Time and Distance Barriers to Mammography Facilities in the Atlanta Metropolitan Area

Peipins, Lucy A.; Graham, Shannon; Young, Randall; Lewis, Brian; Foster, Stanley O.; Flanagan, Barry; Dent, Andrew L.

doi:10.1007/s10900-011-9359-5

Cited by 77 publications

(67 citation statements)

References 38 publications

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“…Income inequality, mammography capacity, and the proportion of digital machines were largest for the metropolitan counties. While services may be present in metropolitan counties with high income inequalities as demanded by high income groups, these services may not be accessible to the lowest income groups within that county whose dependence on public transportation may limit access [31]. Household income inequality ratio was the same for rural capacity and zero-capacity counties.…”

Section: Discussionmentioning

confidence: 90%

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: 90%

Characteristics of US Counties with No Mammography Capacity

et al. 2012

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“…Weinreb et al (2006) found that caregivers, females in particular, who live in communities that lack social capital (i.e., social support, financial resources, safe neighborhoods) tend to utilize fewer community-based health services and may rely extensively on short-term emergency/urgent care services (Weinreb et al 2006). Furthermore, communities characterized by instability and poverty are likely to experience racial disparities in mental health services for youth (Gudiño et al 2009) and lack easily accessible, high quality healthcare (Dai 2010;Guagliardo et al 2004;Lillie-Blanton and Hoffman 2005;Peipins et al 2011).…”

Section: Caregiver Service Utilization and Child Mental Healthmentioning

confidence: 99%

Community Adversity and Children’s Mental Health: Moderating Effects of Caregiver Service Utilization and Race on Children’s Internalizing and Externalizing Problems

Briggs

Quinn

Orellana

et al. 2015

Child Adolesc Soc Work J

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This study examined the relationship between community adversity and childhood internalizing and externalizing problems, and whether this relationship is moderated by service utilization and race. The study involved 3225 children ages 2-17, and their caregivers, who participated in the second cohort of the National Survey on Child and Adolescent Well-Being. Bivariate correlation and regression analysis indicate that childhood internalizing problems were correlated with sex and community adversity. Children externalizing problems were correlated with race, sex, service utilization, and community adversity. Childhood internalizing and externalizing problems were highly correlated. Regression analysis reveals a strong relationship between community adversity and childhood externalizing problems. Service utilization was found to significantly and positively moderate the relationship between community adversity and externalizing problems. While a strong correlation between race and childhood externalizing problems exist, race did not moderate the relationship between community adversity and externalizing problems. Internalizing problems were excluded from the regression analysis since it did not correlate with any of the moderator variables. The findings are discussed and the study limitations are examined. Implications for child welfare practice, policy, and research are highlighted.

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“…Using these networks, we calculated travel times to radiation facilities via public and private vehicle transportation from a population-weighted center of each of the 282 census tracts located within the 2-county area served by the Metropolitan Atlanta Rapid Transit Authority (MARTA). A full description of the construction of the multimodal public transportation models has been published previously (Peipins et al, 2011).…”

Section: Overviewmentioning

confidence: 99%

Racial disparities in travel time to radiotherapy facilities in the Atlanta metropolitan area

Peipins

Graham

Young

et al. 2013

Social Science & Medicine

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Low-income women with breast cancer who rely on public transportation may have difficulty in completing recommended radiation therapy due to inadequate access to radiation facilities. Using a geographic information system (GIS) and network analysis we quantified spatial accessibility to radiation treatment facilities in the Atlanta, Georgia metropolitan area. We built a transportation network model that included all bus and rail routes and stops, system transfers and walk and wait times experienced by public transportation system travelers. We also built a private transportation network to model travel times by automobile. We calculated travel times to radiation therapy facilities via public and private transportation from a population-weighted center of each census tract located within the study area. We broadly grouped the tracts by low, medium and high household access to a private vehicle and by race. Facility service areas were created using the network model to map the extent of areal coverage at specified travel times (30, 45 and 60 min) for both public and private modes of transportation. The median public transportation travel time to the nearest radiotherapy facility was 56 min vs. approximately 8 min by private vehicle. We found that majority black census tracts had longer public transportation travel times than white tracts across all categories of vehicle access and that 39% of women in the study area had longer than 1 h of public transportation travel time to the nearest facility. In addition, service area analyses identified locations where the travel time barriers are the greatest. Spatial inaccessibility, especially for women who must use public transportation, is one of the barriers they face in receiving optimal treatment.

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Time and Distance Barriers to Mammography Facilities in the Atlanta Metropolitan Area

Cited by 77 publications

References 38 publications

Characteristics of US Counties with No Mammography Capacity

Characteristics of US Counties with No Mammography Capacity

Community Adversity and Children’s Mental Health: Moderating Effects of Caregiver Service Utilization and Race on Children’s Internalizing and Externalizing Problems

Racial disparities in travel time to radiotherapy facilities in the Atlanta metropolitan area

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