Exposure Datasets Research Articles

Developing an online tool for identifying at-risk populations to wildfire smoke hazards

Wildfire episodes pose a significant public health threat in the United States. Adverse health impacts associated with wildfires occur near the burn area as well as in places far downwind due to wildfire smoke exposures. Health effects associated with exposure to particulate matter arising from wildfires can range from mild eye and respiratory tract irritation to more serious outcomes such as asthma exacerbation, bronchitis, and decreased lung function. Real-time operational forecasts of wildfire smoke concentrations are available but they are not readily integrated with information on vulnerable populations necessary to identify at-risk communities during wildfire smoke episodes. Efforts are currently underway at the Centers for Disease Control and Prevention (CDC) to develop an online tool that utilizes short-term predictions and forecasts of smoke concentrations and integrates them with measures of population-level vulnerability for identifying at-risk populations to wildfire smoke hazards. The tool will be operationalized on a national scale, seeking input and assistance from several academic, federal, state, local, Tribal, and Territorial partners. The final product will then be incorporated into CDC's National Environmental Public Health Tracking Network (http://ephtracking.cdc.gov), providing users with access to a suite of mapping and display functionalities. A real-time vulnerability assessment tool incorporating standardized health and exposure datasets, and prevention guidelines related to wildfire smoke hazards is currently unavailable for public health practitioners and emergency responders. This tool could strengthen existing situational awareness competencies, and expedite future response and recovery efforts during wildfire episodes.

Managing Counterparty Risk in OTC Markets

We study how banks manage their default risk before bilaterally negotiating the quantities and prices of over-the-counter (OTC) contracts resembling credit default swaps (CDSs). We show that the costly actions exerted by banks to reduce their default probabilities are not socially optimal. Depending on the imposed trade size limits, risk-management costs and sellers' bargaining power, banks may switch from choosing default risk levels above the social optimum to reducing them even below the social optimum. We use a unique and comprehensive data set of bilateral exposures from the CDS market to test the main model implications on the OTC market structure: (i) intermediation is done by low-risk banks with medium credit exposure; (ii) all banks with high credit exposures are net buyers of CDSs, and low-risk banks with low credit exposures are the main net sellers; and (iii) heterogeneity in post-trade credit exposures is higher for riskier banks and smaller for safer banks.

The Decline of Solvency Contagion Risk

We study solvency contagion risk in the UK banking system from 2008 to 2015. We develop a model that only accounts for losses transmitted after banks default, but also for losses due to the fact that creditors revalue their exposures when probabilities of default of their counterparties change. We apply our model to a unique data set of real UK interbank exposures. We show that risks due to solvency contagion decrease markedly from the peak of the crisis to the present, to the point of becoming negligible. By decomposing the change in losses into two main contributions — the increase in banks’ capital and the decrease in interbank exposures — we are able to pinpoint the main driver in each year. In some cases we observe that an increase in aggregate capital is associated with a positive contribution to losses. This suggests that the distribution of capital among banks is also important.

How Does Risk Flow in the Credit Default Swap Market?

We develop a framework to analyse the Credit Default Swaps (CDS) market as a network of risk transfers among counter-parties. From a theoretical perspective, we introduce the notion of flow-of-risk and provide sufficient conditions for a bow-tie network architecture to endogenously emerge as a result of intermediation. This architecture shows three distinct sets of counter-parties: i) Ultimate Risk Sellers (URS), ii) Dealers (indirectly connected to each other), iii) Ultimate Risk Buyers (URB). We show that the probability of widespread distress due to counter-party risk is higher in a bow-tie architecture than in more fragmented network structures. Empirically, we analyse a unique global dataset of bilateral CDS exposures on major sovereign and financial reference entities in 2011-2014. We find the presence of a bow-tie network architecture consistently across both reference entities and time, and that the flow-of-risk originates from a large number of URSs (e.g. hedge funds) and ends up in a few leading URBs, most of which are non-banks (in particular asset managers). Finally, the analysis of the CDS portfolio composition of the URBs shows a high level of concentration: in particular, the top URBs often show large exposures to potentially correlated reference entities.

How does risk flow in the credit default swap market?

We develop a framework to analyse the credit default swap (CDS) market as a network of risk transfers among counterparties. From a theoretical perspective, we introduce the notion of flow-of-risk and provide sufficient conditions for a bow-tie network architecture to endogenously emerge as a result of intermediation. This architecture shows three distinct sets of counterparties: (i) Ultimate Risk Sellers (URS), (ii) Dealers (indirectly connected to each other), (iii) Ultimate Risk Buyers (URB). We show that the probability of widespread distress due to counterparty risk is higher in a bow-tie architecture than in more fragmented network structures. Empirically, we analyse a unique global dataset of bilateral CDS exposures on major sovereign and financial reference entities in 2011–2014. We find the presence of a bow-tie network architecture consistently across both reference entities and time, and that the flow-of-risk originates from a large number of URSs (e.g. hedge funds) and ends up in a few leading URBs, most of which are non-banks (in particular asset managers). Finally, the analysis of the CDS portfolio composition of the URBs shows a high level of concentration: in particular, the top URBs often show large exposures to potentially correlated reference entities.

Geocoding routinely collected administrative data to measure access to alcohol outlets in Wales

ABSTRACTObjectivesA substantial level of excess alcohol consumption results in a wide range of harm and the potential impact on health at the population level of a reduction in consumption is considerable. A proposed policy for reducing alcohol consumption is restricting the availability of alcohol through reducing the density of alcohol outlets. We set out to create a high spatial resolution alcohol outlet dataset suitable for evaluating longitudinal changes in chronic alcohol related conditions.
 ApproachRequests were made for the names and location of all licensed alcohol outlets within each of the 22 Unitary Authorities in Wales, between Nov 2005 and Dec 2011. Data requested for each outlet consisted of: the date permission was granted or the licence became active, the licence expiry date or an indicated date of outlet closure, whether this premise is licensed for ON and/or OFF premise sales, the hours permissible to sell alcohol or general opening hours of the outlet and the type of premise as assigned by the LA if available. Our approach included collating, geocoding and manually matching alcohol outlet data received from each unitary authority for use in a longitudinal analysis of outlet density.
 ResultsAll authorities were able to provide an actual or approximate license issue date, allowing us to summarise the number of outlets annually. Several authorities were unable to provide precise outlet closure dates, so the date of the last interaction with the outlet was used to generate an approximate end date. One-half of the unitary authorities were able to provide the On/Off sales status of outlets, and 9 were able to provide opening hours. From these data we were able to geocode 53% (range 28% to 72% by local authority) using GIS, the remaining 47% were matched using Google products to verify and extract a precise geographic location.
 Conclusions The collation and processing of retrospective alcohol outlet data was successfully completed to enable the building of a longitudinal exposure dataset. There was considerable variation between the unitary authorities in the quality of address data, and data related to the availability of alcohol, for example opening hours. The lack of address structure required us to devise a manual address matching process to capture the addresses that could not be geocoded. To aid future data linkage based evaluations to provide policy evidence in a timely manner, local government datasets should use standardised data fields, including addresses and Point-of-Capture address verification.

PM2.5 exposure in highly polluted cities: A case study from New Delhi, India

Personal exposure (PE) to air pollutants is driven by a combination of pollutant concentrations in indoor and outdoor environments, and time-activity pattern of individuals. The objectives of this study were to estimate personal exposure to PM2.5 and black carbon (BC), and assess the representability of ambient air quality monitoring stations to serve as surrogates for PE in New Delhi. Personal exposure to air pollutants (PM2.5-PE and BCPE) was measured using portable, battery-operated instruments (PM2.5- pDR1500 and BC- microAethalometer AE51) in a small cohort of healthy adults (n=12 in summer, n=6 in winter) with no occupational exposure. Average PM2.5-PE and BCPE (µg/m3) were 53.9±136 and 3.71±4.29 respectively, in summer and 489.2±209.2 and 23.3±14.9 respectively, in winter. Activities associated with highest exposure levels were cooking and indoor cleaning for PM2.5, and commuting for BC. Within transport microenvironments, autorickshaws were found to be the most polluted, and lowest BC exposure was registered in public buses. Comparison of fixed-site ambient monitoring data showed a higher correlation with personal exposure dataset in winter compared to summer (r2 of 0.51 (winter) and 0.21 (summer); 51% (winter) and 20% (summer)). This study highlights the need for detailed assessment of PE to air pollutants in Indian cities, and calls for a denser network of monitoring stations for better exposure assessment.

Using Open-Access Data in the Development of Exposure Data Sets of Industrial Buildings for Earthquake Risk Modeling

Recent developments in volunteered geographic information (VGI), such as the OpenStreetMap initiative, highlight the potential of these data sets as supplementary or alternative sources of spatially based building information. Its increase in usefulness is particularly evident when combined with additional open-access data, such as the CORINE initiative, which provides the geo-referenced distribution of non-residential areas in Europe. However, the systematic application of VGI in the development of exposure models for catastrophe risk assessment has been the subject of limited research. In this paper, we describe an algorithm that, based on open-access information, provides an automated tool for the development of exposure data sets of industrial buildings in Europe, at the 30 arc-second resolution. Its practical application shows that results obtained at national and regional scales are in excellent agreement with data collected from cadastral agencies in Denmark, Italy, and Portugal; which highlights the potential of the algorithm when real building information is scarce or non-existent.

Multiplex interbank networks and systemic importance: An application to European data

Research on interbank networks and systemic importance is starting to recognise that the web of exposures linking banks’ balance sheets is more complex than the single-layer-of-exposure approach suggests. We use data on exposures between large European banks, broken down by both maturity and instrument type, to characterise the main features of the multiplex (or multi-layered) structure of the network of large European banks. Banks that are well connected or important in one network, tend to also be well connected in other networks (i.e. the network features positively correlated multiplexity). The different layers exhibit a high degree of similarity, stemming both from standard similarity analyses as well as a core-periphery analyses at the layer level. We propose measures of systemic importance that fit the case in which banks are connected through an arbitrary number of layers (be it by instrument, maturity or a combination of both). Such measures allow for a decomposition of the global systemic importance index for any bank into the contributions of each of the sub-networks, providing a potentially useful tool for banking regulators and supervisors in identifying tailored policy responses. We use the dataset of exposures between large European banks to illustrate that both the methodology and the specific level of network aggregation may matter both in the determination of interconnectedness and in the policy making process.

Developing Asbestos Job Exposure Matrix Using Occupation and Industry Specific Exposure Data (1984-2008) in Republic of Korea.

BackgroundThe goal of this study is to develop a general population job-exposure matrix (GPJEM) on asbestos to estimate occupational asbestos exposure levels in the Republic of Korea.MethodsThree Korean domestic quantitative exposure datasets collected from 1984 to 2008 were used to build the GPJEM. Exposure groups in collected data were reclassified based on the current Korean Standard Industrial Classification (9th edition) and the Korean Standard Classification of Occupations code (6th edition) that is in accordance to international standards. All of the exposure levels were expressed by weighted arithmetic mean (WAM) and minimum and maximum concentrations.ResultsBased on the established GPJEM, the 112 exposure groups could be reclassified into 86 industries and 74 occupations. In the 1980s, the highest exposure levels were estimated in “knitting and weaving machine operators” with a WAM concentration of 7.48 fibers/mL (f/mL); in the 1990s, “plastic products production machine operators” with 5.12 f/mL, and in the 2000s “detergents production machine operators” handling talc containing asbestos with 2.45 f/mL. Of the 112 exposure groups, 44 groups had higher WAM concentrations than the Korean occupational exposure limit of 0.1 f/mL.ConclusionThe newly constructed GPJEM which is generated from actual domestic quantitative exposure data could be useful in evaluating historical exposure levels to asbestos and could contribute to improved prediction of asbestos-related diseases among Koreans.

Using Routinely Collected Administrative Data in Public Health Research: Geocoding Alcohol Outlet Data

We describe our process of geocoding alcohol outlets to create a national longitudinal exposure dataset for Wales, United Kingdom from 2006 to 2011. We investigated variation in the availability of data items and the quality of alcohol outlet addresses held within unitary authorities. We used a standard geocoding method augmented with a manual matching procedure to achieve a fully spatially referenced dataset. We found higher quality addresses are held for outlets based in urban areas, resulting in the automatic geocoding of 68 % of urban outlets, compared to 48 % in rural areas. Missing postcodes and a lack of address structure contributed to a lower geocoding proportion. An urban rural bias was removed with the development of a manual matching procedure. Only one-half of the unitary authorities provided data on on/off sales and opening times, which are important availability factors. The resulting outlet dataset is suitable for contributing to the evidence-base of alcohol availability and alcohol-related harm. Local government should be encouraged to use standardised data fields, including addresses, to enable accurate geocoding of alcohol outlets and facilitate research that aims to prevent alcohol-related harm. Standardising data collection would enable efficient secondary data reuse using record linkage techniques, allowing the retrospective creation and evaluation of population-based natural experiments to provide evidence for policy and practice.

Using open building data in the development of exposure data sets for catastrophe risk modelling

Abstract. One of the necessary components to perform catastrophe risk modelling is information on the buildings at risk, such as their spatial location, geometry, height, occupancy type and other characteristics. This is commonly referred to as the exposure model or data set. When modelling large areas, developing exposure data sets with the relevant information about every individual building is not practicable. Thus, census data at coarse spatial resolutions are often used as the starting point for the creation of such data sets, after which disaggregation to finer resolutions is carried out using different methods, based on proxies such as the population distribution. While these methods can produce acceptable results, they cannot be considered ideal. Nowadays, the availability of open data is increasing and it is possible to obtain information about buildings for some regions. Although this type of information is usually limited and, therefore, insufficient to generate an exposure data set, it can still be very useful in its elaboration. In this paper, we focus on how open building data can be used to develop a gridded exposure model by disaggregating existing census data at coarser resolutions. Furthermore, we analyse how the selection of the level of spatial resolution can impact the accuracy and precision of the model, and compare the results in terms of affected residential building areas, due to a flood event, between different models.

Multiplex Interbank Networks and Systemic Importance: An Application to European Data

Research on interbank networks and systemic importance is starting to recognise that the web of exposures linking banks balance sheets is more complex than the single-layer-of-exposure approach. We use data on exposures between large European banks broken down by both maturity and instrument type to characterise the main features of the multiplex structure of the network of large European banks. This multiplex network presents positive correlated multiplexity and a high similarity between layers, stemming both from standard similarity analyses as well as a core-periphery analyses of the different layers. We propose measures of systemic importance that fit the case in which banks are connected through an arbitrary number of layers (be it by instrument, maturity or a combination of both). Such measures allow for a decomposition of the global systemic importance index for any bank into the contributions of each of the sub-networks, providing a useful tool for banking regulators and supervisors in identifying tailored policy instruments. We use the dataset of exposures between large European banks to illustrate that both the methodology and the specific level of network aggregation matter in the determination of interconnectedness and thus in the policy making process. JEL Classification: G21, D85, C67

Checking the Dark Matter Origin of a 3.53 keV Line with the Milky Way Center

We detect a line at 3.539±0.011 keV in the deep exposure data set of the Galactic center region, observed with the x-ray multi-mirror mission Newton. The dark matter interpretation of the signal observed in the Perseus galaxy cluster, the Andromeda galaxy [A. Boyarsky et al., Phys. Rev. Lett. 113, 251301 (2014)], and in the stacked spectra of galaxy clusters [E. Bulbul et al., Astrophys. J. 789, 13 (2014)], together with nonobservation of the line in blank-sky data, put both lower and upper limits on the possible intensity of the line in the Galactic center data. Our result is consistent with these constraints for a class of Milky Way mass models, presented previously by observers, and would correspond to the radiative decay dark matter lifetime, τDM∼6-8×10(27) sec. Although it is hard to exclude an astrophysical origin of this line based on the Galactic center data alone, this is an important consistency check of the hypothesis that encourages us to check it with more observational data that are expected by the end of 2015.

There is more to a Bank than its Size: Bank Expertise and Financial Information Collection

Concentration features prominently in models of information acquisition by banks. However, empirical evidence on the role of concentration is limited because banks rarely disclose details about their exposures or the information they collect. Using a novel dataset of bank-level commercial loan exposures, we find banks are less likely to collect audited financial statements from firms in industries and regions in which they have more exposure. These findings are stronger in settings in which adverse selection is acute and muted when the bank lacks experience with an exposure. Our results offer novel evidence on how organizational design is related to the type of financial information used by financial intermediaries and support theoretical predictions suggesting that portfolio concentration reveals a bank’s relative expertise.

Multiplex Interbank Networks and Systemic Importance: An Application to European Data

Research on interbank networks and systemic importance is starting to recognise that the web of exposures linking banks balance sheets is more complex than the single-layer-of-exposure approach. We use data on exposures between large European banks broken down by both maturity and instrument type to characterise the main features of the multiplex structure of the network of large European banks. This multiplex network presents positive correlated multiplexity and a high similarity between layers, stemming both from standard similarity analyses as well as a core-periphery analyses of the different layers. We propose measures of systemic importance that fit the case in which banks are connected through an arbitrary number of layers (be it by instrument, maturity or a combination of both). Such measures allow for a decomposition of the global systemic importance index for any bank into the contributions of each of the sub-networks, providing a useful tool for banking regulators and supervisors in identifying tailored policy instruments. We use the dataset of exposures between large European banks to illustrate that both the methodology and the specific level of network aggregation matter in the determination of interconnectedness and thus in the policy making process.

“Quasi Nonparametric” Upper Tolerance Limits for Occupational Exposure Evaluations

Upper tolerance limits (UTLs) are often used in comparing exposure data sets with an occupational exposure limit (OEL) or other regulatory criterion (RC): if the 95%-95% UTL does not exceed the OEL, one is 95% confident that at most 5% of exposures exceed the OEL, and the comparison “passes.” The largest of 59 observations is a nonparametric (distribution-free) 95%-95% UTL (NPUTL); the chance that this largest value equals or exceeds the actual 95th percentile is at least 95%, regardless of the underlying data distribution. That many observations may seem excessive in clean environments or small studies, though, and one would like to “pass” using UTLs based on fewer observations sufficiently far below the OEL or RC.“Quasi-nonparametric” UTLs (QNP UTLs) accomplish this. QNP UTLs assign a “pass” so long as one has “59 [values] less than the RC” (the NPUTL itself), “30 less than 1/2 [of the RC],” “21 less than 1/3,” and on down to “8 less than 1/10,” the last matching a rule-of-thumb given in 2006 American Industrial Hygiene Association (AIHA) guidance. They are derived using the conservative, experience-based assumption that the data distribution is lognormal with log-scale standard deviation σ at most 2.0 (geometric standard deviation at most 7.39). Although based on this assumption, their statistical performance is reasonably unaffected or conservative when data come from other distributions often assumed for contaminant concentrations; moreover, their performance is insensitive to analytical variation. This conservative robustness merits the description “quasi-nonparametric.” QNP UTLs are very easy to use. Reporting Limit (RL) issues do not arise. QNP UTLs reduce the numbers of observations needed to support conservative risk management decisions when sampling from compliant working conditions.

Forecasting mortality in subpopulations using Lee–Carter type models: A comparison

The relative performance of multipopulation stochastic mortality models is investigated. When targeting mortality rates, we consider five extensions of the well known Lee–Carter single population extrapolative approach. As an alternative, we consider similar structures when mortality improvement rates are targeted. We use a dataset of deaths and exposures of Italian regions for the years 1974–2008 to conduct a comparison of the models, running a battery of tests to assess the relative goodness of fit and forecasting capability of different approaches. Results show that the preferable models are those striking a balance between complexity and flexibility.

Developing a pooled job physical exposure data set from multiple independent studies: an example of a consortium study of carpal tunnel syndrome

BackgroundSix research groups independently conducted prospective studies of carpal tunnel syndrome (CTS) incidence in 54 US workplaces in 10 US States. Physical exposure variables were collected by all research groups...

The Comparative Toxicogenomics Database's 10th year anniversary: update 2015.

Ten years ago, the Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) was developed out of a need to formalize, harmonize and centralize the information on numerous genes and proteins responding to environmental toxic agents across diverse species. CTD's initial approach was to facilitate comparisons of nucleotide and protein sequences of toxicologically significant genes by curating these sequences and electronically annotating them with chemical terms from their associated references. Since then, however, CTD has vastly expanded its scope to robustly represent a triad of chemical–gene, chemical–disease and gene–disease interactions that are manually curated from the scientific literature by professional biocurators using controlled vocabularies, ontologies and structured notation. Today, CTD includes 24 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, phenotypes, Gene Ontology annotations, pathways and interaction modules. In this 10th year anniversary update, we outline the evolution of CTD, including our increased data content, new ‘Pathway View’ visualization tool, enhanced curation practices, pilot chemical–phenotype results and impending exposure data set. The prototype database originally described in our first report has transformed into a sophisticated resource used actively today to help scientists develop and test hypotheses about the etiologies of environmentally influenced diseases.