Spatial autocorrelation and data uncertainty in the American Community Survey: a critique

Jung, Paul H.; Thill, Jean‐Claude; Issel, L. Michele

doi:10.4324/9781003123842-3

Cited by 4 publications

(5 citation statements)

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“…The ADI and the SVI rely fully on ACS data, while the CCVI and the COI incorporate additional data sources and indicators. Although the ACS is the most extensive national source of demographic and economic data currently available, there are critical challenges with ACS data and data collection 43,44 . The ACS uses imprecise small‐area estimates that often lead to large margins of error and uncertainty, rendering the data unreliable 45 .…”

Section: Discussionmentioning

confidence: 99%

“…Although the ACS is the most extensive national source of demographic and economic data currently available, there are critical challenges with ACS data and data collection. 43,44 The ACS uses imprecise small-area estimates that often lead to large margins of error and uncertainty, rendering the data unreliable. 45 Spielman and Folch have demonstrated key drivers of uncertainty in ACS data and proposed strategies for ameliorating some of these concerns, including development of an open-source spatial optimization algorithm to improve the usability of ACS and other survey data.…”

Section: Methodological Challenges: Underlying Data Source Index Cons...mentioning

confidence: 99%

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Assessment of Population‐Level Disadvantage Indices to Inform Equitable Health Policy

Kaalund¹,

Thoumi²,

Bhavsar

et al. 2022

Milbank Quarterly

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The rapid uptake of disadvantage indices during the pandemic highlights investment in implementing tools that address health equity to inform policy. Existing indices differ in their design, including data elements, social determinants of health domains, and geographic unit of analysis. These differences can lead to stark discrepancies in place‐based social risk scores depending on the index utilized. Disadvantage indices are useful tools for identifying geographic patterns of social risk; however, indiscriminate use of indices can have varied policy implications and unintentionally worsen equity. Implementers should consider which indices are suitable for specific communities, objectives, potential interventions, and outcomes of interest. Context There has been unprecedented uptake of disadvantage indices such as the Centers for Disease Control and Prevention Social Vulnerability Index (SVI) to identify place‐based patterns of social risk and guide equitable health policy during the COVID‐19 pandemic. However, limited evidence around data elements, interoperability, and implementation leaves unanswered questions regarding the utility of indices to prioritize health equity. Methods We identified disadvantage indices that were (a) used three or more times from 2018 to 2021, (b) designed using national‐level data, and (c) available at the census‐tract or block‐group level. We used a network visualization to compare social determinants of health (SDOH) domains across indices. We then used geospatial analyses to compare disadvantage profiles across indices and geographic areas. Findings We identified 14 indices. All incorporated data from public sources, with half using only American Community Survey data (n = 7) and the other half combining multiple sources (n = 7). Indices differed in geographic granularity, with county level (n = 5) and census‐tract level (n = 5) being the most common. Most states used the SVI during the pandemic. The SVI, the Area Deprivation Index (ADI), the COVID‐19 Community Vulnerability Index (CCVI), and the Child Opportunity Index (COI) met criteria for further analysis. Selected indices shared five indicators (income, poverty, English proficiency, no high school diploma, unemployment) but varied in other metrics and construction method. While mapping of social risk scores in Durham County, North Carolina; Cook County, Illinois; and Orleans Parish, Louisiana, showed differing patterns within the same locations depending on choice of disadvantage index, risk scores across indices showed moderate to high correlation (rs 0.7‐1). However, spatial autocorrelation analyses revealed clustering, with discrepant distributions of social risk scores between different indices. Conclusions Existing disadvantage indices use varied metrics to represent place‐based social risk. Within the same geographic area, different indices can provide differences in social risk values and interpretations, potentially leading to varied public health or policy responses.

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Section: Discussionmentioning

confidence: 99%

Section: Methodological Challenges: Underlying Data Source Index Cons...mentioning

confidence: 99%

Assessment of Population‐Level Disadvantage Indices to Inform Equitable Health Policy

Kaalund¹,

Thoumi²,

Bhavsar

et al. 2022

Milbank Quarterly

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“…One possible approach would be to use demographic data based on higher survey frequency, such as 5‐year estimates at the census‐tract level of American Community Survey data without overlapping of sampling‐year windows. However, that survey’s high degree of data uncertainty should be treated appropriately (Spielman, Folch, and Nagle 2014; Folch et al 2016; Jung, Thill, and Issel 2019a), using advanced statistical tools—such as empirical or hierarchical Bayesian estimations—with uncertain information (Jung, Thill, and Issel 2019b). Combining the FDA approach with empirical or hierarchical Bayesian estimations in future research would enable the production of multivariate neighborhood curves that represent neighborhood change with greater accuracy.…”

Section: Discussionmentioning

confidence: 99%

Multivariate Neighborhood Trajectory Analysis: An Exploration of the Functional Data Analysis Approach

Jung

Song

2021

Geographical Analysis

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Recent neighborhood studies have focused on longitudinal aspects of neighborhood change and data‐mining methodologies that identify neighborhood trajectory patterns using time‐series multivariate census data. Existing neighborhood trajectory models capture neighborhood change by stacking cross‐sectional neighborhood clustering results across years and analyzing the discrete stepwise switching patterns between the clusters. Taking a different approach, we employ the functional data analysis (FDA) method to analyze longitudinal patterns of neighborhood change from mathematically represented multivariate time‐dependent curves to identify neighborhood trajectory clusters. This FDA‐based neighborhood trajectory model incorporates a multivariate functional principal component analysis and k‐means clustering. We have applied our model to neighborhoods in the Charlotte and Detroit metropolitan areas to identify ongoing racial and socioeconomic segregation patterns and the time dynamics of neighborhood change.

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“…Although much spatial statistical work has been done in the field of census analysis, methodological concerns continue to be raised. For example, using the American Community Survey, Jung et al (2019a) criticise the inappropriate use of spatial statistics, particularly in the context of rates, an issue discussed in Section 5.1. Yet, explicitly spatial techniques are increasingly being used but are still not as widespread as they probably should be, given the spatial nature of census data.…”

Section: Census Analysesmentioning

confidence: 99%

Exploratory Statistical Analysis of Spatial Structures in Urban Datasets

Westerholt¹

2022

Metropolitan Research

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Spatial autocorrelation and data uncertainty in the American Community Survey: a critique

Cited by 4 publications

References 0 publications

Assessment of Population‐Level Disadvantage Indices to Inform Equitable Health Policy

Assessment of Population‐Level Disadvantage Indices to Inform Equitable Health Policy

Multivariate Neighborhood Trajectory Analysis: An Exploration of the Functional Data Analysis Approach

Exploratory Statistical Analysis of Spatial Structures in Urban Datasets

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