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Much research has been conducted that demonstrates a link between racial/ethnic residential segregation and health care outcomes. We suggest that minority segregated neighborhoods may have diminished access to organizations and that this differential access may contribute to differences in health care outcomes across communities. We analyze this specifically using the case of pediatric health care provider choice. To examine this association, we estimate a series of multinomial logistic regression models using restricted data with ZIP code level geoidentifiers from the 2011–2012 National Survey of Children’s Health (NSCH). We find that racial/ethnic residential segregation is related to a greater reliance on non-ideal forms of health care, such as clinics, and hospital outpatient departments, instead of pediatric physician’s offices. This association is at least partially attenuated by the distribution of health care facilities in the local area, physician’s offices, and health care practitioners in particular. Additionally, families express greater dissatisfaction with these other forms of care compared to physician’s offices, demonstrating that the lack of adequate health care provision is meaningful for health care outcomes. This study expands the literature by examining how the siting of health organizations has consequences for individuals residing within these areas.
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Residential Segregation, Neighborhood Health Care Organizations, and Children's Health Care Utilization in the Phoenix Urbanized Area
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Previous work has suggested that minority segregated neighborhoods may have diminished access to health care, and that this may contribute to differences in health outcomes across communities. We analyze this using the case of pediatric provider choice. We estimate a series of multinomial logistic regression models using restricted data from the 2011-2012 National Survey of Children’s Health (NSCH). We find that racial/ethnic residential segregation is related to a greater reliance on non-ideal forms of health care, such as clinics and hospital outpatient departments, instead of pediatric physician’s offices. Additionally, families express greater dissatisfaction with these other forms of care.more » « less
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Puerto Rico was hit by a category 4 hurricane that severely damaged power, water, and communications systems on the 20th of September 2017. Based on 56 qualitative interviews, this article documents how health care workers created a new ethics of care after Hurricane Maria and engaged in novel forms of health activism to both repair past damage and imagine a different future. Many doctors, nurses, and other health care professionals went to work after the storm treating patients, fixing their workplaces, and resolving logistical problems. Health care workers responded emotionally to the event by finding meaning and purpose in their work, forging a sense of solidarity, and valuing their ability to help others. Our respondents used the term compromiso to describe their determination and sense of purpose, and we borrow this term to label the specific ethics of care generated from their experiences after Maria.more » « less
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Improving the performance and explanations of ML algorithms is a priority for adoption by humans in the real world. In critical domains such as healthcare, such technology has significant potential to reduce the burden on humans and considerably reduce manual assessments by providing quality assistance at scale. In today’s data-driven world, artificial intelligence (AI) systems are still experiencing issues with bias, explainability, and human-like reasoning and interpretability. Causal AI is the technique that can reason and make human-like choices making it possible to go beyond narrow Machine learning-based techniques and can be integrated into human decision-making. It also offers intrinsic explainability, new domain adaptability, bias free predictions, and works with datasets of all sizes. In this tutorial of type lecture style, we detail how a richer representation of causality in AI systems using a knowledge graph (KG) based approach is needed for intervention and counterfactual reasoning (Figure 1), how do we get to model-based and domain explainability, how causal representations helps in web and health care.more » « less
