Small Geographical Level Research Articles

Integrating social inequalities in the burden of environmental stressors

Abstract In the process of designing equitable policies to reduce the burden of environmental stressors, it needs to be taken into consideration that socially disadvantaged communities are disproportionately exposed to environmental pollutants. These environmental health inequalities are currently however not routinely taken into account when designing mitigation measures, amongst others because of the absence of a coherent methodological framework. Within the BEST-COST project, an innovative framework for assessing social inequalities in the socioeconomic impact of environmental stressors will be developed and implemented. In particular, a novel index of multiple deprivation was developed. This includes indicators that are considered to be temporally and culturally valid for assessing social and material deprivation in Europe. In particular the new index was developed to make comparison across different European countries and to be used at a small geographical level. Within the workshop we will discuss the stepwise framework developed, starting from the results of a scoping review conducted to gather information methodological approaches used in the currently existing MDIs developed in Europe. The results of the scoping review were used to make informative decisions about the selection of indicators and other relevant methods (e.g. weighting). The construction of the composite score for the use within BEST-COST was evaluated also considering the data availability of the selected indicators at a small geographical level (i.e. LAU2) for the case-study countries: Belgium, Estonia, France, Norway and Portugal. Initial results of the quantification of social inequalities in the burden of environmental will be presented. Participants to the workshop will be able to understand the advantages and challenges of this kind of exercises with specific examples.

COVID-19 Vaccination Rate under Different Political Incentive: A Counterfactual Trend Approach Using Nationwide Data.

(1) Background: France implemented a COVID-19 certificate in July 2021 to incentivize the population to uptake COVID-19 vaccines. However, little is known about the variation in its impact across age groups and its dependence on socio-demographic, economic, logistic, or political factors. (2) Methods: Using France's weekly first dose vaccination rate, a counterfactual trend approach allowed for the estimation of the vaccination rate across age groups at a small geographical level before and after the implementation of the health pass. The effect of the health pass was operationalized as the vaccination rate among those who would not be vaccinated without it. (3) Results: Vaccination before the health pass varied greatly among age groups and was mainly influenced by territory (lower in rural and overseas territories when compared to urban and metropolitan ones), political beliefs, and socio-economic disparities. Vaccine logistics played a minor but significant role, while the impact of COVID-19 did not affect the vaccination rate. The health pass increased the vaccination overall but with varying efficiency across groups. It convinced mainly young people politically close to the governmental vaccination strategy and living in urban metropolitan areas with low socio-economical discrepancies. The selected variables explained most of the variability of the vaccination rate before the health pass; they explained, at most, a third of the variation in the health pass effect on vaccination. (4) Conclusions: From a public health perspective, the French health pass increased the overall vaccination, but failed to promote preventive behaviours in all segments of society, particularly in vulnerable communities.

Beyond the City–County Divide: Examining Consolidation Referenda Since 2000

Despite the relative infrequency of successful city-county consolidation campaigns, local government officials and reformers continue revisiting the possibilities and perceived benefits associated with consolidation. While studies analyzing campaigns for and against consolidation efforts exist, little is known beyond factors at the city and/or county level. For this reason, it is important to dig deeper and explore the differences at smaller geographic levels. Utilizing demographic, geographic, and electoral data, this study examines the factors which contribute to support (or opposition) to consolidation at the voting precinct-level in reform efforts over the last two decades. Results indicate that factors related to higher levels of education, home values, and larger Hispanic populations tend to increase the vote share in favor of consolidation, while areas with larger median household incomes demonstrate lower levels of support. These findings help extend our understanding of boundary change and voting in local referenda in the United States.

The effect of neighbourhood and spatial crime rates on mental wellbeing

This paper attempts to provide new evidence on the indirect cost of neighbourhood crime rates on mental wellbeing of residents. Crime places a heavy burden on the economy, and it receives significant attention from the public. Yet, the connection between crime and health for victims and non-victims is not well captured in the literature. Using spatial methodology, we differentiate the effect on mental wellbeing of residents due to the crime rates in their immediate neighbourhood (local) and crime rates in the surrounding areas (spatial). The main innovation of the paper is to use these crime rates at small geographical level (around 8500 residents) to better translate the changes in crime in the neighbourhoods and their effects on a person’s mental wellbeing. This is different to other studies that looked at crime incidences in larger geographical units. Our estimation results show that the increase in local and spatial crime rates against the property negatively affect the mental wellbeing of residents. The negative impact of spatial crime rates against the person is also present and is 6.7 times larger than the effect of property crime rates.

Spatial Analysis of Shared Risk Factors between Pleural and Ovarian Cancer Mortality in Lombardy (Italy).

Background: Asbestos exposure is a recognized risk factor for ovarian cancer and malignant mesothelioma. There are reports in the literature of geographical ecological associations between the occurrence of these two diseases. Our aim was to further explore this association by applying advanced Bayesian techniques to a large population (10 million people). Methods: We specified a series of Bayesian hierarchical shared models to the bivariate spatial distribution of ovarian and pleural cancer mortality by municipality in the Lombardy Region (Italy) in 2000–2018. Results: Pleural cancer showed a strongly clustered spatial distribution, while ovarian cancer showed a less structured spatial pattern. The most supported Bayesian models by predictive accuracy (widely applicable or Watanabe–Akaike information criterion, WAIC) provided evidence of a shared component between the two diseases. Among five municipalities with significant high standardized mortality ratios of ovarian cancer, three also had high pleural cancer rates. Wide uncertainty was present when addressing the risk of ovarian cancer associated with pleural cancer in areas at low background risk of ovarian cancer. Conclusions: We found evidence of a shared risk factor between ovarian and pleural cancer at the small geographical level. The impact of the shared risk factor can be relevant and can go unnoticed when the prevalence of other risk factors for ovarian cancer is low. Bayesian modelling provides useful information to tailor epidemiological surveillance.

Invited Commentary: Predicting Incidence Rates of Rare Cancers-Adding Epidemiologic and Spatial Contexts.

There are unique challenges to identifying causes of and developing strategies for prevention of rare cancers, driven by the difficulty in estimating incidence, prevalence, and survival due to small case numbers. Using a Poisson modeling approach, Salmerón et al. (Am J Epidemiol. 2022;191(3):487-498) built upon their previous work to estimate incidence rates of rare cancers in Europe using a Bayesian framework, establishing a uniform prior for a measure of variability for country-specific incidence rates. They offer a methodology with potential transferability to other settings with similar cancer surveillance infrastructure. However, the approach does not consider the spatiotemporal correlation of rare cancer case counts and other, potentially more appropriate nonnormal probability distributions. In this commentary, we discuss the implications of future work from cancer epidemiology and spatial epidemiology perspectives. We describe the possibility of developing prediction models tailored to each type of rare cancer; incorporating the spatial heterogeneity in at-risk populations, surveillance coverage, and risk factors in these predictions; and considering a modeling framework with which to address the inherent spatiotemporal components of these data. We note that extension of this methodology to estimate subcountry rates at provincial, state, or smaller geographic levels would be useful but would pose additional statistical challenges.

Depicting socioeconomic disadvantage and social gradient in quality of life in Limassol, Cyprus

Abstract Background The lack of validated and accepted area-level indices of socioeconomic disadvantage at national or regional level in Cyprus poses a barrier to “spatial thinking” among Public Health policy-makers and researchers. Methods A composite index of socioeconomic disadvantage was constructed by exploring the factor structure of 28 commonly used census indicators at city quarter (N = 38, median population 3894, IQR 2570-6169) and postcode level (N = 124, median 1320, IQR 798-2014). Predictive validity was explored in mixed random-effect regression models in term of associations with SF-36 health-related quality of life among a stratified random sample aged 45-64 (N = 450, 45 neighbourhoods). Results There was wide variability across communities in all indicators, for instance, 0.4%-25.9% unemployment, 41.1%-90.7% at most secondary level education, 3.6%-95.9% residences built prior to 1980. Two factors were identified: “socioeconomic disadvantage” (SED), with the same set of 11-12 indicators at the two levels of geographical aggregation, and “built environment”, reflecting proximity to the city centre, with nevertheless no meaningful associations with the outcome of interest. The SED index captured a stepwise decrease in physical (-1.39 95%CI -2.06, -0.72) and mental health (-1.26 95% -1.98, -0.54) scores across quartiles of postcodes with increasing disadvantage, of similar clinically significant effect size by gender. Associations attenuated slightly but remained significant after adjusting for person-based indicators of social position, with some weak evidence of cross-level interactions for physical, but not mental, health. Conclusions The composite index, tapping on educational, economic, material, occupational and social disadvantage, allowed the exploration of socioeconomic disadvantage patterning at a small-area level in the city of Limassol and documented the social gradient in health-related quality of life among a late working-life sample of residents. Key messages First-time depiction of the geographical patterning of socioeconomic disadvantage across the small geographical level of postcodes in the city of Limassol, Cyprus. Social gradient in physical and mental health in men and women across the continuum of socioeconomic disadvantage over and above person-based indicators of social position.

Characterizing the spatial variability and vulnerability of heatwaves on ambulance callouts in Adelaide

BACKGROUND AND AIM: High temperatures and heatwaves are associated with increased morbidity/mortality, and pose a substantial public health burden. Understanding the patterns of heatwave vulnerability at a fine spatial scale is essential to better target prevention strategies to areas with heightened vulnerability. This study aimed to investigate heatwave impacts on ambulance callouts in Adelaide at a small geographical level and identify area-level factors that influence the high morbidity risk within Adelaide. METHODS: Ambulance callouts data during the warm-season (October-March) from 2007 to 2014 were obtained from the South Australian Department for Health and Wellbeing. A time-stratified case-crossover design was used to examine associations between heatwave-severity (defined using Excess Heat Factor) and ambulance callouts at the Statistical Area Level 2 (SA2) spatial unit (representing suburbs). Effect modification by neighbourhood characteristics was examined and city-level estimates were pooled using random-effects meta-analyses. Results are reported as the percent increase in risk according to heatwave-severity (low-intensity, severe/extreme) compared with non-heatwaves, and were then mapped to produce a spatial representation of risk. RESULTS:A total of 377,125 ambulance callouts were included in the analysis. An exposure-response relationship was observed with ambulance callouts increasing by 5% (95%CI: 3-6%) and 8% (95%CI: 6-11%) during low-intensity and severe/extreme heatwaves, respectively. SA2s in western and southern Adelaide had higher risks during low-intensity heatwaves, while SA2s in southern and central Adelaide had higher risks during severe/extreme heatwaves. Analysis of area-level factors on heatwave and ambulance callouts revealed higher health impacts in areas (SA2s) characterized by high percentages of populations with high heat exposure, low socioeconomic status, poor housing and transport, vulnerable household composition, and poor health status. CONCLUSIONS:Our results show that ambulance callouts increased during heatwaves, but with a varying magnitude within Adelaide. The identified differential demographic, social, and environmental drivers of heatwave-morbidity calls for place-based prevention and adaptation strategies to reduce heatwave-related health burden. KEYWORDS: Heatwave, Morbidity,Spatial heterogeneity,Vulnerability, Case-crossover, Neighbourhood

Childhood leukemia near nuclear sites in Belgium: An ecological study at small geographical level

BackgroundA previous investigation of the occurrence of childhood acute leukemia around the Belgian nuclear sites has shown positive associations around one nuclear site (Mol-Dessel). In the following years, the Belgian Cancer Registry has made data available at the smallest administrative unit for which demographic information exists in Belgium, i.e. the statistical sector. This offers the advantage to reduce the potential misclassification due to large geographical scales. MethodsThe current study performed for the period 2006–2016 uses Poisson models to investigate (i) the incidence of childhood acute leukemia within 20 km around the four Belgian nuclear sites, (ii) exposure-response relationships between cancer incidence and surrogate exposures from the nuclear sites (distance, wind direction frequency and exposure by hypothetical radioactive discharges taking into account historical meteorological conditions). All analyses are carried out at statistical sector level. ResultsHigher incidence rate ratios were found for children <15 years (7 cases, RR = 3.01, 95% CI: 1.43;6.35) and children <5 years (< 5 cases, RR = 3.62, 95% CI: 1.35;9.74) living less than 5 km from the site of Mol-Dessel. In addition, there was an indication for positive exposure-response relationships with the different types of surrogate exposures. ConclusionResults confirm an increased incidence of acute childhood leukemia around Mol-Dessel, but the number of cases remains very small. Random variation cannot be excluded and the ecological design does not allow concluding on causality. These findings emphasize the need for more in-depth research into the risk factors of childhood leukemia, for a better understanding of the etiology of this disease.

The Use of Small Area Estimates in Place-Based Health Research.

Interest in the impact of the built environment on health behaviors, outcomes, and disparities is increasing, and the growing development of statistical modeling techniques has allowed researchers to better investigate these relationships. However, without enough data that are identifiable at smaller geographic levels (e.g., census tract), place-based health researchers are unable to reliably estimate the prevalence of a health outcome at these more granular and potentially more salient neighborhood levels.When reliable direct survey estimates cannot be produced because of small samples or a lack of samples, estimates based on small area estimation techniques are often used. As place-based health research and the production and secondary use of small area estimates increase, it is critical that researchers understand both the underlying methods used to create these estimates and their limitations. Without this foundation, researchers may fit inappropriate models, or interpret findings inaccurately.As a demonstrative example, we focus this discussion on the small area health indicator estimates recently produced through the 500 Cities Project by the Robert Wood Johnson Foundation, the Centers for Disease Control and Prevention (CDC), and the CDC Foundation.

Spatial Modelling to Inform Public Health Based on Health Surveys: Impact of Unsampled Areas at Lower Geographical Scale.

Small area estimation is an important tool to provide area-specific estimates of population characteristics for governmental organizations in the context of education, public health and care. However, many demographic and health surveys are unrepresentative at a small geographical level, as often areas at a lower level are not included in the sample due to financial or logistical reasons. In this paper, we investigated (1) the effect of these unsampled areas on a variety of design-based and hierarchical model-based estimates and (2) the benefits of using auxiliary information in the estimation process by means of an extensive simulation study. The results showed the benefits of hierarchical spatial smoothing models towards obtaining more reliable estimates for areas at the lowest geographical level in case a spatial trend is present in the data. Furthermore, the importance of auxiliary information was highlighted, especially for geographical areas that were not included in the sample. Methods are illustrated on the 2008 Mozambique Poverty and Social Impact Analysis survey, with interest in the district-specific prevalence of school attendance.

Thyroid cancer incidence near nuclear sites in Belgium: An ecological study at small geographical level.

In Belgium, variations in thyroid cancer incidence were observed around the major nuclear sites. The present ecological study investigates whether there is an excess incidence of thyroid cancer among people living in the vicinity of the four nuclear sites at the smallest Belgian geographical level. Rate ratios were obtained from a Bayesian hierarchical model for areas of varying sizes around the nuclear sites. Focused hypothesis tests and generalized additive models were performed to test the hypothesis of a gradient in thyroid cancer incidence with increasing levels of surrogate exposures. No evidence was found for more incident cases of thyroid cancer near the two nuclear power plants. Regarding the two industrial and research nuclear sites, no evidence for a higher incidence in the vicinity of Mol‐Dessel was observed, whereas a slightly nonsignificant higher incidence was found in the close vicinity of Fleurus. In addition, significant gradients for thyroid cancer incidence were observed with the different types of surrogate exposure considered in the 20 km area around the site of Fleurus (decreasing distance, increasing wind direction frequency and increasing exposure to estimated hypothetical radioactive discharges of iodine‐131). In the investigation at the smallest Belgian geographical level, variations in thyroid cancer incidence were found around the Belgian nuclear sites. Significant exposure–response relationships were also observed for the site of Fleurus. Further investigations into these findings could be useful to allow inferring causal relationships on the origin of variations in incidence and to provide information at the individual level.

Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates

BackgroundIt is well known that the burden caused by cancer can vary geographically, which may relate to differences in health, economics or lifestyle. However, to date, there was no comprehensive picture of how the cancer burden, measured by cancer incidence and survival, varied by small geographical area across Australia.MethodsThe Atlas consists of 2148 Statistical Areas level 2 across Australia defined by the Australian Statistical Geography Standard which provide the best compromise between small population and small area. Cancer burden was estimated for males, females, and persons separately, with 50 unique sex-specific (males, females, all persons) cancer types analysed. Incidence and relative survival were modelled with Bayesian spatial models using the Leroux prior which was carefully selected to provide adequate spatial smoothing while reflecting genuine geographic variation. Markov Chain Monte Carlo estimation was used because it facilitates quantifying the uncertainty of the posterior estimates numerically and visually.ResultsThe results of the statistical model and visualisation development were published through the release of the Australian Cancer Atlas (https://atlas.cancer.org.au) in September, 2018. The Australian Cancer Atlas provides the first freely available, digital, interactive picture of cancer incidence and survival at the small geographical level across Australia with a focus on incorporating uncertainty, while also providing the tools necessary for accurate estimation and appropriate interpretation and decision making.ConclusionsThe success of the Atlas will be measured by how widely it is used by key stakeholders to guide research and inform decision making. It is hoped that the Atlas and the methodology behind it motivates new research opportunities that lead to improvements in our understanding of the geographical patterns of cancer burden, possible causes or risk factors, and the reasons for differences in variation between cancer types, both within Australia and globally. Future versions of the Atlas are planned to include new data sources to include indicators such as cancer screening and treatment, and extensions to the statistical methods to incorporate changes in geographical patterns over time.

A Sociolinguistic Perspective on Accent and Social Mobility in the UK Teaching Profession

A new interdisciplinary approach to understanding the role of accent in relation to intergenerational social mobility is introduced here. Sociological analyses that attend to accent often focus on broader regional distinctions or construct limiting dichotomies of accents, rather than capturing the full variety and range of accents, often found at smaller geographic levels. Drawing on the case of the teaching profession and using qualitative data collected as part of a study of teachers, we illustrate how integrating sociolinguistics into a sociological analysis of social mobility would allow us to combine the micro-geography of class, mobility, and speech to a more granular level. The analytical tools provided by sociolinguistics, outlined here, could push forward work on understanding prospects for, and experiences of, social mobility.

Spatial distribution and temporal trends in social fragmentation in England, 2001−2011: a national study

ObjectiveSocial fragmentation is commonly examined in epidemiological studies of mental illness as high levels of social fragmentation are often found in areas with high prevalence of mental illness. In this...

Emergency Medical Services Utilization Is Associated With Community Deprivation in Children

Background: Pediatric emergency medical services (EMS) utilization is costly and resource intensive; significant variation exists across large-scale geographies. Less is known about variation at smaller geographic levels where factors including lack of transportation, low health literacy, and decreased access to medical homes may be more relevant. Our objective was to determine whether pediatric EMS utilization varied across Hamilton County, Ohio, census tracts and whether such utilization was associated with socioeconomic deprivation. Methods: This was a retrospective analysis of children living in Hamilton County, Ohio, transported by EMS to the Cincinnati Children’s emergency department between July 1, 2014, and July 31, 2016. Participants’ addresses were assigned to census tracts and an EMS utilization rate and deprivation index were calculated for each. Pearson’s correlation coefficients evaluated relationships between tract-level EMS utilization and deprivation. Tract-level deprivation was used as a predictor in patient-level evaluations of acuity. Results: During the study period, there were 4,877 pediatric EMS transports from 219 of the 222 county census tracts. The county EMS utilization rate during the study period was 2.4 transports per 100 children (range 0.2–11). EMS utilization rates were positively correlated with increasing deprivation (r = 0.72, 95% confidence interval [CI], 0.65–0.77). Deprivation was associated with lower illness severity at triage, fewer transports resulting in resuscitation suite use, and fewer transports resulting in hospitalizations (all p < 0.05). Conclusions: EMS utilization varied substantially across census tracts in Hamilton County, Ohio. A deeper understanding into why certain socioeconomically deprived areas contribute to disproportionately high rates of EMS utilization could support development of targeted interventions to improve use.

Estimating the prevalence of 26 health-related indicators at neighbourhood level in the Netherlands using structured additive regression

BackgroundLocal policy makers increasingly need information on health-related indicators at smaller geographic levels like districts or neighbourhoods. Although more large data sources have become available, direct estimates of the prevalence of a health-related indicator cannot be produced for neighbourhoods for which only small samples or no samples are available. Small area estimation provides a solution, but unit-level models for binary-valued outcomes that can handle both non-linear effects of the predictors and spatially correlated random effects in a unified framework are rarely encountered.MethodsWe used data on 26 binary-valued health-related indicators collected on 387,195 persons in the Netherlands. We associated the health-related indicators at the individual level with a set of 12 predictors obtained from national registry data. We formulated a structured additive regression model for small area estimation. The model captured potential non-linear relations between the predictors and the outcome through additive terms in a functional form using penalized splines and included a term that accounted for spatially correlated heterogeneity between neighbourhoods. The registry data were used to predict individual outcomes which in turn are aggregated into higher geographical levels, i.e. neighbourhoods. We validated our method by comparing the estimated prevalences with observed prevalences at the individual level and by comparing the estimated prevalences with direct estimates obtained by weighting methods at municipality level.ResultsWe estimated the prevalence of the 26 health-related indicators for 415 municipalities, 2599 districts and 11,432 neighbourhoods in the Netherlands. We illustrate our method on overweight data and show that there are distinct geographic patterns in the overweight prevalence. Calibration plots show that the estimated prevalences agree very well with observed prevalences at the individual level. The estimated prevalences agree reasonably well with the direct estimates at the municipal level.ConclusionsStructured additive regression is a useful tool to provide small area estimates in a unified framework. We are able to produce valid nationwide small area estimates of 26 health-related indicators at neighbourhood level in the Netherlands. The results can be used for local policy makers to make appropriate health policy decisions.

Spatial Inequality and Uneven Development: The Local Stratification of Poverty in Appalachia

Abstract Scholars have long analyzed the persistence of Appalachian poverty in counties, but less is known about the dimensions of poverty at smaller geographic levels. This paper builds upon the concept of spatial inequality, which explains how socio-economic disadvantage is segregated across residential space based on race and class. I extend the study of spatial inequality to Appalachia by investigating how local poverty is stratified across the region. The literature suggests two historical processes that could lead to spatial stratification in Appalachia: (1) the concentration of local economic opportunity in county seat cities, and (2) historical economic development processes that emphasized growth and marginalized the most rural areas of the region. Therefore, I analyze whether distance to a county seat influences poverty rates in Appalachian neighborhoods. The results indicate that distance to a county seat has a curvilinear ‘U-shaped’ relationship with poverty: the poorest neighborhoods, on average, are likely to be closest and farthest away from county seats. While data limitations exist, these findings emphasize the importance of place-based, subcounty initiatives for poverty alleviation in Appalachia.

Characteristics of small areas with high rates of hospital-treated self-harm: deprived, fragmented and urban or just close to hospital? A national registry study

BackgroundPrevious research has shown an inconsistent relationship between the spatial distribution of hospital treated self-harm and area-level factors such as deprivation and social fragmentation. However, many of these studies have...

Geographical variation and factors associated with colorectal cancer mortality in a universal health care system.

To investigate the geographical variation and small geographical area level factors associated with colorectal cancer (CRC) mortality. Information regarding CRC mortality was obtained from the population-based Manitoba Cancer Registry, population counts were obtained from Manitoba's universal health care plan Registry and characteristics of the area of residence were obtained from the 2001 Canadian census. Bayesian spatial Poisson mixed models were used to evaluate the geographical variation of CRC mortality and Poisson regression models for determining associations with CRC mortality. Time trends of CRC mortality according to income group were plotted using joinpoint regression. The southeast (mortality rate ratio [MRR] 1.31 [95% CI 1.12 to 1.54) and southcentral (MRR 1.62 [95% CI 1.35 to 1.92]) regions of Manitoba had higher CRC mortality rates than suburban Winnipeg (Manitoba's capital city). Between 1985 and 1996, CRC mortality did not vary according to household income; however, between 1997 and 2009, individuals residing in the highest-income areas were less likely to die from CRC (MRR 0.77 [95% CI 0.65 to 0.89]). Divergence in CRC mortality among individuals residing in different income areas increased over time, with rising CRC mortality observed in the lowest income areas and declining CRC mortality observed in the higher income areas. Individuals residing in lower income neighbourhoods experienced rising CRC mortality despite residing in a jurisdiction with universal health care and should receive increased efforts to reduce CRC mortality. These findings should be of particular interest to the provincial CRC screening programs, which may be able to reduce the disparities in CRC mortality by reducing the disparities in CRC screening participation.