Airborne Lead Exposure and Childhood Cognition: The Environmental Influences on Child Health Outcomes (ECHO) Cohort (2003-2022).

Objectives. To examine whether a previously reported association between airborne lead exposure and children's cognitive function replicates across a geographically diverse sample of the United States. Methods. Residential addresses of children (< 5 years) were spatially joined to the Risk-Screening Environmental Indicators model of relative airborne lead toxicity. Cognitive outcomes for children younger than 8 years were available for 1629 children with IQ data and 1476 with measures of executive function (EF; inhibitory control, cognitive flexibility). We used generalized linear models using generalized estimating equations to examine the associations of lead, scaled by interquartile range (IQR), accounting for individual- and area-level confounders. Results. An IQR increase in airborne lead was associated with a 0.74-point lower mean IQ score (b = -0.74; 95% confidence interval = -1.00, -0.48). The association between lead and EF was nonlinear and was modeled with a knot at the 97.5th percentile of lead in our sample. Lead was significantly associated with lower mean inhibitory control but not with cognitive flexibility. This effect was stronger among males for both IQ and inhibitory control. Conclusions. Early-life exposure to airborne lead is associated with lower cognitive functioning. (Am J Public Health. 2024;114(3):309-318. https://doi.org/10.2105/AJPH.2023.307519).

Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data.

BACKGROUND: The growth of geolocated data has opened the door to a wealth of new research opportunities in the health fields. One avenue of particular interest is the relationship between the spaces where people spend time and their health outcomes. This research model typically intersects individual data collected on a specific cohort with publicly available socioeconomic or environmental aggregate data. In spatial terms: individuals are represented as points on map at a particular time, and context is represented as polygons containing aggregated or modeled data from sampled observations. Uncertainty abounds in these kinds of complex representations. METHODS: We present four sensitivity analysis approaches that interrogate the stability of spatial and temporal relationships between point and polygon data. Positional accuracy assesses the significance of assigning the point to the correct polygon. Neighborhood size investigates how the size of the context assumed to be relevant impacts observed results. Life course considers the impact of variation in contextual effects over time. Time of day recognizes that most people occupy different spaces throughout the day, and that exposure is not simply a function residential location. We use eight years of point data from a longitudinal study of children living in rural Pennsylvania and North Carolina and eight years of air pollution and population data presented at 0.5 mile (0.805 km) grid cells. We first identify the challenges faced for research attempting to match individual outcomes to contextual effects, then present methods for estimating the effect this uncertainty could introduce into an analysis and finally contextualize these measures as part of a larger framework on uncertainty analysis. RESULTS: Spatial and temporal uncertainty is highly variable across the children within our cohort and the population in general. For our test datasets, we find greater uncertainty over the life course than in positional accuracy and neighborhood size. Time of day uncertainty is relatively low for these children. CONCLUSIONS: Spatial and temporal uncertainty should be considered for each individual in a study since the magnitude can vary considerably across observations. The underlying assumptions driving the source data play an important role in the level of measured uncertainty.

Proximity to sources of airborne lead is associated with reductions in Children's executive function in the first four years of life.

Although policies to remove lead from gasoline have resulted in a substantial reduction in airborne lead, multiple industries are known to generate lead that is released in the air. The present study examines the extent to which residential proximity to a documented source of airborne lead is associated with intellectual and executive function in children. Data were available for n = 849 children from the Family Life Project. Geolocation for children's residences between birth and 36 months were referenced against the Environmental Protection Agency's Risk Screening Environmental Indicators (RSEI) database, which estimates exposure for each ½ mile grid in the contiguous United States. Instrumental variable models were employed to estimate causal associations between exposure and cognitive outcomes measured at 36, 48, and 60 months, using census-documented density of manufacturing employment as the instrument. Models of continuous lead dosage indicated small negative effects for both child IQ and executive function (EF). These results indicate that RSEI estimates of airborne lead exposure are meaningfully associated with decrements in cognitive development.

A comparative study of urban occupational structures: Brazil and United States / Estudo comparativo das estruturas ocupacionais urbanas: Brasil e Estados Unidos / Un estudio comparado de las estructuras ocupacionales urbanas: Brasil y Estados Unidos de Norteamérica

This paper compares the occupational structure of cities in Brazil and United States aiming to evaluate the extent to which the economic structure of these urban agglomerations is associated with the different stages of development, specifically when comparing a rich country with a developing one. Using a harmonized occupational database and microdata from the Brazilian 2010 Demographic Census and the U.S. American Community Survey (2008-2012), results show that Brazilian cities have a stronger connection between population size, both with occupational structure and human capital distribution, than the one found for cities in the United States. These findings suggest a stronger primacy of large cities in Brazil's urban network and a more unequal distribution of economic activity across cities when compared to USA, indicating a strong correlation between development and occupational structure.

Este artigo compara a estrutura ocupacional das cidades do Brasil e dos Estados Unidos, visando avaliar a extensão em que a estrutura econômica dessas aglomerações urbanas se associa aos diferentes estágios de desenvolvimento, especificamente quando se compara um país rico com um em desenvolvimento. Utilizando um banco de dados ocupacionais harmonizado e microdados do Censo Demográfico Brasileiro de 2010 e do US American Community Survey (2008-2012), os resultados mostram que as cidades brasileiras têm uma conexão mais forte do tamanho da população com a estrutura ocupacional e a distribuição de capital humano do que a observada em cidades nos Estados Unidos. Estes resultados sugerem uma primazia mais forte das grandes cidades na rede urbana do Brasil e uma distribuição mais desigual da atividade econômica entre as cidades quando comparadas aos EUA, indicando uma alta correlação entre desenvolvimento e estrutura ocupacional.

Este documento compara la estructura ocupacional de ciudades de Brasil y Estados Unidos de Norteamérica con el objetivo de evaluar la extensión a la cual la estructura económica de estas aglomeraciones urbanas se asocia en las diferentes etapas de desarrollo, específicamente cuando se compara un país rico con un país en desarrollo. Utilizando una base de datos ocupacional armonizada y microdatos del censo demográfico brasileño de 2010 y de la Encuesta de la Comunidad Estadounidense (2008-2012), los resultados muestran una conexión más fuerte en las ciudades brasileñas entre el tamaño de la población y la estructura ocupacional y la distribución del capital humano que la encontrada para ciudades de los Estados Unidos. Estos hallazgos sugieren una mayor primacía de las grandes ciudades en la red urbana de Brasil y una distribución más desigual de la actividad económica entre las ciudades en comparación con las de Estados Unidos, lo que indica una fuerte correlación entre el desarrollo y la estructura ocupacional.

Spatial Variation in the Quality of American Community Survey Estimates.

Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006-2010 ACS median household income estimates at the census tract scale as a test case, we explore spatial and nonspatial patterns in ACS estimate quality. We find that spatial patterns of uncertainty in the northern United States differ from those in the southern United States, and they are also different in suburbs than in urban cores. In both cases, uncertainty is lower in the former than the latter. In addition, uncertainty is higher in areas with lower incomes. We use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses. We find that these demographic and geographic patterns in estimate quality persist even after we account for the number of responses. Our results indicate that data quality varies across places, making cross-sectional analysis both within and across regions less reliable. Finally, we present advice for data users and potential solutions to the challenges identified.

Reducing uncertainty in the american community survey through data-driven regionalization.

The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold.

Patterns and causes of uncertainty in the American Community Survey.

In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use. This paper explains why the ACS tract and block group estimates have large margins of error. Statistical concepts are explained in plain English. ACS margins of error are attributed to specific methodological decisions made by the Census Bureau. These decisions are best seen as compromises that attempt to balance financial constraints against concerns about data quality, timeliness, and geographic precision. In addition, demographic and geographic patterns in ACS data quality are identified. These patterns are associated with demographic composition of census tracts. Understanding the fundamental causes of uncertainty in the survey suggests a number of geographic strategies for improving the usability and quality ACS.

Identifying Regions Based on Flexible User Defined Constraints.

The identification of regions is both a computational and conceptual challenge. Even with growing computational power, regionalization algorithms must rely on heuristic approaches in order to find solutions. Therefore, the constraints and evaluation criteria that define a region must be translated into an algorithm that can efficiently and effectively navigate the solution space to find the best solution. One limitation of many existing regionalization algorithms is a requirement that the number of regions be selected a priori. The max-p algorithm, introduced in Duque et al. (2012), does not have this requirement, and thus the number of regions is an output of, not an input to, the algorithm. In this paper we extend the max-p algorithm to allow for greater flexibility in the constraints available to define a feasible region, placing the focus squarely on the multidimensional characteristics of region. We also modify technical aspects of the algorithm to provide greater flexibility in its ability to search the solution space. Using synthetic spatial and attribute data we are able to show the algorithm's broad ability to identify regions in maps of varying complexity. We also conduct a large scale computational experiment to identify parameter settings that result in the greatest solution accuracy under various scenarios. The rules of thumb identified from the experiment produce maps that correctly assign areas to their "true" region with 94% average accuracy, with nearly 50 percent of the simulations reaching 100 percent accuracy.