The Vancouver Area Neighbourhood Deprivation Index (VANDIX): a census-based tool for assessing small-area variations in health status

Neighborhood Characteristics Associated with Running in Metro Vancouver: A Preliminary Analysis

Running can improve physical health and psychological wellbeing. However, the characteristics of conducive running environments are relatively unknown. This study determines neighborhood factors that attract running and explores how age and gender mediate built environment preferences. Spatial patterns of runners in Metro Vancouver were identified using crowdsourced fitness data from Strava, a popular application for tracking physical activities. The influence of socio-economic status (SES), green and/or blue space, and urbanicity on route popularity was assessed using a Generalized Linear Model (GLM). The influence of these neighborhood variables was also calculated for runners by age and gender. The results show high neighborhood SES, the presence of green and/or blue space, and high population density are associated with increased running activities in all age and gender groups. This study contributes a novel approach to understanding conducive running environments by demonstrating the utility of crowdsourced data in combination with data about urban environments. The patterns of this large group of runners can be used to inform planning for cities that promote running, as well as seek to encourage equal participation among different ages and genders.

Neighborhood Greenspace and Socioeconomic Risk are Associated with Diabetes Risk at the Sub-neighborhood Scale: Results from the Prospective Urban and Rural Epidemiology (PURE) Study

Greenspace and socioeconomic status are known correlates of diabetes prevalence, but their combined effects at the sub-neighborhood scale are not yet known. This study derives, maps, and validates a combined socioeconomic/greenspace index of individual-level diabetes risk at the sub-neighborhood scale, without the need for clinical measurements. In two Canadian cities (Vancouver and Hamilton), we computed 4 greenspace variables from satellite imagery and extracted 11 socioeconomic variables from the Canadian census. We mapped 5125 participants from the Prospective Urban and Rural Epidemiology Study by their residential address and used age- and sex-dependent walking speeds to estimate individual exposure zones to local greenspace and socioeconomic characteristics, which were then entered into a principal component analysis to derive a novel diabetes risk index (DRI-GLUCoSE). We mapped index scores in both study areas and validated the index using fully adjusted logistic regression models to predict individual diabetes status. Model performance was then compared to other non-clinical diabetes risk indices from the literature. Diabetes prevalence among participants was 9.9%. The DRI-GLUCoSE index was a significant predictor of diabetes status, exhibiting a small non-significant attenuation with the inclusion of dietary and physical activity variables. The final models achieved a predictive accuracy of 75%, the highest among environmental risk models to date. Our combined index of local greenspace and socioeconomic factors demonstrates that the environmental component of diabetes risk is not sufficiently explained by diet and physical activity, and that increasing urban greenspace may be a suitable means of reducing the burden of diabetes at the community scale.

British Columbia’s Index of Multiple Deprivation for Community Health Service Areas

Area-based socio-economic indicators, such as the Canadian Index of Multiple Deprivation (CIMD), have been used in equity analyses to inform strategies to improve needs-based, timely, and effective patient care and public health services to communities. The CIMD comprises four dimensions of deprivation: residential instability, economic dependency, ethno-cultural composition, and situational vulnerability. Using the CIMD methodology, the British Columbia Index of Multiple Deprivation (BCIMD) was developed to create indexes at the Community Health Services Area (CHSA) level in British Columbia (BC). BCIMD indexes are reported by quintiles, where quintile 1 represents the least deprived (or ethno-culturally diverse), and quintile 5 is the most deprived (or diverse). Distinctive characteristics of a community can be captured using the BCIMD, where a given CHSA may have a high level of deprivation in one dimension and a low level of deprivation in another. The utility of this data as a surveillance tool to monitor population demography has been used to inform decision making in healthcare by stakeholders in the regional health authorities and governmental agencies. The data have also been linked to health care data, such as COVID-19 case incidence and vaccination coverage, to understand the epidemiology of disease burden through an equity lens.

Does Where You Live Predict What You Say? Associations between Neighborhood Factors, Child Sleep, and Language Development

Language ability is strongly related to important child developmental outcomes. Family-level socioeconomic status influences child language ability; it is unclear if, and through which mechanisms, neighborhood-level factors impact child language. The current study investigated the association between neighborhood factors (deprivation and disorder) assessed before birth and child language outcomes at age 5, with sleep duration as a potential underlying pathway. Secondary analysis was conducted on data collected between 2008 and 2018 on a subsample of 2444 participants from the All Our Families cohort study (Calgary, Canada) for whom neighborhood information from pregnancy could be geocoded. Neighborhood deprivation was determined using the Vancouver Area Neighborhood Deprivation Index (VANDIX), and disorder was assessed using crime reports. Mothers reported on their children’s sleep duration and language ability. Multilevel modeling indicated that greater neighborhood deprivation and disorder during pregnancy were predictive of lower scores on the Child Communication Checklist–2 (CCC–2) at 5 years. Path analyses revealed an indirect effect of neighborhood disorder on language through child sleep duration at 12 months. These results add to growing evidence that child development should be considered within the context of multiple systems. Sleep duration as an underlying link between environmental factors and child language ability warrants further study as a potential target for intervention.

A systematic review of geographic indices of disadvantage with implications for older adults

BACKGROUNDNeighborhood-level socioeconomic disadvantage has wide-ranging impacts on health outcomes, particularly in older adults. Although indices of disadvantage are a widely used tool, research conducted to date has not codified a set of standard variables that should be included in these indices for the United States. The objective of this study was to conduct a systematic review of literature describing the construction of geographic indices of neighborhood-level disadvantage and to summarize and distill the key variables included in these indices. We also sought to demonstrate the utility of these indices for understanding neighborhood-level disadvantage in older adults.METHODSWe conducted a systematic review of existing indices in the English-language literature.RESULTSWe identified 6021 articles, of which 130 met final study inclusion criteria. Our review identified 7 core domains across the surveyed papers, including income, education, housing, employment, neighborhood structure, demographic makeup, and health. Although not universally present, the most prevalent variables included in these indices were education and employment.CONCLUSIONIdentifying these 7 core domains is a key finding of this review. These domains should be considered for inclusion in future neighborhood-level disadvantage indices, and at least 5 domains are recommended to improve the strength of the resulting index. Targeting specific domains offers a path forward toward the construction of a new US-specific index of neighborhood disadvantage with health policy applications. Such an index will be especially useful for characterizing the life-course impact of lived disadvantage in older adults.

Social inequalities in mobility during and following the COVID-19 associated lockdown of the Madrid metropolitan area in Spain

Spain has been one of the most affected regions by the COVID-19 worldwide, and Madrid its most affected city. In response to this, the Spanish government enacted a strict lockdown in late March 2020, that was gradually eased until June 2020. We explored differentials in mobility by area-level deprivation in the functional area of Madrid, before, during, and after the COVID-19 lockdown. We used cell phone-derived mobility indicators (% of the population leaving their area) from the National Institute of Statistics (INE), and a composite measure of deprivation from the Spanish Society of Epidemiology (SEE). We computed changes in mobility with respect to pre-pandemic levels, and explored spatial patterns and associations with deprivation. We found that levels of mobility before COVID-19 were slightly higher in areas with lower deprivation. The economic hibernation period resulted in very strong declines in mobility, most acutely in low deprivation areas. These differences weakened during the re-opening, and levels of mobility were similar by deprivation once the lockdown was completely lifted. Given the existence of important socioeconomic differentials in COVID-19 exposure, it is key to ensure that these interventions do not widen existing social inequalities.

Transport Time and Mortality in Critically Ill Patients with Severe Traumatic Brain Injury

PURPOSE

Severe traumatic brain injury (TBI) is a major cause of morbidity and mortality in critically ill patients. Pre-hospital care and transportation time may impact their outcomes.

METHODS

Using the British Columbia Trauma Registry, we included 2,860 adult (≥18 years) patients with severe TBI (abbreviated injury scale head score ≥4), who were admitted to an intensive care unit (ICU) in a centre with neurosurgical services from January 1, 2000 to March 31, 2013. We evaluated the impact of transportation time (time of injury to time of arrival at a neurosurgical trauma centre) on in-hospital mortality and discharge disposition, adjusting for age, sex, year of injury, injury severity score (ISS), revised trauma score at the scene, location of injury, socio-economic status and direct versus indirect transfer.

RESULTS

Patients had a median age of 43 years (interquartile range [IQR] 26-59) and 676 (23.6%) were female. They had a median ISS of 33 (IQR 26-43). Median transportation time was 80 minutes (IQR 40-315). ICU and hospital length of stay were 6 days (IQR 2-12) and 20 days (IQR 7-42), respectively. Six hundred and ninety-six (24.3%) patients died in hospital. After adjustment, there was no significant impact of transportation time on in-hospital mortality (odds ratio 0.98, 95% confidence interval 0.95-1.01). There was also no significant effect on discharge disposition.

CONCLUSIONS

No association was found between pre-hospital transportation time and in-hospital mortality in critically ill patients with severe TBI.

Where to enhance rural palliative care? Developing a spatial model to identify suitable communities most in need of service enhancement

Background

In Canada, access to palliative care is a growing concern, particularly in rural communities. These communities have constrained health care services and accessing local palliative care can be challenging. The Site Suitability Model (SSM) was developed to identify rural “candidate” communities with need for palliative care services and existing health service capacity that could be enhanced to support a secondary palliative care hub. The purpose of this study was to test the feasibility of implementing the SSM in Ontario by generating a ranked summary of rural “candidate” communities as potential secondary palliative care hubs.

Methods

Using Census data combined with community-level data, the SSM was applied to assess the suitability of 12 communities as rural secondary palliative care hubs. Scores from 0 to 1 were generated for four equally-weighted components: (1) population as the total population living within a 1-h drive of a candidate community; (2) isolation as travel time from that community to the nearest community with palliative care services; (3) vulnerability as community need based on a palliative care index score; and (4) community readiness as five dimensions of fit between a candidate community and a secondary palliative care hub. Component scores were summed for the SSM score and adjusted to range from 0 to 1.

Results

Population scores for the 12 communities ranged widely (0.19–1.00), as did isolation scores (0.16–0.94). Vulnerability scores ranged more narrowly (0.27–0.35), while community readiness scores ranged from 0.4–1.0. These component scores revealed information about each community’s particular strengths and weaknesses. Final SSM scores ranged from a low of 0.33 to a high of 0.76.

Conclusions

The SSM was readily implemented in Ontario. Final scores generated a ranked list based on the relative suitability of candidate communities to become secondary palliative care hubs. This list provides information for policy makers to make allocation decisions regarding rural palliative services. The calculation of each community’s scores also generates information for local policy makers about how best to provide these services within their communities. The multi-factorial structure of the model enables decision makers to adapt the relative weights of its components.

A Bayesian shared components modeling approach to develop small area indicators of social determinants of health with measures of uncertainty

OBJECTIVES

Existing Canadian social determinants of health (SDOH) indicators do not quantify uncertainty to identify priority areas. The objectives of this methodologic study were: (1) to estimate and map small area (dissemination area) shared and variable-specific SDOH indicators with measures of uncertainty using a Bayesian model that accounts for spatial dependence; (2) to quantify geographic variation in the SDOH indicators and their contribution to a shared indicator; and (3) to assess the SDOH indicators' associations with behavioural risk factors and their consistency with the Ontario Marginalization Index (ON-Marg).

METHODS

Lower education-, income-, unemployment-, living alone- and visible minority-related variables used in existing Canadian SDOH indices were fit as dependent variables to a Bayesian model to produce area-based SDOH indicators that were mapped with measures of uncertainty in two study areas. The fractions of spatial variation explained by the model components were computed. Bayesian analysis of variance was used to examine the SDOH indicator associations with behavioural risk factors and their consistency with ON-Marg examined using Pearson's correlation coefficient.

RESULTS

The shared component was strongly associated with material deprivation (i.e., income) in each study area; however, variable-specific SDOH indicators were important too. The SDOH indicators were associated with behavioural risk factors for chronic disease, particularly alcohol consumption and smoking, and the shared component estimates were consistent with the ON-Marg material deprivation.

CONCLUSIONS

The Bayesian approach to produce SDOH indicators met the three study objectives and as such provides a new approach to prioritize areas that may experience health inequalities.

Spatiotemporal analysis of regional socio-economic vulnerability change associated with heat risks in Canada

Excess mortality can be caused by extreme hot weather events, which are increasing in severity and frequency in Canada due to climate change. Individual and social vulnerability factors influence the mortality risk associated with a given heat exposure. We constructed heat vulnerability indices using census data from 2006 and 2011 in Canada, developed a novel design to compare spatiotemporal changes of heat vulnerability, and identified locations that may be increasingly vulnerable to heat. The results suggest that 1) urban areas in Canada are particularly vulnerable to heat, 2) suburban areas and satellite cities around major metropolitan areas show the greatest increases in vulnerability, and 3) heat vulnerability changes are driven primarily by changes in the density of older ages and infants. Our approach is applicable to heat vulnerability analyses in other countries.

The Heat Exposure Integrated Deprivation Index (HEIDI): A data-driven approach to quantifying neighborhood risk during extreme hot weather

Mortality attributable to extreme hot weather is a growing concern in many urban environments, and spatial heat vulnerability indexes are often used to identify areas at relatively higher and lower risk. Three indexes were developed for greater Vancouver, Canada using a pool of 20 potentially predictive variables categorized to reflect social vulnerability, population density, temperature exposure, and urban form. One variable was chosen from each category: an existing deprivation index, senior population density, apparent temperature, and road density, respectively. The three indexes were constructed from these variables using (1) unweighted, (2) weighted, and (3) data-driven Heat Exposure Integrated Deprivation Index (HEIDI) approaches. The performance of each index was assessed using mortality data from 1998-2014, and the maps were compared with respect to spatial patterns identified. The population-weighted spatial correlation between the three indexes ranged from 0.68-0.89. The HEIDI approach produced a graduated map of vulnerability, whereas the other approaches primarily identified areas of highest risk. All indexes performed best under extreme temperatures, but HEIDI was more useful at lower thresholds. Each of the indexes in isolation provides valuable information for public health protection, but combining the HEIDI approach with unweighted and weighted methods provides richer information about areas most vulnerable to heat.

The development of a spatial palliative care index instrument for assessing population-level need for palliative care services

We developed an index to measure potential need for palliative care services (PCIX). This is an instrument that enables spatial identification of potential population-level need for palliative care services and can be developed using census data. Four indicators of potential need for palliative care services -age, sex, living arrangement, socio-economic status (SES)-were used to produce composite potential need scores for DAs. Scores were graphically mapped, producing a spatial delineation of relative need for end-of-life services. To assess the benefit of combining multiple variables to define potential need, PCIX resolution was compared to general SES-based delineations of need. PCIX scores and maps were generated for all DAs, revealing spatial variability in potential need for palliative care services (PCS). Comparison of PCIX maps to those based on purely on SES indicated that use of variables specifically linked to palliative need resulted in more precise delineations of potential populations in need of PCS. Using composite scores - based on freely available census data - to spatially assess potential need for palliative care services can provide critical data for decision makers charged with rationalizing service locations and service capacity.

Spatial Variability of Geriatric Depression Risk in a High-Density City: A Data-Driven Socio-Environmental Vulnerability Mapping Approach

Previous studies found a relationship between geriatric depression and social deprivation. However, most studies did not include environmental factors in the statistical models, introducing a bias to estimate geriatric depression risk because the urban environment was found to have significant associations with mental health. We developed a cross-sectional study with a binomial logistic regression to examine the geriatric depression risk of a high-density city based on five social vulnerability factors and four environmental measures. We constructed a socio-environmental vulnerability index by including the significant variables to map the geriatric depression risk in Hong Kong, a high-density city characterized by compact urban environment and high-rise buildings. Crude and adjusted odds ratios (ORs) of the variables were significantly different, indicating that both social and environmental variables should be included as confounding factors. For the comprehensive model controlled by all confounding factors, older adults who were of lower education had the highest geriatric depression risks (OR: 1.60 (1.21, 2.12)). Higher percentage of residential area and greater variation in building height within the neighborhood also contributed to geriatric depression risk in Hong Kong, while average building height had negative association with geriatric depression risk. In addition, the socio-environmental vulnerability index showed that higher scores were associated with higher geriatric depression risk at neighborhood scale. The results of mapping and cross-section model suggested that geriatric depression risk was associated with a compact living environment with low socio-economic conditions in historical urban areas in Hong Kong. In conclusion, our study found a significant difference in geriatric depression risk between unadjusted and adjusted models, suggesting the importance of including environmental factors in estimating geriatric depression risk. We also developed a framework to map geriatric depression risk across a city, which can be used for identifying neighborhoods with higher risk for public health surveillance and sustainable urban planning.

Assessing the validity of commercial and municipal food environment data sets in Vancouver, Canada

OBJECTIVE

The present study assessed systematic bias and the effects of data set error on the validity of food environment measures in two municipal and two commercial secondary data sets.

DESIGN

Sensitivity, positive predictive value (PPV) and concordance were calculated by comparing two municipal and two commercial secondary data sets with ground-truthed data collected within 800 m buffers surrounding twenty-six schools. Logistic regression examined associations of sensitivity and PPV with commercial density and neighbourhood socio-economic deprivation. Kendall's τ estimated correlations between density and proximity of food outlets near schools constructed with secondary data sets v. ground-truthed data.

SETTING

Vancouver, Canada.

SUBJECTS

Food retailers located within 800 m of twenty-six schools RESULTS: All data sets scored relatively poorly across validity measures, although, overall, municipal data sets had higher levels of validity than did commercial data sets. Food outlets were more likely to be missing from municipal health inspections lists and commercial data sets in neighbourhoods with higher commercial density. Still, both proximity and density measures constructed from all secondary data sets were highly correlated (Kendall's τ>0·70) with measures constructed from ground-truthed data.

CONCLUSIONS

Despite relatively low levels of validity in all secondary data sets examined, food environment measures constructed from secondary data sets remained highly correlated with ground-truthed data. Findings suggest that secondary data sets can be used to measure the food environment, although estimates should be treated with caution in areas with high commercial density.

Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach

BACKGROUND

Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city.

METHODS

In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city.

RESULTS

The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation.

CONCLUSION

Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

Delineation of Spatial Variability in the Temperature-Mortality Relationship on Extremely Hot Days in Greater Vancouver, Canada

BACKGROUND

Climate change has increased the frequency and intensity of extremely hot weather. The health risks associated with extemely hot weather are not uniform across affected areas owing to variability in heat exposure and social vulnerability, but these differences are challenging to map with precision.

OBJECTIVES

We developed a spatially and temporally stratified case-crossover approach for delineation of areas with higher and lower risks of mortality on extremely hot days and applied this approach in greater Vancouver, Canada.

METHODS

Records of all deaths with an extremely hot day as a case day or a control day were extracted from an administrative vital statistics database spanning the years of 1998-2014. Three heat exposure and 11 social vulnerability variables were assigned at the residential location of each decedent. Conditional logistic regression was used to estimate the odds ratio for a 1°C increase in daily mean temperature at a fixed site with an interaction term for decedents living above and below different values of the spatial variables.

RESULTS

The heat exposure and social vulnerability variables with the strongest spatially stratified results were the apparent temperature and the labor nonparticipation rate, respectively. Areas at higher risk had values ≥ 34.4°C for the maximum apparent temperature and ≥ 60% of the population neither employed nor looking for work. These variables were combined in a composite index to quantify their interaction and to enhance visualization of high-risk areas.

CONCLUSIONS

Our methods provide a data-driven framework for spatial delineation of the temperature--mortality relationship by heat exposure and social vulnerability. The results can be used to map and target the most vulnerable areas for public health intervention. Citation: Ho HC, Knudby A, Walker BB, Henderson SB. 2017. Delineation of spatial variability in the temperature-mortality relationship on extremely hot days in greater Vancouver, Canada. Environ Health Perspect 125:66-75; http://dx.doi.org/10.1289/EHP224.

Socio-economic deprivation: a significant determinant affecting stage of oral cancer diagnosis and survival

Background

Many factors contribute to socioeconomic status (SES), yet in most survival studies only income is used as a measure for determining SES. We used a complex, composite, census-based metric for socioeconomic deprivation to better distinguish individuals with lower SES and assess its impact on survival and staging trends of oral cancers.

Methods

Oropharyngeal (OPC) and oral cavity cancer (OCC) cases were identified from the British Columbia cancer registry between 1981–2009 and placed into affluent and deprived neighborhoods using postal codes linked to VANDIX (a composite SES index based on 7 census variables encompassing income, housing, family structure, education, and employment). Stage and cancer-specific survival rates were examined by sex, SES, and time period.

Results

Approximately 50 % of OPC and OCC cases of both sexes resided in SES deprived neighborhoods. Numbers of cases have increased in recent years for all but OCC in men. The deprivation gap in survival between affluent and deprived neighborhoods widened in recent years for OPC and OCC in men, while decreasing for OPC and increasing slightly for OCC in women. Greater proportions of OCC cases were diagnosed at later stage disease for both sexes residing in deprived neighborhoods, a trend not seen for OPC.

Conclusion

SES remains a significant independent determinant of survival for both OPC and OCC when using a composite metric for SES. OPC survival rates among men have improved, albeit at slower rates in deprived communities. OCC screening programs need to be targeted towards SES-deprived neighborhoods where greater proportions of cases were diagnosed at a later stage and survival rates have significantly worsened in both sexes.

A Spatial Framework to Map Heat Health Risks at Multiple Scales

In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord G_i index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord G_i index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.

Neighbourhood immigration, health care utilization and outcomes in patients with diabetes living in the Montreal metropolitan area (Canada): a population health perspective

BACKGROUND

Understanding health care utilization by neighbourhood is essential for optimal allocation of resources, but links between neighbourhood immigration and health have rarely been explored. Our objective was to understand how immigrant composition of neighbourhoods relates to health outcomes and health care utilization of individuals living with diabetes.

METHODS

This is a secondary analysis of administrative data using a retrospective cohort of 111,556 patients living with diabetes without previous cardiovascular diseases (CVD) and living in the metropolitan region of Montreal (Canada). A score for immigration was calculated at the neighbourhood level using a principal component analysis with six neighbourhood-level variables (% of people with maternal language other than French or English, % of people who do not speak French or English, % of immigrants with different times since immigration (<5 years, 5-10 years, 10-15 years, 15-25 years)). Dependent variables were all-cause death, all-cause hospitalization, CVD event (death or hospitalization), frequent use of emergency departments, frequent use of general practitioner care, frequent use of specialist care, and purchase of at least one antidiabetic drug. For each of these variables, adjusted odds ratios were estimated using a multilevel logistic regression.

RESULTS

Compared to patients with diabetes living in neighbourhoods with low immigration scores, those living in neighbourhoods with high immigration scores were less likely to die, to suffer a CVD event, to frequently visit general practitioners, but more likely to visit emergency departments or a specialist and to use an antidiabetic drug. These differences remained after controlling for patient-level variables such as age, sex, and comorbidities, as well as for neighbourhood attributes like material and social deprivation or living in the urban core.

CONCLUSIONS

In this study, patients with diabetes living in neighbourhoods with high immigration scores had different health outcomes and health care utilizations compared to those living in neighbourhoods with low immigration scores. Although we cannot disentangle the individual versus the area-based effect of immigration, these results may have an important impact for health care planning.