Under-representation of certain disability groups by the American Community Survey (ACS)

Hall, Jean P.; Ipsen, Catherine; Kurth, Noelle K.; Chambless, Cathy; McCormick, Sara; Goe, Rebecca

doi:10.1016/j.dhjo.2017.06.001

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…In the extreme case, VGI has the potential to deepen rather than ameliorate differentials in place-based resources devoted to certain locales through policy decisions if neighborhoods where a large portion of trees are unrepresented are neglected due to their lack of representation in urban forest VGI datasets. The uneven representation in VGI coverage extends beyond urban forests [1,14,16], and may reinforce inequalities in representation from traditional data sources, where marginalized groups and areas are not always counted [1,2,4,21,49]. While there are concerns around privacy and surveillance by both the state and capital with the growing amount of digital traces generated through VGI and other sources [1,2,17], documenting the presence and condition of urban trees seems to provide a positive increase in legibility.…”

Section: Discussionmentioning

confidence: 99%

Citizen Science for Urban Forest Management? Predicting the Data Density and Richness of Urban Forest Volunteered Geographic Information

2017

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Volunteered geographic information (VGI) has been heralded as a promising new data source for urban planning and policymaking. However, there are also concerns surrounding uneven levels of participation and spatial coverage, despite the promotion of VGI as a means to increase access to geographic knowledge production. To begin addressing these concerns, this research examines the spatial distribution and data richness of urban forest VGI in Philadelphia, Pennsylvania and San Francisco, California. Using ordinary least squares (OLS), general linear models (GLM), and spatial autoregressive models, our findings reveal that sociodemographic and environmental indicators are strong predictors of both densities of attributed trees and data richness. Although recent digital urban tree inventory applications present significant opportunities for collaborative data gathering, innovative research, and improved policymaking, asymmetries in the quantity and quality of the data may undermine their effectiveness. If these incomplete and uneven datasets are used in policymaking, environmental justice issues may arise.

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Section: Discussionmentioning

confidence: 99%

Citizen Science for Urban Forest Management? Predicting the Data Density and Richness of Urban Forest Volunteered Geographic Information

2017

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“…The National Survey on Health and Disability (NSHD) is a nationally representative, internet-based survey of adults with disabilities 18 to 62 years of age that provides data related to health insurance and access to health care (Hall & Kurth, 2019). The 2018 NSHD used a purposive convenience sampling approach and was fielded from February to June of 2018.…”

Section: Methodsmentioning

confidence: 99%

Examining Differences in Self-Reported Health and Access to Health Care Among People With Early- and Late-Onset Disability

Goddard

Hall

Kurth

2022

Journal of Disability Policy Studies

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The purpose of the study is to compare self-reported health and access to health care among people with early- and late-onset disability. Adults with disabilities 18 to 62 years of age participated in the 2018 National Survey on Health and Disability (NSHD), which is a nationally representative, internet-based survey that provides data related to health insurance and access to health care. A subsample of participants who reported age at disability onset ( n = 1,188) completed measures related to demographics, health status, health insurance, and access to health care services. Results show that people with early-onset disability were significantly more likely to receive Supplemental Security Income (SSI; p < .001), less likely to receive Social Security Disability Insurance (SSDI; p < .001), more likely to have Medicaid ( p < .001), and less likely to have Medicare ( p < .01). People with late-onset disability were more likely to report service limitations and were more likely to report fair/poor health ( p < .001). Overall, study findings suggest that people with late-onset disability report higher out-of-pocket health care expenditures, greater access limitations, and poorer health. Medical professionals and disability service providers should be mindful about assisting this population in locating appropriate health insurance coverage, health services, and financial assistance.

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Under-representation of certain disability groups by the American Community Survey (ACS)

Cited by 2 publications

References 2 publications

Citizen Science for Urban Forest Management? Predicting the Data Density and Richness of Urban Forest Volunteered Geographic Information

Citizen Science for Urban Forest Management? Predicting the Data Density and Richness of Urban Forest Volunteered Geographic Information

Examining Differences in Self-Reported Health and Access to Health Care Among People With Early- and Late-Onset Disability

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