he will fail to find fingerprints on most surfaces. If he does not understand where the criminal is likely to have put his fingers, he will not look in the right places. Equally, the analyst of data needs both tools and understanding."John Tukey (1977, p.1)"When we observe the environment, we necessarily do so on only a limited number of scales."Simon Levin (1992, p. 1945)There is a desperate need to develop methods with the same precision for an individual's environmental exposure as we have for an individual's genome … even a partial, targeted understanding of exposure can provide substantial advantages."Christopher Wild (2005, p.1848).