Poverty, affluence, and income inequality: neighborhood economic structure and its implications for health

Wen, Ming; Browning, Christopher R.; Cagney, Kathleen A.

doi:10.1016/s0277-9536(02)00457-4

Cited by 349 publications

(291 citation statements)

References 56 publications

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“…The magnitude of differences across the income spectrum and concentrated wealth or poverty represent different kinds of inequalities that can operate differently at individual, community or larger geographic levels [26]. Income inequality has been associated with health disparities [27] but the pathways through which inequality operates are not fully understood [28] [29].…”

Section: Discussionmentioning

confidence: 99%

Characteristics of US Counties with No Mammography Capacity

et al. 2012

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Access to screening mammography may be limited by the availability of facilities and machines, and nationwide mammography capacity has been declining. We assessed nationwide capacity at state and county levels from 2003 to 2009, the most recent year for which complete data were available. Using mammography facility certification and inspection data from the Food and Drug Administration, we geocoded all mammography facilities in the United States and determined the total number of fully accredited mammography machines in each US County. We categorized mammography capacity as counties with zero capacity (i.e., 0 machines) or counties with capacity (i.e.,≥1 machines), and then compared those two categories by sociodemographic, health care, and geographic characteristics. We found that mammography capacity was not distributed equally HHS Public Access Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript across counties within states and that more than 27 % of counties had zero capacity. Although the number of mammography facilities and machines decreased slightly from 2003 to 2009, the percentage of counties with zero capacity changed little. In adjusted analyses, having zero mammography capacity was most strongly associated with low population density (OR = 11.0; 95 % CI 7.7-15.9), low primary care physician density (OR = 8.9; 95 % CI 6.8-11.7), and a low percentage of insured residents (OR = 3.3; 95 % CI 2.5-4.3) when compared with counties having at least one mammography machine. Mammography capacity has been and remains a concern for a portion of the US population-a population that is mostly but not entirely rural.

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Section: Discussionmentioning

confidence: 99%

Characteristics of US Counties with No Mammography Capacity

et al. 2012

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“…The role of the local environment plays in determining birth outcomes is ambiguous. There is evidence that the local environment influences the health of different populations in different ways (Elliot et al, 1996;Ahern et al, 2003;Wen et al, 2003;Beckman et al, 2004;Diez-Roux et al, 2004;Sundquist et al, 2004). However, too few studies have examined the influence of culture, contamination, housing, or healthcare and other features of the local environment over infant prematurity, low birthweight, or mortality (Matteson et al, 1998;Kaufman et al, 2003;Savitz et al, 2004).…”

Section: Understanding the Local Environment And Its Role In Birth Oumentioning

confidence: 99%

“…Analyzing the impact of the local environment using multi-level modeling For these reasons, HLM is often used to study the relationship between community level factors and individual outcomes in health outcomes (Novak and Clayton, 2001;Blakely et al, 2002;McLaren and Gauvin, 2002;Wen et al, 2003;Cherpitel et al, 2004). Epidemiologists have applied this approach rather sparingly (Matteson et al, 1998;Rosenheck and Stolar, 1998;Diez-Roux, 2001;Pickett and Pearl, 2001;Beland et al, 2002;Blakely et al, 2002;Ahern et al, 2003;Kaufman et al, 2003;Wen et al, 2003;DiezRoux et al, 2004;Savitz et al, 2004;Sundquist et al, 2004;Diez-Roux and Aiello, 2005).…”

Section: Understanding the Local Environment And Its Role In Birth Oumentioning

confidence: 99%

Assessing the impact of the local environment on birth outcomes: a case for HLM

Williams

Pennock-Román

Suen

et al. 2006

J Expo Sci Environ Epidemiol

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Hierarchical linear Models (HLM) is a useful way to analyze the relationships between community level environmental data, individual risk factors, and birth outcomes. With HLM we can determine the effects of potentially remediable environmental conditions (e.g., air pollution) after controlling for individual characteristics such as health factors and socioeconomic factors. Methodological limitations of ecological studies of birth outcomes and a detailed analysis of the varying models that predict birth weight will be discussed. Ambient concentrations of criterion air pollutants (e.g., lead and sulfur dioxide) demonstrated a sizeable negative effect on birth weight; while the economic characteristics of the mother's residential census tract (ex. poverty level) also negatively influenced birth weight.

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“…Economic conditions are measured as a county's percentage of children in poverty and the Gini coefficient of income inequality. 17 Aggregate socioeconomic factors, interpreted as indicating problems in an area or lack of access to resources, 23 have frequently been used in neighborhood effects research to assess health status, 67 with such factors often predicting worse individuallevel health or physical functioning. Socioeconomic status has been interpreted variously-area unemployment percentage and proportion of below-poverty level residents have been interpreted as proxy measures for crime, stress, and inadequate housing resources.…”

Section: Methodsmentioning

confidence: 99%

“…Fourth, the measures used to operationalize the concepts of the physical, social, and institutional environments from the adapted conceptual model of Wen et al 17 are limited. It was not possible to observationally assess the built environments of each county or assess residents' perceptions of either the built or social environments (such as levels of social support).…”

Section: Limitationsmentioning

confidence: 99%

Examining Contextual Influences on Fall-Related Injuries Among Older Adults for Population Health Management

Hoffman

Rodríguez

2015

Population Health Management

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The objectives were to assess the associations between fall-related injuries (FRIs) treated in the emergency department (ED) among older adults in California and contextual county-level physical, social, and economic characteristics, and to assess how county-level economic conditions are associated with FRIs when controlling for other county-level factors. Data from 2008 California ED discharge, Medicare Impact File, and County Health Rankings were used. Random effects logistic regression models estimated contextual associations between county-level factors representing economic conditions, the built environment, community safety, access to care, and obesity with patient-level FRI treatment among 1,712,409 older adults, controlling for patient-level and hospital-level characteristics. Patient-level predictors of FRI treatment were consistent with previous studies not accounting for contextual associations. Larger and rural hospitals had higher odds of FRI treatment, while teaching and safety net hospitals had lower odds. Better county economic conditions were associated with greater odds (ß = 0.73, P = 0.001) and higher county-level obesity were associated with lower odds (ß = -0.37, P = 0.004), but safer built environments (ß = -0.31, P = 0.38) were not associated with FRI treatment. The magnitude of association between county-level economic conditions and FRI treatment attenuated with the inclusion of countylevel obesity rates. FRI treatment was most strongly and consistently related to more favorable county economic conditions, suggesting differences in treatment or preferences for treatment for FRIs among older individuals in communities of varying resource levels. Using population health data on FRIs, policy makers may be able to remove barriers unique to local contexts when implementing falls prevention educational programs and built environment modifications. (Population Health Management 2015;18:437-448)

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Poverty, affluence, and income inequality: neighborhood economic structure and its implications for health

Cited by 349 publications

References 56 publications

Characteristics of US Counties with No Mammography Capacity

Characteristics of US Counties with No Mammography Capacity

Assessing the impact of the local environment on birth outcomes: a case for HLM

Examining Contextual Influences on Fall-Related Injuries Among Older Adults for Population Health Management

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