Local-scale Data Research Articles

Quantification of active layer depth at multiple scales in Interior Alaska permafrost

Much of Interior Alaska is underlain by permafrost that has been thawing at an unprecedented rate. Top-down expansion of the seasonally thawed ‘active layer’ and development of thermokarst features are increasing across the landscape. This can be attributed primarily due to a warming climate and disturbances like wildfires which have accelerated summer season permafrost thaw. Quantification of active-layer thickness (ALT) is critical to understanding the response of permafrost terrains to these disturbances. ALT measurements are time consuming, and point based. As a result, there are large uncertainties in ALT estimates at regional/global scales (100 km2 or larger) using field scale (1 m2) measurements as direct inputs for calibrating/validating large scale process-based or statistical/empirical models. Here we developed a framework to link field scale ALT measurements with satellite observations to a regional scale (100 km2) via an intermediary upscaling of field scale ALT to the local scale (1 km2) with fine-resolution airborne hyperspectral and light detection and ranging data, thus leading to a characterization of ALT across space and time at multiple scales. We applied an object-based machine learning ensemble approach to upscale field scale (1 m2) measurements to the local (1 km2) and regional scale (100 km2) and achieved encouraging results across three permafrost experimental sites in Interior Alaska that represent a variety of terrain types. Our study demonstrates that generating local scale data products is an effective approach to bridge the gap with field scale measurements and regional scale estimations as it seeks to reduce upscaling uncertainty.

Sectoral water use responses to droughts and heatwaves: analyses from local to global scales for 1990–2019

Water use for various sectors (e.g. irrigation, livestock, domestic, energy and manufacturing) is increasing due to a growing global population and economic development. Additionally, increases in frequency and severity of droughts, heatwaves and compound drought-heatwave events, also lead to responses in sectoral water use and a reduction in water availability, intensifying water scarcity. However, limited knowledge exists on the responses in sectoral water use during these hydroclimatic extremes. In this study we quantify the impacts of droughts, heatwaves and compound events on water use of irrigation, livestock, domestic, energy and manufacturing sectors at global, country and local scales. To achieve this, datasets of reported and downscaled sectoral water use (i.e. withdrawal and consumption) were evaluated during these hydroclimatic extremes and compared to normal (non-extreme) periods for 1990–2019. Our analysis shows that these hydroclimatic extremes affect water use patterns differently per sector and region. Reported data show that domestic and irrigation water use increases during heatwaves in Eastern Europe and central continental United States, while water use decreases for thermoelectric sector, particularly in Europe while it increases in north and Eastern Asia. Additionally, global water use response patterns reveal that irrigation and domestic sectors are mostly prioritized over livestock, thermoelectric and manufacturing. Reported local-scale data reveal that for most sectors and regions/locations, stronger water use responses are found for heatwaves and compound events compared to impacts during hydrological droughts. Our outcomes provide improved understanding of sectoral water use behaviour under hydroclimatic extremes. Nonetheless, given the future threats to water availability and the limited accessible information of water use, there is an urgency to collect more monitored-driven data of sectoral water use for improved assessments of water scarcity under these extremes. Consequently, this research reveals the necessity of more realistic water use models to better represent the sectoral responses to hydroclimatic extremes.

Spatially varying relationships of soil Se concentration and rice Se concentration in Guangxi, China: A geographically weighted regression approach

In recent years, the biogeochemical behavior and environmental impact of Selenium (Se) on soil-plant systems have received widespread attention, and traditional statistical methods reveal generally positive correlations between rice Se and soil Se. However, that initial positive relationship may have been obscured by local external factors. Using local scale data from the geochemical evaluation of land quality project, this work employed geographically weighted regression (GWR) to examine the spatial variation of rice Se (as the dependent variable) and soil Se (as the independent variable) in Guangxi. Strong and weak correlation coefficients occur between rice Se and soil Se, thereby indicating that their relationships are spatially varying. Guangxi is characterized by significantly positive correlations in most areas, with weak correlations mostly found in the south-western and central-eastern regions. Areas with weak correlation can be divided into two patterns: high soil Se with low rice Se and high rice Se with low soil Se. The unique patterns are correlated with distinct natural factors, particularly the abundance of Fe-rich soils in the carbonate area; by contrast, sandstone areas in central Guangxi may have been affected by anthropogenic activities. To reveal the spatially varying relationships at the local scale, we employed GWR, an effective tool that allowed us to identify the association between environmental variables and influencing factors and explore spatially varying relationships between them. This study breaks through the existing understanding that soil Se is completely positively correlated with rice Se for the first time, and concludes that their correlation is spatially variable, providing an effective approach for the study of complex relationships.

Crude Birth Rate in Croatia: local-scale variability, spatial patterns, and trends from 2001 to 2020

This study investigates the spatio-temporal variability of the Crude Birth Rate in Croatia between 2001 and 2020. The local-scale data covers 556 spatial units. Additionally, the data covers categories by type and size of unit and their respective regions. The study is based on descriptive and spatial statistics (Global and Local Moran's indices). The results reveal two main processes in the long-term trends of birth rates in Croatia: convergence at a low birth rate and a significant decrease in spatial autocorrelation. The most intense convergence took place between 2009 and 2017, mainly overlapping with the economic recession period. The process was also observed at the regional level. Spatial autocorrelation decreased between 2001 and 2016, followed by a slight increase. In the initial years, the main cause could be modernization, and later the economic recession and the subsequent emigration wave that followed Croatia's accession to the European Union. Finally, the continuation of the polarization between the center and the periphery is evident.

Statistical Downscaling Menggunakan Pengelompokan Expectation-Maximization pada Data CFSRv2

General Circulation Model (GCM) is a numerical model that produce a number of data from various climate parameters that are used to estimate climate, one of which is precipitation. GCM is a global scale data and has high resolution. So, the GCM cannot consider the local-scale areas with a higher resolution than the GCM. Therefore, to be able to use the GCM for estimating the local rainfall, namely Statistical Downscaling (SD). SD is a technique used to get the relationship between global-scale (GCM) and local-scale data. SD use the GCM which consist of dependent variables that have multicollinearity. So in this research, the principal component regression (PCR) and partial least square regression (PLSR) will be used to reduce the multicollinearity. In addition, to reduce the RMSEP and increase the correlation value, a clustering technique will be applied before modeling, namely Expectation-Maximization (EM) clustering. This research use CFSRv2 data as GCM and local rainfall data at four rainfall stations in West Java (January 2011 to December 2017). Based on this research, PCR is a good modeling than PLSR and EM clustering get the lower RMSEP and higher correlation value than without clustering before modeling. The conclusion is PCR with EM clustering is a good method for estimating local rainfall using the SD technique especially rainfall in West Java and CFSRv2 data.

High Wind Speed Prevents the Establishment of the Disease Vector Mosquito Aedes albopictus in Its Climatic Niche in Europe

Environmentally suitable habitats ofAedes albopictus(Ae. albopictus) in Europe are identified by several modeling studies. However, it is noticeable that even after decades of invasion process in Europe, the vector mosquito has not yet been established in all its environmentally suitable areas. Natural barriers and human-mediated transport play a role, but the potential of wind speed to explainAe. albopictus’absences and its inability to establish in its suitable areas are largely unknown. This study therefore evaluates the potential of wind speed as an explanatory parameter of the non-occurrence ofAe. albopictus. We developed a global ecological niche model with relevant environmental parameters including wind speed and projected it to current climatic conditions in Europe.Differences in average wind speed between areas of occurrence and non-occurrence ofAe. albopictuswithin its modeled suitable areas were tested for significance. A second global ecological niche model was trained with the same species records and environmental parameters, excluding windspeed parameters. Using multiple linear regression analyses and a test of average marginal effect, the effect of increasing wind speed on the average marginal effect of temperature and precipitation on the projected habitat suitability was estimated. We found that climatically suitable and monitored areas whereAe. albopictusis not established (3.12 ms-1 ± 0.04 SD) have significantly higher wind speed than areas where the species is already established (2.54 ms-1 ± 0.04 SD). Among temperature-related bioclimatic variables, the annual mean temperature was the most important variable contributing to the performance of both global models. Wind speed has a negative effect on the predicted habitat suitability ofAe. albopictusand reduces false-positive rates in model predictions. With increasing wind speed, the average marginal effect of annual mean temperatures decreases but that of the annual precipitation increases. Wind speed should be considered in future modeling efforts aimed at limiting the spread and dispersal ofAe. albopictusand in the implementation of surveillance and early warning systems. Local-scale data collected from fieldwork or laboratory experiments will help improve the state of the art on how wind speed influences the distribution, flight, and dispersal activity of the mosquito.

A global indicator of utilized wildlife populations: Regional trends and the impact of management

A global indicator of utilized wildlife populations: Regional trends and the impact of management

Efficient small-scale marine reserve design requires high-resolution biodiversity and stakeholder data

Establishing marine reserves is particularly challenging in highly populated coastal areas where stakeholders compete for resources and space, such as the Mediterranean Sea. While large-scale conservation planning is useful, there is a need for finer-grained assessments at local and regional scales. Yet fine scale environmental and socioeconomic data are not always available. Here, we evaluate the influence of the spatial resolution of biodiversity and socioeconomic data on the selection of priority areas for conservation in Montgrí, the Illes Medes and the Baix Ter Natural Park, Spain. We used varying levels of habitat data, from fine-scale maps created using detailed bathymetry and underwater surveys to less detailed ones using existing data or broader classifications. We also used different estimates of the cost of protection, from a combined recreational and artisanal fishing cost obtained through local consultation and in situ mapping to simple indirect measures such as distance to port. Our results reveal that conservation planning scenarios that do not use fine-scale bathymetry are ineffective at protecting biodiversity and only represent 40% or less of the habitats. Scenarios that only include recreational fishing, which is limited to certain planning units in the park, perform very poorly at minimizing cost, highlighting the need to use cost estimates that include all planning units. We conclude that local-scale data will often be needed to support and guide local-scale efforts to expand or establish new marine reserves.

Selected Aspects of Carbon Stock Assessment in Aboveground Biomass

Given the significance of national carbon inventories, the importance of large-scale estimates of carbon stocks is increasing. Accurate biomass estimates are essential for tracking changes in the carbon stock through repeated assessment of carbon stock, widely used for both vegetation and soil, to estimate carbon sequestration. Objectives: The aim of our study was to determine the variability of several aspects of the carbon stock value when the input matrix was (1) expressed either as a vector or as a raster; (2) expressed as in local (1:10,000) or regional (1:100,000) scale data; and (3) rasterized with different pixel sizes of 1, 10, 100, and 1000 m. Method: The look-up table method, where expert carbon content values are attached to the mapped landscape matrix. Results: Different formats of input matrix did not show fundamental differences with exceptions of the biggest raster of size 1000 m for the local level. At the regional level, no differences were notable. Conclusions: The results contribute to the specification of best practices for the evaluation of carbon storage as a mitigation measure, as well as the implementation of national carbon inventories.

Variation of community assembly rules of a whole turtle family (Pelomedusidae) from continental to local scales in Africa

AbstractAssessing community assembly rules has been a prominent task by community ecologists. Using a turtle family (Pelomedusidae) as a study case, we analyze the structure of their communities from the continental scale to the habitat scale, by using a suite of statistical approaches: (i) GIS modeling of the most complete presence sites dataset (from IUCN/SSC TFTSG archive); (ii) niche overlap and co‐occurrence null models with Monte Carlo simulations; (iii) generalized linear models. We used, after controlling for spatial autocorrelation, continental scale presence data totalling 1698 locality records belonging to 27 species of African Pelomedusidae and local scale data from 58 surveyed stations in eight different countries, totalling 1936 turtle individuals of at least five species (but several taxa are included into one of these “species”: the Pelomedusa subrufa complex). At the local scale, the number of co‐occurring Pelomedusidae in Africa ranged from two to three species, although the ranges of up to five total species overlapped at various locations on the continental scale. A simulation study showed that, at the continental scale, the various species co‐occurred following assembly rules that are compatible with interspecific competition by two metrics. At the habitat scale, the mean number of sympatric species was just below two and the maximum number of sympatric species per local site was three, with significant variation among habitat categories (more in forest than in any other habitat). Body size filtering may be also a mechanism that facilitates coexistence of pelomedusid species under sympatric conditions.

Co-creating and evaluating a citizen science program for monitoring submerged aquatic vegetation in Chesapeake Bay

Research on submerged aquatic vegetation (SAV) directly informs policy and management decisions in Chesapeake Bay; however, limitations to data collection capabilities of professional scientists have left knowledge gaps that hinder scientific research and management efforts. Experts have called upon volunteers to help collect the local-scale data that are necessary for understanding SAV growth and species distribution within one of the world’s largest estuaries. We collaborated with various environmental stakeholders, including SAV experts, volunteer monitoring coordinators, and citizen scientists, to design a multi-tiered monitoring protocol and training program for citizen scientists monitoring SAV in Chesapeake Bay. We employed a sequence of research approaches, which included online surveys, literature review, a pilot study, participant observation with volunteers in the field, and a protocol-testing workshop. From these approaches, we were able to better understand the stakeholders’ various needs and requirements for the Chesapeake Bay SAV Watchers program. Evaluative interviews helped us collect feedback on the process of developing the program, as well as on the program itself. We learned that the collaborative process of co-creating the monitoring program led to positive outcomes for the stakeholders involved, as well as for the perceived scientific rigor of the program. Furthermore, our use of science communication and user-centered design increased the program’s functionality and credibility. Finally, our interview results suggested that continuing to incorporate participants’ motivations into the program’s management will improve volunteer retention and therefore increase the program’s impact and longevity. This research will inform the creation of other collaboratively-developed participatory research programs, as well as citizen science programs that simultaneously prioritize engagement, education, and collection of high-quality data.

Spatiotemporal Patterns in Data Availability of the Sentinel-5P NO2 Product over Urban Areas in Norway

Due to its comparatively high spatial resolution and its daily repeat frequency, the tropospheric nitrogen dioxide product provided by the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor platform has attracted significant attention for its potential for urban-scale monitoring of air quality. However, the exploitation of such data in, for example, operational assimilation of local-scale dispersion models is often complicated by substantial data gaps due to cloud cover or other retrieval limitations. These challenges are particularly prominent in high-latitude regions where significant cloud cover and high solar zenith angles are often prevalent. Using the example of Norway as a representative case for a high-latitude region, we here evaluate the spatiotemporal patterns in the availability of valid data from the operational TROPOMI tropospheric nitrogen dioxide (NO2) product over five urban areas (Oslo, Bergen, Trondheim, Stavanger, and Kristiansand) and a 2.5 year period from July 2018 through November 2020. Our results indicate that even for relatively clean environments such as small Norwegian cities, distinct spatial patterns of tropospheric NO2 are visible in long-term average datasets from TROPOMI. However, the availability of valid data on a daily level is limited by both cloud cover and solar zenith angle (during the winter months), causing the fraction of valid retrievals in each study site to vary from 20% to 50% on average. A temporal analysis shows that for our study sites and the selected period, the fraction of valid pixels in each domain shows a clear seasonal cycle reaching a maximum of 50% to 75% in the summer months and 0% to 20% in winter. The seasonal cycle in data availability shows the inverse behavior of NO2 pollution in Norway, which typically has its peak in the winter months. However, outside of the mid-winter period we find the TROPOMI NO2 product to provide sufficient data availability for detailed mapping and monitoring of NO2 pollution in the major urban areas in Norway and see potential for the use of the data in local-scale data assimilation and emission inversions applications.

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.

Metodologia de espacialização dos componentes do balanço de energia usando dados MODIS e ERA Interim

This work aims at proposing a methodology to spatialize the Energy Balance (EB) components from the combined use of MODIS sensor products and ERA-Interim reanalysis data, and to evaluate the performance of the OSEB and METRIC models for estimating EB components in the predominantly humid subtropical climate of Rio Grande do Sul, Brazil. The research was conducted for 3 consecutive years from 2009 to 2011. The temporal variation patterns of the EB components were analyzed by comparing the values estimated by the models against the reference measurements of a micrometeorological tower (Sulflux network). For the spatial variability analysis, reference evapotranspiration (ETo) was calculated at a local scale (data from Weather stations of the National Institute of Meteorology) and a regional scale (ERA-Interim reanalysis data) while the consistency between these estimates was also verified. Afterward, using only the pixels in which the sensible heat flux represented more than 50% of the radiation balance, correlation maps between ETo ERA-Interim and latent heat fluxes were constructed. The estimated BE components are consistent regarding both, the order of magnitude and variation throughout the year, when compared to the reference measurements of the micrometeorological tower. The spatial variability analyses also reveal satisfactory results for both models, however, the METRIC model results showed greater coherence since it provided a greater number of dates with consistent results, thus being indicated as the most suitable model for building the time series.

Good organizational reasons for better medical records: The data work of clinical documentation integrity specialists

Healthcare organizations and workers are under pressure to produce increasingly complete and accurate data for multiple data-intensive endeavors. However, little research has examined the emerging occupations arising to carry out the data work necessary to produce “improved” data sets, or the specific work activities of these emerging data occupations. We describe the work of Clinical Documentation Integrity Specialists (CDIS), an emerging occupation that focuses on improving clinical documentation to produce more detailed and accurate administrative datasets crucial for evolving data-intensive forms of healthcare accountability, management, and research. Using ethnographic methods, we describe the core of CDIS’ work as a translation practice in which the language, interests, and concerns of clinicians and clinical documentation are translated via real-time “nudging” and ongoing education of clinicians into the language, interests, and concerns of medical coders, structured administrative datasets, and the various stakeholders of these datasets. Further, we show how the institutional context of CDIS’ work shapes the occupational virtues that guide CDIS’ translation practice, including financial reimbursement, quality measures, clinical accuracy, and protecting clinician’s time. Despite the existence of these multiple virtues, financial reimbursement is the most prominent virtue guiding CDIS’ limited attention. Thus, overall clinical documentation is “improved” in specific, partial ways. This research provides one of the first studies of the emergent data work occupations arising in the wake of digitization and big data opportunities, and shows how local data settings shape large scale data in specific ways and thus may influence outcomes of analyses based on such data.

Robust learning algorithms for capturing oceanic dynamics and transport of Noctiluca blooms using linear dynamical models.

The blooms of Noctiluca in the Gulf of Oman and the Arabian Sea have been intensifying in recent years, posing now a threat to regional fisheries and the long-term health of an ecosystem supporting a coastal population of nearly 120 million people. We present the results of a local-scale data analysis to investigate the onset and patterns of the Noctiluca blooms, which form annually during the winter monsoon in the Gulf of Oman and in the Arabian Sea. Our approach combines methods in physical and biological oceanography with machine learning techniques. In particular, we present a robust algorithm, the variable-length Linear Dynamic Systems (vLDS) model, that extracts the causal factors and latent dynamics at the local-scale along each individual drifter trajectory, and demonstrate its effectiveness by using it to generate predictive plots for all variables and test macroscopic scientific hypotheses. The vLDS model is a new algorithm specifically designed to analyze the irregular dataset from surface velocity drifters, in which the multivariate time series trajectories are having variable or unequal lengths. The test results provide local-scale statistical evidence to support and check the macroscopic physical and biological Oceanography hypotheses on the Noctiluca blooms; it also helps identify complementary local trajectory-scale dynamics that might not be visible or discoverable at the macroscopic scale. The vLDS model also exhibits a generalization capability (as a machine learning methodology) to investigate important causal factors and hidden dynamics associated with ocean biogeochemical processes and phenomena at the population-level and local trajectory-scale.

Local and landscape influences on turbidity in urban streams: a global approach using citizen scientists

The ecological degradation of urban rivers and streams has been termed the ‘urban stream syndrome’ and attributed to increased catchment urbanization. Limiting future degradation requires an understanding of the drivers of reduced water quality at both catchment and site scales. The goal of this study was to identify the probable drivers of turbidity in river ecosystems in highly urbanized areas, under the premise that turbidity does not respond consistently to urbanization. Catchment-scale data were compiled from remotely sensed datasets, whereas local-scale data were collected by citizen scientists as part of the global FreshWater Watch (FWW) program. The local-scale data included nearly 2600 coincident measurements of turbidity and observations of other local characteristics taken with a common method between March 2013 and June 2016 across 127 unique locations in 6 major population centers: Vancouver (Canada), São Paulo (Brazil), Curitiba (Brazil), Buenos Aires (Argentina), Hong Kong SAR (China), and Guangzhou-Foshan (China). Catchment- and site-scale information were modeled with Boosted Regression Trees (BRT) to identify likely drivers of increased turbidity both across the entire dataset and within individual cities. Urbanization was not consistently associated with turbidity. The global BRT model explained 60% of the variation in turbidity, and key predictors were catchment area, % of the catchment as grassland, rainfall, Gross Domestic Product, and % of the catchment as artificial surfaces. City-specific BRT models explained 35–67% of the variation in turbidity. Key predictors varied between cities and were often different than those observed at the global scale. Local-scale data collected by citizen scientists were less predictive of turbidity than catchment-scale factors and explained ~12% of the observed global variability in turbidity. Factors such as riverbank vegetation and the presence of point pollution sources explained some of the variation in turbidity, indicating their management could help mitigate elevated turbidity and sediment load in some urban rivers. Through this high-resolution, site-scale information, we highlight how community-sourced data may add value to freshwater monitoring programs.

Preservation of local high-resolution data in a regional low-resolution dataset: A ‘nested’ 3D modeling approach using an example from a Quaternary glacial stratigraphy (Ontario, Canada)

Regional scale 3D models provide insight into general unit geometries and stratigraphic relationships; however, potentially significant local scale data and unit geometries are rarely preserved. Accurate modeling of local scale unit geometries and stratigraphic relationships is important for understanding aquifer-aquitard connectivity, estimation of aquifer volume, and prediction of contaminant migration pathways; this is particularly important in previously glaciated areas because of the high degree of lithological heterogeneity and stratigraphic variation in glaciogenic deposits. This study presents a case study (Ontario, Canada) that explores the impacts of an optimized model (in which high-resolution data are preserved in local scale models as ‘golden blocks’) and non-optimized regional scale 3D models on the predicted geometries and stratigraphic relationships of coarse- and fine-grained units (lithofacies associations) within a Quaternary stratigraphy. Each of the regional scale models include high quality data (outcrop and cored borehole logs) and low quality data (water well lithology logs); however, the optimized model was constructed by seamlessly integrating the golden blocks into the regional scale model using an algorithm (producing a ‘nested’ model), whereas the non-optimized model was built using a global modeling approach. By way of comparison to unit geometries previously delineated in outcrops and surficial geology maps and hydraulic responses from pumping tests, the results of this study indicate that coarse-grained units, which host significant municipal aquifers, and fine-grained units were more realistically and accurately represented in the optimized model as a result of the nested 3D modeling approach. This case study demonstrates a relatively simple and effective method for preserving high quality data in a regional scale 3D model without sacrificing model accuracy in key areas of interest (e.g. aquifer well fields).

Multiple Determinants of Vulnerability for Emergency Department Visits for Heat-Related Illness in California 2005-2008 Warm Seasons

Background: High temperatures are associated with risk of Heat-Related Illness (HRI) and other acute clinical outcomes. Information on HRI risk factors is rarely place-based; decision-makers need local-scale (e.g., neighborhood) data to inform strategies to reduce heat health impacts. Objective: In multi- determinant multi-level models assess relations between daily maximum temperature (Tmax) and emergency department (ED) visits for HRI and vulnerability factors, i.e., biological susceptibility (e.g., sex, age, race), physical environment (e.g., land-use, air quality), or socioeconomic (e.g., neighborhood assets). Methods: CA Office of Statewide Healthcare Planning and Development ED data for 2005-2008, restricted to the warm seasons (May 1-October 31) were used. Cases were defined as a HRI diagnosis (International Classification of Diseases (ICD) ninth revision, clinical modification (ICD9-CM) code 992.0-992.9). Non-HRI cases were retained as controls. Exclusions: If date of visit or residence ZIP code (ZC) was missing. ZC were converted to Zip Code Tabulation Area (ZCTA) to link census tract data to patients. Hierarchical generalized linear models with a logic link (SAS v9.3 PROC GLIMMIX) were used. For each exposure (daily Tmax, daily O3 or PM10) models included both the ZCTA-specific 4-year seasonal mean (Tmaxmn), and ZCTA-day (i.e., ED visit date) deviation from that mean (Tmaxdif). Interaction terms were used to evaluate effect modification. Results: HRI risk was positively associated with Tmaxdif (OR 1.02 95%CI: 1.016, 1.019, p<.0001), and co-exposure to O3 (Tmaxdif*O3dif) increased the risk (OR 1.051 95%CI: 1.043, 1.059, p<.0001). Risk varied by age group, sex and race/ethnicity, with girls (especially those ≤ 10 years), African American and Hispanic children, and elderly Black at greatest risk. ZCTA %-impervious surfaces, % multi-family homes were positively associated with HRI. These and other results pointed to interventions to reduce HRI risk.

The utility of geodemographic indicators in small area estimates of limiting long-term illness

Small area health data are not always available on a consistent and robust routine basis across nations, necessitating the employment of small area estimation methods to generate local-scale data or the use of proxy measures. Geodemographic indicators are widely marketed as a potential proxy for many health indicators. This paper tests the extent to which the inclusion of geodemographic indicators in small area estimation methodology can enhance small area estimates of limiting long-term illness (LLTI). The paper contributes to international debates on small area estimation methodologies in health research and the relevance of geodemographic indicators to the identification of health care needs. We employ a multilevel methodology to estimate small area LLTI prevalence in England, Scotland and Wales. The estimates were created with a standard geographically-based model and with a cross-classified model of individuals nested separately in both spatial groupings and non-spatial geodemographic clusters. LLTI prevalence was estimated as a function of age, sex and deprivation. Estimates from the cross-classified model additionally incorporated residuals relating to the geodemographic classification. Both sets of estimates were compared against direct estimates from the 2011 Census. Geodemographic clusters remain relevant to understanding LLTI even after controlling for age, sex and deprivation. Incorporating a geodemographic indicator significantly improves concordance between the small area estimates and the Census. Small area estimates are however consistently below the equivalent Census measures, with the LLTI prevalence in urban areas characterised as ‘blue collar’ and ‘struggling families’ being markedly lower. We conclude that the inclusion of a geodemographic indicator in small area estimation can improve estimate quality and enhance understanding of health inequalities. We recommend the inclusion of geodemographic indicators in public releases of survey data to facilitate better small area estimation but caution against assumptions that geodemographic indicators can, on their own, provide a proxy measure of health status.