Building spatial composite indicators to analyze environmental health inequalities on a regional scale

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.

Comparing Global and Spatial Composite Measures of Neighborhood Socioeconomic Status Across US Counties

Area-level neighborhood socioeconomic status (NSES) is often measured without consideration of spatial autocorrelation and variation. In this paper, we compared a non-spatial NSES measure to a spatial NSES measure for counties in the USA using principal component analysis and geographically weighted principal component analysis (GWPCA), respectively. We assessed spatial variation in the loadings using a Monte Carlo randomization test. The results indicated that there was statistically significant variation (p = 0.004) in the loadings of the spatial index. The variability of the census variables explained by the spatial index ranged from 60 to 90%. We found that the first geographically weighted principal component explained the most variability in the census variables in counties in the Northeast and the West, and the least variability in counties in the Midwest. We also tested the two measures by assessing the associations with county-level diabetes prevalence using data from the CDC's US Diabetes Surveillance System. While associations of the two NSES measures with diabetes did not differ for this application, the descriptive results suggest that it might be important to consider a spatial index over a global index when constructing national county measures of NSES. The spatial approach may be useful in identifying what factors drive the socioeconomic status of a county and how they vary across counties. Furthermore, we offer suggestions on how a GWPCA-based NSES index may be replicated for smaller geographic scopes.

Inequalities in Health: Methodological Approaches to Spatial Differentiation

The prevalence of inequalities in the general health position of communities can be assessed by using selected determinants. The aims of this article are three-fold: (1) to apply a comprehensive approach to the assessment of inequalities in the general health position of communities, (2) to determine the spatial differentiation of determinants, and (3) to present selected assessment methods and their impact on the results. To present a quantitative assessment of these inequalities in health status in communities, a composite indicator (Health Index) was developed. This Health Index is composed of 8 areas of evaluation and 60 indicators which include, amongst others, determinants of health status and healthcare at district level (LAU 1) in the Czech Republic. The data are evaluated using multicriteria decision-making methods (the WSA and TOPSIS methods). Findings suggest that, when all eight domains are assigned the same weight of one, the spatial differentiation among the districts is similar when using both methods. If different weightings are assigned to the districts, changes occur in both the index values and the rankings of the analyzed districts. For example, the allocation of weightings in both methods results in a rearrangement of the ranking of districts for which the Health Index is around the average.

Creating a Composite Index to Target Recruitment of UK Students from Areas of Low Participation in Higher Education

This research presents a framework through which a new Higher Education Access and Deprivation (HEAD) composite index was created to assist UK universities in efficiently recruiting and supporting students from areas with traditionally low engagement rates in higher education. The index was designed to be easily adaptable by university staff to suit their own work priorities and/or an institution's strategic requirements by utilising open socio-demographic data and construction techniques that require minimal technical statistical skills. Although Cornwall was used as the study area in this research, this index has been designed such that it can be readily applied elsewhere. Two differently weighted models were created using the framework - one with equal weights and the other based on the frequency the constituent indicators appeared in the reviewed academic literature. Both models were mapped across Cornwall, identifying areas of deprivation at a finer resolution than under the current widely used Participation of Local Areas (POLAR) methodology. The weighted model performed marginally better than both the equal weighted model and the current POLAR methodology when validated against external data, and additionally it worked well in both rural and urban environments leading to it being selected as the new HEAD index. The HEAD index maps smaller scale areas of deprivation than previously available, and by enabling users to investigate the underlying socio-demographic characteristics of an area, it also allows universities to create interventions, support, and policies that best targets the students they aim to recruit and teach.

How can we analyze environmental health resilience and vulnerability? A joint analysis with composite indices applied to the north of France

In environmental health, vulnerability reflecting the cumulative harmful constraints and nuisances to which populations are subjected and resilience defined as the capacity of a territory to cope with health inequalities have been little extensively investigated together with the same importance. Besides the diversity of factors involved, there is no consensual framework to develop composite indices, one recognized methodology to deal with a multifaceted issue. Therefore, this research aims to establish a new transferable approach to assess the spatial heterogeneity of territorial inequalities. This new strategy relies on the simultaneous evaluation of resilience and vulnerability and the joint analysis based on the cross-interpretation of the spatialized composite indices of resilience and vulnerability. A case study was conducted to demonstrate the feasibility of this methodology, using the municipality as a spatial unit of analysis within a region in the north of France. To provide the most holistic description possible of the 3817 studied municipalities, 50 variables related to the economic, environment, policy, health, services and social dimensions were used to develop the composite indices. The vulnerability Index has a median value of 0.151 with an IQR of [0.126-0.180] and the Resilience Index has a median value of 0.341 with an IQR of [0.273-0.401]. The joint analysis was conducted to classify each municipality among four defined typologies: 1687 municipalities (44.2%) belong to the "To monitor" category, 1646 (43.1%) to the "Resilient" category, 329 (8.6%) to the "Have resources" category and 155 (4.1%) to the "Territorial blackspot" category. The methodology herein may be a diagnostic tool to identify and prioritize municipalities that could benefit from the implementation of specifically tailored public health policies.

Development of a spatial web tool to identify hotspots of environmental burdens in Wallonia (Belgium)

In response to the Walloon Environment and Health Program, the Scientific Institute of Public Services (ISSeP) developed an integrated approach of environmental exposure assessment in the Walloon region, Belgium. The study presents an index-based approach to estimate the multiple environmental burdens at regional level and detailed local resolution. Indicators are based on environmental measurements of pollutants in ambient air and soil, and on stressors for citizens related to noise and radon. These indicators were mapped as proportions to obtain an accurate comparison between spatial units. In order to indicate the need for intervention, environmental indicators are calculated as the proportion of areas where the level of detrimental environmental factors exceeds threshold values from WHO guidelines and Walloon legal threshold values. In parallel, a spatial web tool based on GIS was developed to enable a flexible and weighted combination of the normalized indicators by computing the resulting composite index online. This interactive web tool designed for policy makers and experts eases the spatial analysis of results in order to identify geographic areas where hotspot exposures are a potential risk to human health. The next steps of this work aim to integrate more environmental indicators (stressors and benefits) and some sociodemographic and health indicators in order to detect vulnerable populations. A holistic assessment is essential to inform environmental justice debates and to ensure a health conducive equal environment. Finally, this environmental health tool will support decision makers focus resources and programs to improve the environmental health of Walloons living in areas disproportionately burdened by multiple sources of pollution.

B. Valadas SY, Puppim-Buzanovsky L, Rocha F, Sanchez-Vazquez MJ. SisLeish: A multi-country standardized information system to monitor the status of Leishmaniasis in the Americas

BACKGROUND

In the Americas, leishmaniasis is endemic in 18 countries, and from 2001 through 2015, 17 countries reported 843,931 cases of cutaneous and mucocutaneous leishmaniasis, and 12 countries reported 52,176 cases of visceral leishmaniasis. A Regional Information System (SisLeish) was created in order to provide knowledge of the distribution and tendency of this disease to analyze and monitor the leishmaniasis status. This article analyses the performance and progress of SisLeish from 2012-2015.

METHODOLOGY

The performance of SisLeish was evaluated by country adhesion, data completeness and delay in entering the data, and also by the SWOT technique. Furthermore, we outlined the structure and modus operandi of the system and indicators utilized.

RESULTS

In 2012, only 18% of the countries entered the data in SisLeish before the deadline, where 66.7% and 50% of the countries with autochthonous CL/ML and VL reported their cases to the system, respectively. Whereas in 2015, 59% of the countries reached the deadline, where 94.4% and 58.3% of the countries reported their CL/ML and VL data, respectively. Regarding data completeness, there was great progress for different variables since its launch, such as gender, which had an approximately 100% improvement from 2012 to 2015. The SWOT analysis of SisLeish showed 12 strengths, 11 opportunities, seven weaknesses and six threats.

CONCLUSIONS

From 2012-2015 there has been an improvement in the adhesion, quality and data completeness, showing the effort of the majority of the countries to enhance their national database. The SWOT analysis demonstrated that strengths and opportunities exceed weaknesses and threats; however, it highlighted the system frailties and challenges that need to be addressed. Furthermore, it has stimulated several National Programs to advance their surveillance system. Therefore, SisLeish has become an essential tool to prioritize areas, assist in decision-making processes, and to guide surveillance and control actions.

Devising a Composite Index to Analyze and Model Loneliness and Related Health Risks in the United Kingdom

Objective:This research presents a framework through which a spatial composite index is devised to determine areas of potential loneliness and associated health risks. The research is evidenced on the London borough of Southwark in the United Kingdom but is designed such that it could be applied more widely. Method: The work adopts a quantitative approach through the combination of census and accessibility variables at a small area level. The output is a scoring system whereby each area is assigned a value indicating the likely presence of loneliness and potentially corresponding health risks. Results: Findings imply that loneliness is quantifiable and that this correlates with socioeconomic and accessibility measures. A strong clustering is evident in Southwark. Discussion: This research builds on previous attempts to locate and quantify loneliness with favorable results. The outcome provides a replicable solution to assist the public service with the targeting of areas deemed most at risk from loneliness and resultant mental and physical health conditions at a time when such issues are high on the political agenda.

Local Geographic Variation of Public Services Inequality: Does the Neighborhood Scale Matter?

This study aims to explore the effect of the neighborhood scale when estimating public services inequality based on the aggregation of social, environmental, and health-related indicators. Inequality analyses were carried out at three neighborhood scales: the original census blocks and two aggregated neighborhood units generated by the spatial "k"luster analysis by the tree edge removal (SKATER) algorithm and the self-organizing map (SOM) algorithm. Then, we combined a set of health-related public services indicators with the geographically weighted principal components analyses (GWPCA) and the principal components analyses (PCA) to measure the public services inequality across all multi-scale neighborhood units. Finally, a statistical test was applied to evaluate the scale effects in inequality measurements by combining all available field survey data. We chose Quito as the case study area. All of the aggregated neighborhood units performed better than the original census blocks in terms of the social indicators extracted from a field survey. The SKATER and SOM algorithms can help to define the neighborhoods in inequality analyses. Moreover, GWPCA performs better than PCA in multivariate spatial inequality estimation. Understanding the scale effects is essential to sustain a social neighborhood organization, which, in turn, positively affects social determinants of public health and public quality of life.