Deprivation indices, population health and geography: an evaluation of the spatial effectiveness of indices at multiple scales

Regional Deprivation and Suicide

BACKGROUND

Regional deprivation due to regional economic and social differences can increase the risk of suicide. This study investigated whether regional structural deprivation in Germany is associated with a higher rate of suicide.

METHODS

Data from cause-of-death statistics for the years 2015-2021 were analyzed. A regression analysis for data with a negative binomial distribution was used to study the association between suicide mortality and the German Index of Multiple Deprivation (GIMD), which is based on data from 2015 and includes 401 districts and independent cities in Germany.

RESULTS

Among the districts and independent cities covered by the GIMD, the quintile with the highest deprivation had higher suicide rates than the quintile with the lowest deprivation (relative risk [RR]: 1.85; 95% confidence interval [1.72; 2.00]). A sensitivity analysis revealed similar associations when the analysis was restricted to men (RR = 1.99 [1.80; 2.18]) or women (RR = 1.69 [1.49; 1.92]). A stronger association, however, was found in a comparison of rural (RR = 2.29 [2.04; 2.57]) with urban areas (RR = 1.51 [1.36; 1.68]).

CONCLUSION

These findings document an association between regional deprivation and suicide. They highlight the need for a more targeted approach to suicide prevention in deprived regions. At the same time, more research is needed into the mechanisms and effects of regional deprivation on mortality due to suicide.

Spatial Hotspots and Sociodemographic Profiles Associated With Traumatic Brain Injury in Nova Scotia

Traumatic brain injury (TBI) is a leading cause of death and disability, primarily caused by falls and motor vehicle collisions (MVCs). Although many TBIs are preventable, there is a notable lack of studies exploring the association of geographically defined TBI hotspots with social deprivation. Geographic information systems (GIS) can be used to identify at-risk neighborhoods (hotspots) for targeted interventions. This study aims to determine the spatial distribution of TBI by major causes and to explore the sociodemographic and economic characteristics of TBI hotspots and cold spots in Nova Scotia. Patient data for TBIs from 2003 to 2019 were obtained from the Nova Scotia Trauma Registry. Residential postal codes were geocoded and assigned to dissemination areas (DA). Area-based risk factors and deprivation status (residential instability [RI], economic dependency [ED], ethnocultural composition [EC], and situational vulnerability [SV]) from the national census data were linked to DAs. Spatial autocorrelation was assessed using Moran's I, and hotspot analysis was performed using Getis-Ord Gi* statistic. Differences in risk factors between hot and cold spots were evaluated using the Mann-Whitney U test for numerical variables and the χ2 test or Fisher's exact test for categorical variables. A total of 5394 TBI patients were eligible for inclusion in the study. The distribution of hotspots for falls exhibited no significant difference between urban and rural areas (p = 0.71). Conversely, hotspots related to violence were predominantly urban (p = 0.001), whereas hotspots for MVCs were mostly rural (p < 0.001). Distinct dimensions of deprivation were associated with falls, MVCs, and violent hotspots. Fall hotspots were significantly associated with areas characterized by higher RI (p < 0.001) and greater ethnocultural diversity (p < 0.001). Conversely, the same domains exhibited an inverse relationship with MVC hotspots; areas with low RI and ethnic homogeneity displayed a higher proportion of MVC hotspots. ED and SV exhibited a strong gradient with MVC hotspots; the most deprived quintiles displayed the highest proportion of MVC hotspots compared with cold spots (ED; p = 0.002, SV; p < 0.001). Areas with the highest levels of ethnocultural diversity were found to have a significantly higher proportion of violence-related hotspots than cold spots (p = 0.005). This study offers two significant contributions to spatial epidemiology. First, it demonstrates the distribution of TBI hotspots by major injury causes using the smallest available geographical unit. Second, we disentangle the various pathways through which deprivation impacts the risk of main mechanisms of TBI. These findings provide valuable insights for public health officials to design targeted injury prevention strategies in high-risk areas.

Visible minority status and occupation were associated with increased COVID-19 infection in Greater Vancouver British Columbia between June and November 2020: an ecological study

Background

The COVID-19 pandemic has highlighted health disparities, especially among specific population groups. This study examines the spatial relationship between the proportion of visible minorities (VM), occupation types and COVID-19 infection in the Greater Vancouver region of British Columbia, Canada.

Methods

Provincial COVID-19 case data between June 24, 2020, and November 7, 2020, were aggregated by census dissemination area and linked with sociodemographic data from the Canadian 2016 census. Bayesian spatial Poisson regression models were used to examine the association between proportion of visible minorities, occupation types and COVID-19 infection. Models were adjusted for COVID-19 testing rates and other sociodemographic factors. Relative risk (RR) and 95% Credible Intervals (95% CrI) were calculated.

Results

We found an inverse relationship between the proportion of the Chinese population and risk of COVID-19 infection (RR = 0.98 95% CrI = 0.96, 0.99), whereas an increased risk was observed for the proportions of the South Asian group (RR = 1.10, 95% CrI = 1.08, 1.12), and Other Visible Minority group (RR = 1.06, 95% CrI = 1.04, 1.08). Similarly, a higher proportion of frontline workers (RR = 1.05, 95% CrI = 1.04, 1.07) was associated with higher infection risk compared to non-frontline.

Conclusion

Despite adjustments for testing, housing, occupation, and other social economic status variables, there is still a substantial association between the proportion of visible minorities, occupation types, and the risk of acquiring COVID-19 infection in British Columbia. This ecological analysis highlights the existing disparities in the burden of diseases among different visible minority populations and occupation types.

Bigger isn't better: Why small area geographies are best for actionable index development

Area-level measures of the social exposome provide powerful tools to understand how context contributes to health disparities. Due to the geographic phenomenon of the modifiable aerial unit problem, the geographic level at which the index is constructed can threaten it utility. Previous work indicates that using smaller geographic levels lead to increased measurement precision which may result in closer alignment to policies that directly address health disparities. To provide an illustrative example of this phenomenon, we use Medicare 100% Fee-for Service hospitalization claims data to evaluate the association between area-level disadvantage and 30-day readmissions when the Area Deprivation Index (ADI) is constructed at different geographic levels. When area-level disadvantage is summarized at the "neighborhood" census block group-the study's smallest geographic level-there was a 20% higher odds of readmissions for those living in the top 20% most disadvantaged neighborhoods compared to those living in the lowest 80% neighborhoods nationwide. Yet, evidence for an association with readmissions was not found when neighborhood disadvantaged was summarized at larger geographic levels. Smaller geographic levels appear most optimal to capture these effects. In order to provide publicly available data that is truly publicly useable, greater attention in providing small area health data is needed.

Impact of Socioeconomic Inequalities on Dental Caries Status in Sardinian Children

BACKGROUND

The association between oral health of schoolchildren living in the North Sardinia area and socioeconomic deprivation was assessed to evaluate a potential spatial correlation.

METHODS

A total of 10,947 subjects were examined (5281 aged 3-5-years, and 5666 aged 6-11-years). The WHO dmft index score was calculated following clinical examination by calibrated examiners. The Sardinian Deprivation Index (IDMS) of the children's municipalities was also considered. Descriptive, bivariate and multinomial data analysis was conducted to assess the association between clinical data and socioeconomic deprivation. The presence of systematic spatial variation regarding caries experience (dmft) and deprivation status was investigated using a spatial autoregressive analysis.

RESULTS

Caries figures were statistically different in the two age groups (dmf > 0, 13.79% in the younger group vs. dmf > 0, 34.20% in the older one, p < 0.01). In a multinomial logistic regression model for caries experience, all the covariates were statistically significantly associated (p < 0.01) in comparison with the base outcome "caries-free". Linear regression analysis showed a dependence of dmft on IDMS (p < 0.01). Based on this equation, the dmft of the 39 municipalities that did not participate in the survey was estimated. IDMS was statistically significantly associated (p < 0.01) with caries prevalence in the spatial regression model.

CONCLUSIONS

The deprivation index significantly increased the risk of caries for all categories of caries experience and prevalence compared to caries-free. The relationship between IDMS and caries data was also confirmed by spatial analysis.

Infant mortality and its determinants in Uganda 2016: Using a geographically weighted regression approach

Infant mortality (IM) represents the overall health of a country or region as it relates to access to medicine, health care, and clean water in a population. IM remains understudied in many areas of Uganda, as many studies are from urban the capital (Kampala). The long-term goal of this research is the mitigation of IM and poor pregnancy outcomes in Uganda. Insights gained from geographic distribution of IM will allow adaptation of diagnosis, treatments, and interventions within the studied areas. Through using OLS and geographically weighted regression, this study has explored the significant factors and their heterogeneous and scaling effects in 2016 across Uganda. The empirical findings from this study include a significant association between IM and both being unmarried and preferring to speak Luganda when interviewed. Those unmarried may lack a social network to assist with income, childcare, and household chores representing decreased resources. Additionally, being interviewed in Luganda was associated over a large geographic area, which may represent not being comfortable in English, which is the language of education, commerce, and presumably health care, thus suggesting a disconnect with health care settings. These data suggest that strides can be made in Uganda by providing targeted resources to areas with high rates of unmarried mothers and those areas with high rates of Luganda as their language of choice.

Cervical cancer screening pathways in France in 2015-2021, a nationwide study based on medico-administrative data

To better document cervical cancer screening (CCS) pathways, the purpose of our study was to examine CCS pathways among women who had undergone a screening test (opportunistic or organised programme), based on real-life data over a 7-year period. This study used data from the French national health care database (SNDS), which covers almost 100 % of the French population of around 66 million inhabitants. Data from 2015 to 2021 were extracted. More than one quarter (27 %) of women who were at least 25 years old in 2015 and up to 65 years old in 2021 were not screened over the 2015-2021 period. Compared to women who had undergone screening at least once, women who were not screened were older (36 % vs. 23 % in the 50-59 years age group in 2015) and lived in the most deprived urban areas (21 % vs 16 % for less and most deprived respectively). 57 % of women underwent screening within recommended intervals, 13 % of women were under-screened, and 30 % were overscreened. Overall, our study identified that, in 2021, women who participated in the French organised screening programme were less likely to be screened within the recommended interval over the 7-year period. These analyses need to be continued over time in order to assess whether the programme helps reintegrate women into the screening process.

Inequalities in access to NHS primary care dental services in Scotland during the COVID-19 pandemic

Introduction This study aimed to quantify the impact of the COVID-19 pandemic on access and inequalities in primary care dental services among children and adults in Scotland.Methods Access was measured as any NHS Scotland primary care dental contacts derived from administrative data from January 2019 to May 2022, linked to the area-based Scottish Index of Multiple Deprivation for children and adults, and related to population denominator estimates from National Record Scotland. Inequalities for pre-pandemic (January 2019-January 2020) and recent (December 2021-February 2022, and March 2022-May 2022) periods for both children and adults were calculated and compared using the slope index of inequality and relative index of inequality.Results Following the first lockdown (March 2020) there was a dramatic fall to near zero dental contacts, followed by a slow recovery to 64.8% of pre-pandemic levels by May 2022. There was initial widening of relative inequalities in dental contacts in early 2022, which, more recently, had begun to return to pre-pandemic levels.Conclusion COVID-19 had a major impact on access to NHS primary dental care, and while inequalities in access are apparent as services recover from lockdown, these inequalities are not a new phenomenon.

A spatial indicator of environmental and climatic vulnerability in Rome

BACKGROUND

Urban areas are disproportionately affected by multiple pressures from overbuilding, traffic, air pollution, and heat waves that often interact and are interconnected in producing health effects. A new synthetic tool to summarize environmental and climatic vulnerability has been introduced for the city of Rome, Italy, to provide the basis for environmental and health policies.

METHODS

From a literature overview and based on the availability of data, several macro-dimensions were identified on 1,461 grid cells with a width of 1 km² in Rome: land use, roads and traffic-related exposure, green space data, soil sealing, air pollution (PM_2.5, PM₁₀, NO₂, C₆H₆, SO₂), urban heat island intensity. The Geographically Weighted Principal Component Analysis (GWPCA) method was performed to produce a composite spatial indicator to describe and interpret each spatial feature by integrating all environmental dimensions. The method of natural breaks was used to define the risk classes. A bivariate map of environmental and social vulnerability was described.

RESULTS

The first three components explained most of the variation in the data structure with an average of 78.2% of the total percentage of variance (PTV) explained by the GWPCA, with air pollution and soil sealing contributing most in the first component; green space in the second component; road and traffic density and SO₂ in the third component. 56% of the population lives in areas with high or very high levels of environmental and climatic vulnerability, showing a periphery-centre trend, inverse to the deprivation index.

CONCLUSIONS

A new environmental and climatic vulnerability indicator for the city of Rome was able to identify the areas and population at risk in the city, and can be integrated with other vulnerability dimensions, such as social deprivation, providing the basis for risk stratification of the population and for the design of policies to address environmental, climatic and social injustice.

Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand

OBJECTIVES

Examine the impact of two generic-urban-rural experimental profile (UREP) and urban accessibility (UA)-and one purposely built-geographic classification for health (GCH)-rurality classification systems on the identification of rural-urban health disparities in Aotearoa New Zealand (NZ).

DESIGN

A comparative observational study.

SETTING

NZ; the most recent 5 years of available data on mortality events (2013-2017), hospitalisations and non-admitted hospital patient events (both 2015-2019).

PARTICIPANTS

Numerator data included deaths (n=156 521), hospitalisations (n=13 020 042) and selected non-admitted patient events (n=44 596 471) for the total NZ population during the study period. Annual denominators, by 5-year age group, sex, ethnicity (Māori, non-Māori) and rurality, were estimated from Census 2013 and Census 2018.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary measures were the unadjusted rural incidence rates for 17 health outcome and service utilisation indicators, using each rurality classification. Secondary measures were the age-sex-adjusted rural and urban incidence rate ratios (IRRs) for the same indicators and rurality classifications.

RESULTS

Total population rural rates of all indicators examined were substantially higher using the GCH compared with the UREP, and for all except paediatric hospitalisations when the UA was applied. All-cause rural mortality rates using the GCH, UA and UREP were 82, 67 and 50 per 10 000 person-years, respectively. Rural-urban all-cause mortality IRRs were higher using the GCH (1.21, 95% CI 1.19 to 1.22), compared with the UA (0.92, 95% CI 0.91 to 0.94) and UREP (0.67, 95% CI 0.66 to 0.68). Age-sex-adjusted rural and urban IRRs were also higher using the GCH than the UREP for all outcomes, and higher than the UA for 13 of the 17 outcomes. A similar pattern was observed for Māori with higher rural rates for all outcomes using the GCH compared with the UREP, and 11 of the 17 outcomes using the UA. For Māori, rural-urban all-cause mortality IRRs for Māori were higher using the GCH (1.34, 95% CI 1.29 to 1.38), compared with the UA (1.23, 95% CI 1.19 to 1.27) and UREP (1.15, 95% CI 1.10 to 1.19).

CONCLUSIONS

Substantial variation in rural health outcome and service utilisation rates were identified with different classifications. Rural rates using the GCH are substantially higher than the UREP. Generic classifications substantially underestimated rural-urban mortality IRRs for the total and Māori populations.

Access to outpatient care in Manhattan and Paris: A tale of real change in two world cities

France's system of universal health insurance (UHI) offers more equitable access to outpatient care than the patchwork system in the U.S., which does not have a UHI system. We investigate the degree to which the implementation of the Patient Protection and Affordable Care Act (ACA) has narrowed the gap in access to outpatient care between France and the U.S. To do so, we update a previous comparison of access to outpatient care in Manhattan and Paris as measured by age-adjusted rates of hospital discharge for avoidable hospital conditions (AHCs). We compare these rates immediately before and after the implementation of the ACA in 2014. We find that AHC rates in Manhattan declined by about 25% and are now lower than those in Paris. Despite evidence that access to outpatient care in Manhattan has improved, Manhattanites continue to experience greater residence-based neighborhood inequalities in AHC rates than Parisians. In Paris, there was a 3% increase in AHC rates and neighborhood-level inequalities increased significantly. Our analysis highlights the persistence of access barriers to outpatient care in Manhattan, particularly among racial and ethnic minorities, even following the expansion of health insurance coverage.

The impact of regional deprivation on stroke incidence, treatment, and mortality in Germany

BACKGROUND

Regional deprivation has been shown to be an influential factor in stroke incidence risk. However, there is a paucity of knowledge on regional differences in stroke incidence and mortality in Germany.

METHODS

We assessed data from the Diagnosis Related Groups statistics (2016-2019) and the German Federal Registry of Physicians (2019). Negative binomial regression analysis was used to examine the association between the German Index of Multiple Deprivation 2015 covering 401 districts and district-free cities in Germany and stroke incidence, treatment, and mortality.

RESULTS

The adjusted rate ratios of stroke incidence and mortality with the highest deprivation level compared with the least deprived area were 1.161 (95% CI [1.143, 1.179]) and 1.193 (95% CI [1.148, 1.239]), respectively. Moreover, this study revealed that physician density was higher in district-free cities compared to districts.

CONCLUSIONS

Our results indicate that regional deprivation is associated with incident and mortality cases of stroke, necessitating a more targeted approach to stroke prevention in deprived regions.

Regional Living Conditions and Individual Dietary Characteristics of the Russian Population

The goal of our study was to examine the effects of the regional characteristics of the living environment on individual a priori and a posteriori dietary patterns of the Russian population. For the analysis, we used cross-sectional data from the Epidemiology of Cardiovascular Diseases in the Regions of the Russian Federation study from 2013-2014. The sample included 18,054 men and women 25-64 years of age from 12 regions. Based on the frequency of consumption of basic foods, four a posteriori empirical dietary patterns (EDPs), along with an a priori cardioprotective dietary pattern (CPDP), were identified. To describe the regional living environment, five regional indices were used. Adherence to the meat-based EDP was directly associated with deterioration of social living conditions and a more northerly location for the region of residence. The probability of a CPDP increased with greater deterioration of social living conditions, aggravation of demographic crises, and higher industrial development in the region, as well as with declines in the economic development of the region, income, and economic inequality among the population. We detected several gender-dependent differences in the associations established. The patterns revealed reflect the national dietary preferences of Russians, and the regional indices characterize the effect of the living environment.

Small-Area Geographic and Socioeconomic Inequalities in Colorectal Cancer in Cyprus

Colorectal cancer (CRC) is one of the leading causes of death and morbidity worldwide. To date, the relationship between regional deprivation and CRC incidence or mortality has not been studied in the population of Cyprus. The objective of this study was to analyse the geographical variation of CRC incidence and mortality and its possible association with socioeconomic inequalities in Cyprus for the time period of 2000-2015. This is a small-area ecological study in Cyprus, with census tracts as units of spatial analysis. The incidence date, sex, age, postcode, primary site, death date in case of death, or last contact date of all alive CRC cases from 2000-2015 were obtained from the Cyprus Ministry of Health's Health Monitoring Unit. Indirect standardisation was used to calculate the sex and age Standardise Incidence Ratios (SIRs) and Standardised Mortality Ratios (SMRs) of CRC while the smoothed values of SIRs, SMRs, and Mortality to Incidence ratio (M/I ratio) were estimated using the univariate Bayesian Poisson log-linear spatial model. To evaluate the association of CRC incidence and mortality rate with socioeconomic deprivation, we included the national socioeconomic deprivation index as a covariate variable entering in the model either as a continuous variable or as a categorical variable representing quartiles of areas with increasing levels of socioeconomic deprivation. The results showed that there are geographical areas having 15% higher SIR and SMR, with most of those areas located on the east coast of the island. We found higher M/I ratio values in the rural, remote, and less dense areas of the island, while lower rates were observed in the metropolitan areas. We also discovered an inverted U-shape pattern in CRC incidence and mortality with higher rates in the areas classified in the second quartile (Q2-areas) of the socioeconomic deprivation index and lower rates in rural, remote, and less dense areas (Q4-areas). These findings provide useful information at local and national levels and inform decisions about resource allocation to geographically targeted prevention and control plans to increase CRC screening and management.

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.

Effect of Social Environments on Cardiovascular Disease in the United States

Background This study aims to examine the effect of time-variant perceived neighborhood social cohesion, perceived neighborhood physical disorder, and local crime on cardiovascular disease (CVD) incidence from 2006 through 2016. Methods and Results We obtained data from the Health & Retirement Study. Respondents aged ≥50 years and with no recorded history of CVD until 2006 (N=8826) were included and followed for 10 years. Cox proportional hazards models were estimated with CVD incidence as an outcome variable and time-variant social environment factors (perceived neighborhood social cohesion, perceived neighborhood physical disorder, and local crime) as exposures, after controlling for sociodemographic factors and CVD-related risk/protective factors. Our results showed that perceived neighborhood social cohesion was associated with CVD among Black respondents, but not Hispanic and White respondents. Perceived neighborhood physical disorder and local crime rates were not associated with CVD incidence across all racial and ethnic groups. Conclusions The results demonstrate that perceptions of favorable social environments need to be considered to reduce CVD risk among Black adults. Further research is needed to identify different pathways through which living in favorable social environments benefits cardiovascular health by racial and ethnic groups.

Longitudinal associations between the neighborhood social, natural, and built environment and mental health: A systematic review with meta-analyses

This review aimed to assess the longitudinal associations between neighborhood social, natural, and built environments, and multiple mental health outcomes (i.e., depression, anxiety, common mental disorder, and pooled mental disorders). Of 6,785 records retrieved, 30 studies fulfilled the inclusion criteria. Meta-analytical results primarily obtained from developed country studies showed that composite neighborhood socioeconomic status was negatively associated with depression (p = 0.007) and pooled mental disorders (p = 0.002), while neighborhood urbanicity was positively associated with depression (p = 0.012) and pooled mental disorders (p = 0.005). Future longitudinal studies with similar designs and standardized exposure assessments are warranted.

Spatial uncertainty and environment-health association: An empirical study of osteoporosis among "old residents" in public housing estates across a hilly environment

BACKGROUND

Built environment can influence physical conditions of older adults (e.g. osteoporosis). However, traditional methods using 2-dimensional circular buffer as a spatial structure to measure neighbourhood effect may create bias in health estimation, especially for the hilly and compact environment across low-income neighbourhoods (e.g. public housing estates).

METHODS

We evaluated the environmental influences on self-reported osteoporosis among "old residents" (age≥65) in Hong Kong (n = 2077). Twelve public housing estates across hilly neighbourhoods in Hong Kong were selected as study sites. A cross-validated approach was developed to evaluate four spatial structures (2D circular, 2D service area, 3D circular, 3D service area). To determine problems of spatial uncertainty, we compared odds ratios (OR) and differences in effect sizes from models using different spatial structures. When all adjusted models achieve significant results based on 95% confidence intervals (CI) and with all positive/negative ORs, this study reported to have reached "a result with consistency". Results from the 3D service area were then used to explain the environment-health relationship.

RESULTS

Different spatial structures can yield different results. Particularly, circular buffers overestimated environmental effects on self-reported osteoporosis. Overestimated measures were related to walkability and accessibility but not greenery. Specifically, results from the 3D service area showed that more public space and health facilities within a walkable distance (500 m) from a location of subject's residence were negatively associated with self-reported osteoporosis (adjusted ORs: 0.44 [0.29, 0.66]; 0.94 [0.90, 0.99]). However, more major transport facilities at the immediate distance from residence (200 m) was positively associated with self-reported osteoporosis (adjusted OR: 1.11 [1.01, 1.23]).

CONCLUSIONS

Physical conditions (e.g. osteoporosis) of older adults living in a hilly neighbourhood could be driven by walking behaviours. It is necessary to include local terrain and road network to define a walkable neighbourhood for environment-health estimations to minimize spatial bias.

Prevalence of overweight, obesity, and early adiposity rebound in nursery school children in southeastern France

BACKGROUND

The aim of the present study was to assess the prevalence of overweight, obesity, and early adiposity rebound in nursery school children aged 3.5-4.5 years and to evaluate associations with deprivation.

METHODS

A cross-sectional study was conducted in the Bouches-du-Rhône department in southeastern France. Data for all nursery school children aged 3.5-4.5 years were collected during systematic medical examinations. The prevalence of overweight and obesity was determined using French, International Obesity Task Force (IOTF), and World Health Organization reference values. A French ecological deprivation index was used to assess associations with deprivation.

RESULTS

Among 19,295 children included in the study, the prevalence of overweight (IOTF-25 < BMI < IOTF-30) was 9.1% and the prevalence of obesity (BMI > IOTF-30) was 2.6%. Children attending nursery schools in the most disadvantaged areas were 4.3 times more likely to be affected by obesity than those from schools in the most advantaged areas (OR: 4.32; 95% CI: 2.98-6.25, p < 0.001), after adjusting for gender, age group, and school status. Early adiposity rebound was observed in 2131 of 9872 children (21.6%).

CONCLUSION

Programs to prevent childhood overweight and obesity in France should be intensified and take account of major persistent social inequalities. Medical practitioners should learn to systematically assess BMI curve dynamics and early adiposity rebound.

Estimating uncertainty in a socioeconomic index derived from the American community survey

Socioeconomic indexes are widely used in public health to facilitate neighborhood-scale analyses. Although they are calculated with high levels of precision, they are rarely reported with accompanying measures of uncertainty (e.g., 90% confidence intervals). Here we use the variance replicate tables that accompany the United States Census Bureau's American Community Survey to report confidence intervals around the Yost Index, a socioeconomic index comprising seven variables that is frequently used in cancer surveillance. The Yost Index is reported as a percentile score from 1 (most affluent) to 100 (most deprived). We find that the average uncertainty for a census tract in the United States is plus or minus 8 percentiles, with the uncertainty a function of the value of the index itself. Scores at the extremes of the distribution are more precise and scores near the center are less precise. Less-affluent tracts have greater uncertainty than corresponding more-affluent tracts. Fewer than 50 census tracts of 72,793 nationally have unusual distributions of socioeconomic conditions that render the index uninformative. We demonstrate that the uncertainty in a census-based socioeconomic index is calculable and can be incorporated into any analysis using such an index.

COVID-19 Vaccine Rollouts and the Reproduction of Urban Spatial Inequality: Disparities Within Large US Cities in March and April 2021 by Racial/Ethnic and Socioeconomic Composition

Rollouts of COVID-19 vaccines in the USA were opportunities to redress disparities that surfaced during the pandemic. Initial eligibility criteria, however, neglected geographic, racial/ethnic, and socioeconomic considerations. Marginalized populations may have faced barriers to then-scarce vaccines, reinforcing disparities. Inequalities may have subsided as eligibility expanded. Using spatial modeling, we investigate how strongly local vaccination levels were associated with socioeconomic and racial/ethnic composition as authorities first extended vaccine eligibility to all adults. We harmonize administrative, demographic, and geospatial data across postal codes in eight large US cities over 3 weeks in Spring 2021. We find that, although vaccines were free regardless of health insurance coverage, local vaccination levels in March and April were negatively associated with poverty, enrollment in means-tested public health insurance (e.g., Medicaid), and the uninsured population. By April, vaccination levels in Black and Hispanic communities were only beginning to reach those of Asian and White communities in March. Increases in vaccination were smaller in socioeconomically disadvantaged Black and Hispanic communities than in more affluent, Asian, and White communities. Our findings suggest vaccine rollouts contributed to cumulative disadvantage. Populations that were left most vulnerable to COVID-19 benefited least from early expansions in vaccine availability in large US cities.

The evolution of infant mortality and neighbourhood inequalities in four world cities: 1988-2016

OBJECTIVES

To determine the level of neighbourhood inequalities in infant mortality (IM) rates in the urban core of four world cities and to examine the association between neighbourhood-level income and IM. We compare our findings with those published in 2004 to better understand how these city health systems have evolved.

METHODS

We compare IM rates among and within the four cities using data from four periods: 1988-1992; 1993-1997; 2003-2008 and 2012-2016. Using a maximum-likelihood negative binomial regression model that controls for births, we predict the relationship between neighbourhood-level income and IM.

RESULTS

IM rates have declined in all four cities. Neighbourhood-level income is statistically significant for New York and, for the two most recent periods, in Paris. In contrast, there is no significant relationship between neighbourhood income and IM in London or Tokyo.

CONCLUSIONS

Despite programmes to reduce IM inequalities at national and local levels, these persist in New York. Until the early part of this century, none of the other cities experienced a relationship between neighbourhood income and IM, but growing income inequalities within Paris have changed this situation and it now has geographic inequalities that are comparable to Manhattan.

POLICY IMPLICATIONS

Policy-makers in these cities should focus on better understanding the social and economic factors associated with neighbourhood inequalities in IM.

Factors associated with participation in the organized cervical cancer screening program in the greater Paris area (France): An analysis among more than 200,000 women

We aimed to identify the contextual factors associated with participation in the organized Cervical Cancer Screening (CCS) pilot program, which includes specific interventions to reach vulnerable women, in the Greater Paris region. Study population consisted of 231,712 women aged 25-65 years, who were not up to date to their smear test and had been invited to take part in the program from July 2014 to September 2017. Using a multilevel mixed logistic regression with random effects, we investigated the effect of grassroots interventions targeting vulnerable women, healthcare provider accessibility, social environment and municipal policy-related factors. The CCS rate was two times higher in women who had received their first invitation to the program during the study period (32·9%) compared to those who were already invited before the study period (15·3%). In both populations, there were no significant trends in participation with regards to the type of grassroots interventions, level of accessibility of healthcare services or municipal commitment to healthcare. Among women invited previously and aged above 35 increased participation was seen in neighborhoods with low proportion of single women or in less deprived neighborhoods. Our results identified groups of women who participated less in the organized CCS program and suggested that additional interventions targeting the barriers faced by vulnerable women, especially those aged 35-45 years old, are needed.

What Regional Living Conditions Affect Individual Smoking of Adults in Russia

Objectives: Our study evaluated the impact of a wide range of characteristics of large administrative regions on the individual level of cigarette smoking in the Russian adult population. Methods: The pool of participants included 20,303 individuals aged 25-64 years. We applied 64 characteristics of the 12 Russian regions under study for 2010-2014. Using principal component analysis, we deduced five evidence-based composite indices of the regions. We applied the generalized estimating equation to determine associations between the regional indices and the individual level of smoking. Results: The increased Industrial index in the region is associated with the probability of smoking (odds ratio = 1.15; 95% confidence interval = 1.06-1.24). The other indices show associations with smoking only in separate gender and educational groups. Surprisingly, it was found that the Economic index has no associations with the probability of smoking. Conclusion: We evaluated the key associations of the territorial indices with the individual probability of smoking, as well as the mutual influence between the territorial indices and individual factors.

Gendering Neighbourhood Marginalization Metrics in Mental Health Services Research: A Cross-Sectional Exploration of a Rural and Small Urban Population

Background: Little is known about the extent to which socioenvironmental characteristics may influence mental health outcomes in smaller population centres or differently among women and men. This study used a gender-based analysis approach to explore individual- and neighbourhood-level sex differences in mental health service use in a context of uniquely smaller urban and rural settlements. Methods: This cross-sectional analysis leveraged multiple person-based administrative health datasets linked with geospatial datasets among the population aged 1 and over in the province of New Brunswick, Canada. We used multinomial logistic regression to examine associations between neighbourhood characteristics with risk of service contacts for mood and anxiety disorders in 2015/2016, characterizing the areal measures among all residents (gender neutral) and by males and females separately (gender specific), and controlling for age group. Results: Among the province’s 707,575 eligible residents, 10.7% (females: 14.0%; males: 7.3%) used mental health services in the year of observation. In models adjusted for gender-neutral neighbourhood characteristics, service contacts were significantly more likely among persons residing in the most materially deprived areas compared with the least (OR = 1.09 [95% CI: 1.05–1.12]); when stratified by individuals’ sex, the risk pattern held for females (OR = 1.13 [95% CI: 1.09–1.17]) but not males (OR = 1.00 [95% CI: 0.96–1.05]). Residence in the most female-specific materially deprived neighbourhoods was independently associated with higher risk of mental health service use among individual females (OR = 1.08 [95% CI: 1.02–1.14]) but not among males (OR = 1.02 [95% CI: 0.95–1.10]). Conclusion: These findings emphasize that research needs to better integrate sex and gender in contextual measures aiming to inform community interventions and neighbourhood designs, notably in small urban and rural settings, to reduce socioeconomic inequalities in the burden of mental disorders.

Center variation in long-term outcomes for socioeconomically deprived children

Neighborhood socioeconomic deprivation is associated with adverse outcomes after pediatric liver transplant. We sought to determine if this relationship varies by transplant center. Using SRTR, we included patients <18 years transplanted 2008-2013 (N = 2804). We matched patient ZIP codes to a deprivation index (range [0,1]; higher values indicate increased socioeconomic deprivation). A center-level patient-mix deprivation index was defined by the distribution of patient-level deprivation. Centers (n = 66) were classified as high or low deprivation if their patient-mix deprivation index was above or below the median across centers. Center quality was classified as low or high graft failure if graft survival rates were better or worse than the overall 10-year graft survival rate. Primary outcome was patient-level graft survival. We used random-effect Cox models to evaluate center-level covariates on graft failure. We modeled center quality using stratified Cox models. In multivariate analysis, each 0.1 increase in the patient-mix deprivation index was associated with increased hazard of graft failure (HR 1.32; 95%CI: 1.05, 1.66). When stratified by center quality, patient-mix deprivation was no longer significant (HR 1.07, 95%CI: 0.89, 1.28). Some transplant centers care for predominantly high deprivation children and maintain excellent outcomes. Revealing and replicating these centers' practice patterns should enable more equitable outcomes.

Small-area analysis on socioeconomic inequalities in cancer survival for 25 cancer sites in Germany

Socioeconomic inequalities in cancer survival have been reported in various countries but it is uncertain to what extent they persist in countries with relatively comprehensive health insurance coverage such as Germany. We investigated the association between area-based socioeconomic deprivation on municipality level and cancer survival for 25 cancer sites in Germany. We used data from seven population-based cancer registries (covering 32 million inhabitants). Patients diagnosed in 1998 to 2014 with one of 25 most common cancer sites were included. Area-based socioeconomic deprivation was assessed using the categorized German Index of Multiple Deprivation (GIMD) on municipality level. We estimated 3-month, 1-year, 5-year and 5-year conditional on 1-year age-standardized relative survival using period approach for 2012 to 2014. Trend analyses were conducted for periods between 2003-2005 and 2012-2014. Model-based period analysis was used to calculate relative excess risks (RER) adjusted for age and stage. In total, 2 333 547 cases were included. For all cancers combined, 5-year survival rates by GIMD quintile were 61.6% in Q1 (least deprived), 61.2% in Q2, 60.4% in Q3, 59.9% in Q4 and 59.0% in Q5 (most deprived). For most cancer sites, the most deprived quintile had lower 5-year survival compared to the least deprived quintile even after adjusting for stage (all cancer sites combined, RER 1.16, 95% confidence interval 1.14-1.19). For some cancer sites, this association was stronger during short-term follow-up. Trend analyses showed improved survival from earlier to recent periods but persisting deprivation differences. The underlying reasons for these persisting survival inequalities and strategies to overcome them should be further investigated.

Measuring neighborhood deprivation for childhood health and development - scale implications in rural and urban context

Neighborhood deprivation plays an important role in childhood health and development, but defining the appropriate neighborhood definition presents theoretical as well as practical challenges. Few studies have compared neighborhood definitions outside of highly urbanized settings. The purpose of the current study was to evaluate how various administrative and ego-centric neighborhood definitions may impact measured exposure to deprivation across the urban-rural continuum. We do so using the Family Life Project, a prospective longitudinal population-based sample of families living in North Carolina and Pennsylvania (USA), which also sets the stage for future investigations of neighborhood impacts on childhood health and development. To measure neighborhood deprivation, a standardized index of socioeconomic deprivation was calculated using data from the 2007-2011 American Community Survey. Families' residential addresses when children were 2 months of age (n=1036) were geocoded and overlaid onto a deprivation index layer created at the census block group level to construct multiple administrative and ego-centric neighborhood definitions. Friedman tests were used to compare distributions of neighborhood deprivation across these neighborhood definitions within urbanized areas, urban clusters, and rural areas. Results indicated differences in urbanized areas (Chisquare= 897.75, P<0.001) and urban clusters (Chi-square=687.83, P<0.001), but not in rural areas (Chi-square=13.52, P=0.332). Findings imply that in urban areas, choice of neighborhood definition impacts measured exposure to neighborhood deprivation. Although exposure to neighborhood deprivation appears to be less sensitive to neighborhood definition in rural areas, researchers should apply theoretical reasoning to choose appropriate definitions of children's neighborhood.

Association Between Neighborhood-level Socioeconomic Deprivation and the Medication Level Variability Index for Children Following Liver Transplantation

BACKGROUND

Neighborhood socioeconomic deprivation is associated with adverse health outcomes. We sought to determine if neighborhood socioeconomic deprivation was associated with adherence to immunosuppressive medications after liver transplantation.

METHODS

We conducted a secondary analysis of a multicenter, prospective cohort of children enrolled in the medication adherence in children who had a liver transplant study (enrollment 2010-2013). Participants (N = 271) received a liver transplant ≥1 year before enrollment and were subsequently treated with tacrolimus. The primary exposure, connected to geocoded participant home addresses, was a neighborhood socioeconomic deprivation index (range 0-1, higher indicates more deprivation). The primary outcome was the medication level variability index (MLVI), a surrogate measure of adherence to immunosuppression in pediatric liver transplant recipients. Higher MLVI indicates worse adherence behavior; values ≥2.5 are predictive of late allograft rejection.

RESULTS

There was a 5% increase in MLVI for each 0.1 increase in deprivation index (95% confidence interval, -1% to 11%; P = 0.08). Roughly 24% of participants from the most deprived quartile had an MLVI ≥2.5 compared with 12% in the remaining 3 quartiles (P = 0.018). Black children were more likely to have high MLVI even after adjusting for deprivation (adjusted odds ratio 4.0 95% confidence interval, 1.7-10.6).

CONCLUSIONS

This is the first study to evaluate associations between neighborhood socioeconomic deprivation and an objective surrogate measure of medication adherence in children posttransplant. These findings suggest that neighborhood context may be an important consideration when assessing adherence. Differential rates of medication adherence may partly explain links between neighborhood factors and adverse health outcomes following pediatric liver transplantation.

Multiresolution Analyses of Neighborhood Correlates of Crime: Smaller Is Not Better

Population analyses of the correlates of neighborhood crime implicitly assume that a single spatial unit can be used to assess neighborhood effects. However, no single spatial unit may be suitable for analyses of the many social determinants of crime. Instead, effects may appear at multiple spatial resolutions, with some determinants acting broadly, others locally, and still others as some function of both global and local conditions. We provide a multiresolution spatial analysis that simultaneously examines US Census block, block group, and tract effects of alcohol outlets and drug markets on violent crimes in Oakland, California, incorporating spatial lag effects at the 2 smaller spatial resolutions. Using call data from the Oakland Police Department from 2010-2015, we examine associations of assaults, burglaries, and robberies with multiple resolutions of alcohol outlet types and compare the performance of single (block-level) models with that of multiresolution models. Multiresolution models performed better than the block models, reflected in improved deviance and Watanabe-Akaike information criteria and well-supported multiresolution associations. By considering multiple spatial scales and spatial lags in a Bayesian framework, researchers can explore multiresolution processes, providing more detailed tests of expectations from theoretical models and leading the way to more effective intervention efforts.

Selecting Health Need Indicators for Spatial Equity Analysis in the New Zealand Primary Care Context

PURPOSE

To examine potential indicators of health need for primary care in spatial equity research, and evidence of the Inverse Care Law in the Waikato region of New Zealand.

METHODS

A cross-sectional analysis of 7 health need indicators (ambulatory sensitive hospitalizations; cancer rate; mortality rate; New Zealand index of multiple deprivation-health domain; age; New Zealand index of deprivation; smoking rate) that were identified through a systematic review was carried out. Values of indicators were mapped and analyzed using geographic information systems (GIS). Spearman's correlations were calculated between indicators, and clusters of high need were identified through spatial autocorrelation. The impact of incorporating indicator-based weightings into an accessibility model was tested using analysis of variance and Spearman's correlations. General practice service spatial equity was assessed by comparing clusters of high access versus need, and quantified through the Gini coefficient.

FINDINGS

Ambulatory sensitive hospitalization (ASH) rates were significantly correlated with all indicators. Health needs were significantly clustered, but incorporating indicator weightings into the spatial accessibility analysis did not impact accessibility scores. A misalignment of access and need, and a Gini coefficient of 0.281 suggest that services are not equitably distributed.

CONCLUSION

ASH rates seem a robust indicator of health need. However, data access issues may restrict their use. Area-level socioeconomic deprivation measures incorporate some social determinants of health, and they have potential for wider use. High need clusters vary spatially according to the indicator used. GIS techniques can identify "hot-spots" of need, but these can be masked in accessibility models.

Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis

BACKGROUND

Extreme heat poses current and future risks to human health. Heat vulnerability indices (HVIs), commonly developed using principal components analysis (PCA), are mapped to identify populations vulnerable to extreme heat. Few studies critically assess implications of analytic choices made when employing this methodology for fine-scale vulnerability mapping.

OBJECTIVE

We investigated sensitivity of HVIs created by applying PCA to input variables and whether training input variables on heat-health data produced HVIs with similar spatial vulnerability patterns for Detroit, Michigan, USA.

METHODS

We acquired 2010 Census tract and block group level data, land cover data, daily ambient apparent temperature, and all-cause mortality during May-September, 2000-2009. We used PCA to construct HVIs using: a) "unsupervised"-PCA applied to variables selected a priori as risk factors for heat-related health outcomes; b) "supervised"-PCA applied only to variables significantly correlated with proportion of all-cause mortality occurring on extreme heat days (i.e., days with 2-d mean apparent temperature above month-specific 95th percentiles).

RESULTS

Unsupervised and supervised HVIs yielded differing spatial vulnerability patterns, depending on selected land cover input variables. Supervised PCA explained 62% of variance in the input variables and was applied on half the variables used in the unsupervised method. Census tract-level supervised HVI values were positively associated with increased proportion of mortality occurring on extreme heat days; supervised PCA could not be applied to block group data. Unsupervised HVI values were not associated with extreme heat mortality for either tracts or block groups.

DISCUSSION

HVIs calculated using PCA are sensitive to input data and scale. Supervised HVIs may provide marginally more specific indicators of heat vulnerability than unsupervised HVIs. PCA-derived HVIs address correlation among vulnerability indicators, although the resulting output requires careful contextual interpretation beyond generating epidemiological research questions. Methods with reliably stable outputs should be leveraged for prioritizing heat interventions. https://doi.org/10.1289/EHP4030.

Exploring spatial dependencies in the prevalence of childhood diarrhea in Mozambique using global and local measures of spatial autocorrelation

Background: Diarrhea in children under 5 years is generally considered as an important public health problem and the major cause of childhood morbidity and mortality in Sub-Saharan Africa. The purpose of this study was to present exploratory spatial data analyses to identify spatial clusters and outliers in the prevalence of childhood diarrhea in Mozambique. Methods: Using data from 2011 Mozambique Demographic and Health Survey, we calculated the prevalence of childhood diarrhea on the district level. Two exploratory spatial data analyses methods were applied, namely, global and local Moran's I statistics, providing spatial autocorrelation and spatial clusters/outlier in the prevalence of childhood diarrhea, respectively. Results: Choropleth mapping and global Moran's I statistics showed that the prevalence of childhood diarrhea has clustered distribution across the study area. A local Moran's I index revealed spatial clusters within the province of Tete, Gaza, Cabo Delgado, and Zambezia. However, spatial outliers were confined within Sofala province. Conclusion: The exploratory spatial data analyses showed various spatial clustering and outliers present in the prevalence of childhood diarrhea, indicating interventions needed in targeted regions.

Non-compensatory aggregation method to measure social and material deprivation in an urban area: relationship with premature mortality

Health inequalities exist between nations, regions, and even smaller units. In societies where social and economic structures change rapidly and continuously, analysis of health socioeconomic determinants plays a fundamental role to provide proper policy answers. This study aims to measure accurately two different conceptions of deprivation by developing two different indexes using non-compensatory among sub-indicators aggregation methods. The proposed indicators are compared with premature mortality to verify deprivation's effect on health status. The results show that materially deprived areas are not necessarily socially deprived and vice versa. Material deprivation has a positive statistical co-graduation with premature mortality, while social deprivation has no association with premature mortality.

A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma

BACKGROUND

Ecologic health studies often rely on outcomes from health service utilization data that are limited by relatively coarse spatial resolutions and missing geographic information, particularly neighborhood level identifiers. When fine-scale geographic data are missing, the ramifications and strategies for addressing them are not well researched or developed. This study illustrates a novel spatio-temporal framework that combines a geographic identifier assignment (i.e., geographic imputation) algorithm with predictive Bayesian variable selection to identify neighborhood factors associated with disparities in emergency department (ED) visits for asthma.

METHODS

ED visit records with missing fine-scale spatial identifiers (~ 20%) were geocoded using information from known, coarser, misaligned spatial units using an innovative geographic identifier assignment algorithm. We then employed systematic variable selection in a spatio-temporal Bayesian hierarchical model (BHM) predictive framework within the NIMBLE package in R. Our novel methodology is illustrated in an ecologic case study aimed at identifying neighborhood-level predictors of asthma ED visits in South Carolina, United States, from 1999 to 2015. The health outcome was annual ED visit counts in small areas (i.e., census tracts) with primary diagnoses of asthma (ICD9 codes 493.XX) among children ages 5 to 19 years.

RESULTS

We maintained 96% of ED visit records for this analysis. When the algorithm used areal proportions as probabilities for assignment, which addressed differential missingness of census tract identifiers in rural areas, variable selection consistently identified significant neighborhood-level predictors of asthma ED visit risk including pharmacy proximity, average household size, and carbon monoxide interactions. Contrasted with common solutions of removing geographically incomplete records or scaling up analyses, our methodology identified critical differences in parameters estimated, predictors selected, and inferences. We posit that the differences were attributable to improved data resolution, resulting in greater power and less bias. Importantly, without this methodology, we would have inaccurately identified predictors of risk for asthma ED visits, particularly in rural areas.

CONCLUSIONS

Our approach innovatively addressed several issues in ecologic health studies, including missing small-area geographic information, multiple correlated neighborhood covariates, and multiscale unmeasured confounding factors. Our methodology could be widely applied to other small-area studies, useful to a range of researchers throughout the world.

Socioeconomic deprivation and blood lipids in first-episode psychosis patients with minimal antipsychotic exposure: Implications for cardiovascular risk

BACKGROUND

The influence of socioeconomic deprivation on the cardiovascular health of patients with psychosis-spectrum disorders (PSD) has not been investigated despite the growing recognition of social factors as determinants of health, and the disproportionate rates of cardiovascular mortality observed in PSD. Discordant results have been documented when studying dyslipidemia -a core cardiovascular risk factor- in first-episode psychosis (FEP), before chronic exposure to antipsychotic medications. The objective of the present study is to determine the extent to which socioeconomic deprivation affects blood lipids in patients with FEP, and examine its implications for cardiovascular risk in PSD.

METHODS

Linear regression models, controlling for age, sex, exposure to pharmacotherapy, and physical anergia, were used to test the association between area-based measures of material and social deprivation and blood lipid levels in a sample of FEP patients (n = 208).

RESULTS

Social, but not material deprivation, was associated with lower levels of total and HDL cholesterol. This effect was statistically significant in patients with affective psychoses, but not in schizophrenia-spectrum disorders.

CONCLUSIONS

Contrary to other reports from the literature, the relationship between socioeconomic disadvantage and blood lipid levels was contingent on the social rather than the material aspects of deprivation. Furthermore, this association also depended on the main diagnostic category of psychosis, suggesting a complex interaction between the environment, psychopathology, and physical health. Future studies exploring health issues in psychosis might benefit from taking these associations into consideration. A better understanding of the biology of blood lipids in this context is necessary.

Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors

Mapping suitable land for development is essential to land use planning efforts that aim to model, anticipate, and manage trade-offs between economic development and the environment. Previous land suitability assessments have generally focused on a few development sectors or lack consistent methodologies, thereby limiting our ability to plan for cumulative development pressures across geographic regions. Here, we generated 1-km spatially-explicit global land suitability maps, referred to as "development potential indices" (DPIs), for 13 sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). To do so, we applied spatial multi-criteria decision analysis techniques that accounted for both resource potential and development feasibility. For each DPI, we examined both uncertainty and sensitivity, and spatially validated the map using locations of planned development. We illustrate how these DPIs can be used to elucidate potential individual sector expansion and cumulative development patterns.

Small-area socioeconomic deprivation indices in Cyprus: development and association with premature mortality

BACKGROUND

Area-level measures of socioeconomic deprivation are important for understanding and describing health inequalities. The aim of this study was the development and validation of a small-area index of socioeconomic deprivation for Cypriot communities and the investigation of its association with the spatial distribution of all-cause premature adult mortality.

METHODS

Six area-level socioeconomic indicators were used from the 2011 national population census (low educational attainment, unemployment, not owner occupied household, single-person household, divorced or widowed and single-parent households). After normalization and standardization of the geographically smoothed indicators, Principal Component Analysis (PCA) was used to construct indicator weights. The association between deprivation indices and the spatial distribution of all-cause premature adult mortality was estimated in Poisson log-linear spatial models.

RESULTS

PCA resulted in two principal components explaining the 65.7% of the total variance. The first principal component included four indicators (low educational attainment, single-person households, divorced or widowed and single-parent households, the latter however with a negative loading) and it thought more likely to capture rural-related aspects of deprivation. The second principal component included the other two indicators (unemployment and not owner occupied households) and it is more likely to capture urban-related aspects of material deprivation. Restricting the analysis in the metropolitan areas of the island resulted in a different set of indicators for the urban-specific deprivation index. All developed indices were linearly associated with all-cause premature adult mortality. The all-cause premature adult mortality increased by 17% per 1 standard deviation (SD) increase in rural-related socioeconomic deprivation (95% CrI: 8-27%) and 8% per 1 SD increase in urban-related aspects of material deprivation (95% CrI: 3-15%) in the nationwide analysis and 9% per 1 SD increase in urban-specific socioeconomic deprivation (95% CrI: 4-15%) across metropolitan areas.

CONCLUSIONS

The results of this study demonstrate that a set of small-area indices of socioeconomic deprivation across Cypriot communities have good construct and predictive validity. However, the study indicates that different aspects of socioeconomic deprivation may be important in rural and urban areas in Cyprus. The developed socioeconomic deprivation indices could offer a valid new tool for Cypriot public health research and policy in terms of identifying areas in greatest need, guiding resource allocation and developing area-targeted public health programmes and policies.

Mapping heat-related health risks of elderly citizens in mountainous area: A case study of Chongqing, China

Heat wave becomes a leading cause of weather-related illness and death across the world under the background of climate change, urban heat island, and population ageing. Heat health risk assessment is an important starting point for heat-related morbidity and mortality reduction within the risk governance framework. Chongqing, a mountainous municipality with a fast rate of population ageing in China, was selected as a case study for mapping the heat health risk of the elderly population at a raster scale. The results indicated that the high heat hazard and human exposure areas were mainly distributed in the metropolitan areas, which largely resulted in high heat health risk in the urban areas. However, the high heat vulnerability pixels were mainly concentrated at the remote mountainous regions which have broken terrains and low socioeconomic statuses. Compared with traditional general heat risk indicator, this new model can provide more targeted spatial information to decision makers, and is helpful to improve the flexibility and comparability of heat risk assessment tool. Furthermore, this new model is particularly valuable for quantifying heat health risk in developing countries with limited open access data.

Population Health Inequalities Across and Within European Metropolitan Areas through the Lens of the EURO-HEALTHY Population Health Index

The different geographical contexts seen in European metropolitan areas are reflected in the uneven distribution of health risk factors for the population. Accumulating evidence on multiple health determinants point to the importance of individual, social, economic, physical and built environment features, which can be shaped by the local authorities. The complexity of measuring health, which at the same time underscores the level of intra-urban inequalities, calls for integrated and multidimensional approaches. The aim of this study is to analyse inequalities in health determinants and health outcomes across and within nine metropolitan areas: Athens, Barcelona, Berlin-Brandenburg, Brussels, Lisbon, London, Prague, Stockholm and Turin. We use the EURO-HEALTHY Population Health Index (PHI), a tool that measures health in two components: Health Determinants and Health Outcomes. The application of this tool revealed important inequalities between metropolitan areas: Better scores were found in Northern cities when compared with their Southern and Eastern counterparts in both components. The analysis of geographical patterns within metropolitan areas showed that there are intra-urban inequalities, and, in most cities, they appear to form spatial clusters. Identifying which urban areas are measurably worse off, in either Health Determinants or Health Outcomes, or both, provides a basis for redirecting local action and for ongoing comparisons with other metropolitan areas.

Application of socio-economic-health deprivation index, analysis of mortality and influenza vaccination coverage in the elderly population of Tuscany

Objective

The aim of this paper is to describe the results obtained from the application of a specific local deprivation index, to general and cause-specific mortality and influenza vaccination coverage among elderly people in the municipality of Florence.

Methods

General and cause-specific mortality data (2009-2013) and influenza vaccination coverage data (2015/16 and 2016/17) were collected for subjects aged ≥ 65 years residing in the municipality of Florence (Tuscany), at the 2011 Census section level. A Socio-Economic and Health Deprivation Index (SEHDI) was constructed and validated by means of socio-economic indicators and mortality ratios.

Results

Half of the population of Florence belonged to the medium deprivation group; about 25% fell into the two most deprived groups, and the remaining 25% were deemed to be wealthy. Elderly people mostly belonged to the high deprivation group. All-cause mortality and cause-specific mortality (cancer and respiratory diseases) reached their highest values in the high deprivation group. Influenza vaccination coverage (VC) was 54.7% in the 2015/16 and 2016/17 seasons, combined. VC showed a linear rising trend as deprivation increased and appeared to be correlated with different factors in the different deprivation groups.

Conclusions

As socio-economic deprivation plays an important role in health choices, application of the SEHDI enables us to identify the characteristics of the main sub-groups of the population with low adherence to influenza vaccination. The results of the present study should be communicated to General Practitioners, in order to help them to promote influenza vaccination among their patients.

[Indices of Multiple Deprivation for the analysis of regional health disparities in Germany : Experiences from epidemiology and healthcare research]

BACKGROUND

Deprivation indices allow material and social differences at the regional level to be described in a statistically efficient and concise manner and to use these in health analyses. Following the British example, Indices of Multiple Deprivation (IMDs) are now available for Germany, the German Index of Multiple Deprivation (GIMD) as well as its regional versions. In this study, empirical experiences based on the use of these indices in health studies will be presented.

METHOD

The German IMDs consist of seven deprivation domains, which represent single aspects of deprivation (income, employment, and educational deprivation, municipal revenue deprivation, social capital deprivation, environment and security deprivation). Specific indicators were generated from data of official statistics and assigned to the deprivation domains. The weighted single domains were finally combined to an overall index. The German IMDs are available at a municipal level and at a district level.

RESULTS

Analyses using the IMDs showed significant associations between regional deprivation and mortality, morbidity and aspects of health services research. Multilevel analyses showed significant associations with regional deprivation, independent of individual factors.

CONCLUSIONS

The German IMDs are valid and efficient tools for the use in epidemiology and health services research, but also for health policy. When constructing deprivation indices, several methodological challenges have to be considered.

Individual and area-level determinants associated with C-reactive protein as a marker of cardiometabolic risk among adults: Results from the German National Health Interview and Examination Survey 2008-2011

BACKGROUND

High-sensitivity C-reactive protein (hsCRP) is a sensitive biomarker of systemic inflammation and is related to the development and progression of cardiometabolic diseases. Beyond individual-level determinants, characteristics of the residential physical and social environment are increasingly recognized as contextual determinants of systemic inflammation and cardiometabolic risks. Based on a large nationwide sample of adults in Germany, we analyzed the cross-sectional association of hsCRP with residential environment characteristics. We specifically asked whether these associations are observed independent of determinants at the individual level.

METHODS

Data on serum hsCRP levels and individual sociodemographic, behavioral, and anthropometric characteristics were available from the German Health Interview and Examination Survey for Adults (2008-2011). Area-level variables included, firstly, the predefined German Index of Socioeconomic Deprivation (GISD) derived from the INKAR (indicators and maps on spatial and urban development in Germany and Europe) database and, secondly, population-weighted annual average concentration of particulate matter (PM10) in ambient air provided by the German Environment Agency. Associations with log-transformed hsCRP levels were analyzed using random-intercept multi-level linear regression models including 6,768 participants aged 18-79 years nested in 162 municipalities.

RESULTS

No statistically significant association of PM10 exposure with hsCRP was observed. However, adults residing in municipalities with high compared to those with low social deprivation showed significantly elevated hsCRP levels (change in geometric mean 13.5%, 95%CI 3.2%-24.7%) after adjusting for age and sex. The observed relationship was independent of individual-level educational status. Further adjustment for smoking, sports activity, and abdominal obesity appeared to markedly reduce the association between area-level social deprivation and hsCRP, whereas all individual-level variables contributed significantly to the model.

CONCLUSIONS

Area-level social deprivation is associated with higher systemic inflammation and the potentially mediating role of modifiable risk factors needs further elucidation. Identifying and assessing the source-specific harmful components of ambient air pollution in population-based studies remains challenging.

Estimation of the Ecological Fallacy in the Geographical Analysis of the Association of Socio-Economic Deprivation and Cancer Incidence

Ecological deprivation indices at the level of spatial units are often used to measure and monitor inequalities in health despite the possibility of ecological fallacy. For the purpose of this study, the European Deprivation Index (EDI) was used, which is based on Townsend theorization of relative deprivation. The Slovenian version of EDI (SI-EDI) at the aggregated level (SI-EDI-A) was calculated to the level of the national assembly polling stations. The SI-EDI was also calculated at the individual level (SI-EDI-I) by the method that represents a methodological innovation. The degree of ecological fallacy was estimated with the Receiver Operating Characteristics (ROC) curves. By calculating the area under the ROC curve, the ecological fallacy was evaluated numerically. Agreement between measuring deprivation with SI-EDI-A and SI-EDI-I was analysed by graphical methods and formal testing. The association of the socio-economic status and the cancer risk was analysed in all first cancer cases diagnosed in Slovenia at age 16 and older in the period 2011⁻2013. Analysis was done for each level separately, for SI-EDI-I and for SI-EDI-A. The Poisson regression model was implemented in both settings but adapted specifically for aggregated and individual data. The study clearly shows that ecological fallacy is unavoidable. However, although the association of cancer incidence and socio-economic deprivation at individual and aggregated levels was not the same for all cancer sites, the results were very similar for the majority of investigated cancer sites and especially for cancers associated with unhealthy lifestyles. The results confirm the assumptions from authors' previous research that using the level of the national assembly polling stations would be the acceptable way to aggregate data when explaining inequalities in health in Slovenia in ecological studies.

Individual- and area-level socioeconomic inequalities in diabetes mellitus in Saskatchewan between 2007 and 2012: a cross-sectional analysis

BACKGROUND

Improving our understanding of social inequalities may improve prevention and treatment efforts for diabetes mellitus. We examined the association between individual- and area-level socioeconomic measures and physician-diagnosed diabetes in Saskatchewan over time.

METHODS

In this cross-sectional study, we linked health administrative data with individual-level socioeconomic data from the Canadian Community Health Survey and area-level data from the 2006 Canadian census. We used general linear mixed-models regression to analyze the effect of each factor, controlling for geographic and demographic measures.

RESULTS

Area-level deprivation was associated with medically diagnosed type 2 diabetes mellitus after adjustment for the individual-level factors of age, sex, household income and education. Individuals residing in areas ranked in the least deprived quintile had a lower likelihood of diabetes than those in the most deprived quintile (odds ratio 0.40, 95% confidence interval 0.18-0.88). However, this disparity existed only in urban areas. This result may reflect less pronounced health inequalities in rural areas, greater socioeconomic heterogeneity, larger geographic units or some combination of these factors.

INTERPRETATION

Individual- and area-level socioeconomic factors were associated with the likelihood of medically diagnosed diabetes; however, the strength of this association varied between urban and rural communities. Acknowledgement of area-level deprivation as a modifiable risk factor related to the prevalence of diabetes is important in the development of effective interventions for urban, but not rural, areas.

Socioeconomic Differences and Lung Cancer Survival-Systematic Review and Meta-Analysis

Background: The impact of socioeconomic differences on cancer survival has been investigated for several cancer types showing lower cancer survival in patients from lower socioeconomic groups. However, little is known about the relation between the strength of association and the level of adjustment and level of aggregation of the socioeconomic status measure. Here, we conduct the first systematic review and meta-analysis on the association of individual and area-based measures of socioeconomic status with lung cancer survival.

Methods: In accordance with PRISMA guidelines, we searched for studies on socioeconomic differences in lung cancer survival in four electronic databases. A study was included if it reported a measure of survival in relation to education, income, occupation, or composite measures (indices). If possible, meta-analyses were conducted for studies reporting on individual and area-based socioeconomic measures.

Results: We included 94 studies in the review, of which 23 measured socioeconomic status on an individual level and 71 on an area-based level. Seventeen studies were eligible to be included in the meta-analyses. The meta-analyses revealed a poorer prognosis for patients with low individual income (pooled hazard ratio: 1.13, 95 % confidence interval: 1.08–1.19, reference: high income), but not for individual education. Group comparisons for hazard ratios of area-based studies indicated a poorer prognosis for lower socioeconomic groups, irrespective of the socioeconomic measure. In most studies, reported 1-, 3-, and 5-year survival rates across socioeconomic status groups showed decreasing rates with decreasing socioeconomic status for both individual and area-based measures. We cannot confirm a consistent relationship between level of aggregation and effect size, however, comparability across studies was hampered by heterogeneous reporting of socioeconomic status and survival measures. Only eight studies considered smoking status in the analysis.

Conclusions: Our findings suggest a weak positive association between individual income and lung cancer survival. Studies reporting on socioeconomic differences in lung cancer survival should consider including smoking status of the patients in their analysis and to stratify by relevant prognostic factors to further explore the reasons for socioeconomic differences. A common definition for socioeconomic status measures is desirable to further enhance comparisons between nations and across different levels of aggregation.

A Raster-Based Subdividing Indicator to Map Urban Heat Vulnerability: A Case Study in Sydney, Australia

Assessing and mapping urban heat vulnerability has developed significantly over the past decade. Many studies have mapped urban heat vulnerability with a census unit-based general indicator (CGI). However, this kind of indicator has many problems, such as inaccurate assessment results and lacking comparability among different studies. This paper seeks to address this research gap and proposes a raster-based subdividing indicator to map urban heat vulnerability. We created a raster-based subdividing indicator (RSI) to map urban heat vulnerability from 3 aspects: exposure, sensitivity and adaptive capacity. We applied and compared it with a raster-based general indicator (RGI) and a census unit-based general indicator (CGI) in Sydney, Australia. Spatial statistics and analysis were used to investigate the performance among those three indicators. The results indicate that: (1) compared with the RSI framework, 67.54% of very high heat vulnerability pixels were ignored in the RGI framework; and up to 83.63% of very high heat vulnerability pixels were ignored in the CGI framework; (2) Compared with the previous CGI framework, a RSI framework has many advantages. These include more accurate results, more flexible model structure, and higher comparability among different studies. This study recommends using a RSI framework to map urban heat vulnerability in the future.

The development of an index of rural deprivation: A case study of Norfolk, England

Geographical deprivation indices such as the English Index of Multiple Deprivation (IMD) have been widely used in healthcare research and planning since the mid-1980s. However, such indices normally provide a measure of disadvantage for the whole population and can be inflexible to adaptation for specific geographies or purposes. This can be an issue, as the measurement of deprivation is subjective and situationally relative, and the type of deprivation experienced within rural areas may differ from that experienced by urban residents. The objective of this study was to develop a Rural Deprivation Index (RDI) using the English county of Norfolk as a case study, but with a view to adopting a flexible approach that could be used elsewhere. It is argued that the model developed in this research gives clarity to the process of populating an index and weighting it for a specific purpose such as rural deprivation. This is achieved by 'bundling' highly correlated indicators that are applicable to both urban and rural deprivation into one domain, and creating a separate domain for indicators relevant to the setting of interest, in this case rural areas. A further domain is proposed to account for population differences in rural areas. Finally, a method was developed to measure variability in deprivation within small areas. The RDI results in more rural areas in Norfolk falling in the most deprived quintile, particularly those classified as 'Rural town and fringe in sparse settings'; these areas also have high levels of heterogeneity of deprivation when using the variability measure created. This model proposed has the potential to provide a starting point for those who wish to create a summary deprivation measure taking into account rurality, or other local geographic factors, and as part of a range of approaches that can be used to allocate, or apply for, resources.

A Closer Look at the Bivariate Association between Ambient Air Pollution and Allergic Diseases: The Role of Spatial Analysis

Although previous ecological studies investigating the association between air pollution and allergic diseases accounted for temporal or seasonal relationships, few studies address spatial non-stationarity or autocorrelation explicitly. Our objective was to examine bivariate correlation between outdoor air pollutants and the prevalence of allergic diseases, highlighting the limitation of a non-spatial correlation measure, and suggesting an alternative to address spatial autocorrelation. The 5-year prevalence data (2011⁻2015) of allergic rhinitis, atopic dermatitis, and asthma were integrated with the measures of four major air pollutants (SO₂, NO₂, CO, and PM₁₀) for each of the 423 sub-districts of Seoul. Lee's L statistics, which captures how much bivariate associations are spatially clustered, was calculated and compared with Pearson's correlation coefficient for each pair of the air pollutants and allergic diseases. A series of maps showing spatiotemporal patterns of allergic diseases at the sub-district level reveals a substantial degree of spatial heterogeneity. A high spatial autocorrelation was observed for all pollutants and diseases, leading to significant dissimilarities between the two bivariate association measures. The local L statistics identifies the areas where a specific air pollutant is considered to be contributing to a type of allergic disease. This study suggests that a bivariate correlation measure between air pollutants and allergic diseases should capture spatially-clustered phenomenon of the association, and detect the local instability in their relationships. It highlights the role of spatial analysis in investigating the contribution of the local-level spatiotemporal dynamics of air pollution to trends and the distribution of allergic diseases.