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. 2022 Jan 1;234(1):32-46.
doi: 10.1016/j.jamcollsurg.2021.09.008.

The Structural Violence Trap: Disparities in Homicide, Chronic Disease Death, and Social Factors Across San Francisco Neighborhoods

Affiliations

The Structural Violence Trap: Disparities in Homicide, Chronic Disease Death, and Social Factors Across San Francisco Neighborhoods

Marissa A Boeck et al. J Am Coll Surg. .

Abstract

Background: On average, a person living in San Francisco can expect to live 83 years. This number conceals significant variation by sex, race, and place of residence. We examined deaths and area-based social factors by San Francisco neighborhood, hypothesizing that socially disadvantaged neighborhoods shoulder a disproportionate mortality burden across generations, especially deaths attributable to violence and chronic disease. These data will inform targeted interventions and guide further research into effective solutions for San Francisco's marginalized communities.

Study design: The San Francisco Department of Public Health provided data for the 2010-2014 top 20 causes of premature death by San Francisco neighborhood. Population-level demographic data were obtained from the US American Community Survey 2015 5-year estimate (2011-2015). The primary outcome was the association between years of life loss (YLL) and adjusted years of life lost (AYLL) for the top 20 causes of death in San Francisco and select social factors by neighborhood via linear regression analysis and heatmaps.

Results: The top 20 causes accounted for N = 15,687 San Francisco resident deaths from 2010-2014. Eight neighborhoods (21.0%) accounted for 47.9% of city-wide YLLs, with 6 falling below the city-wide median household income and many having a higher percent population Black, and lower education and higher unemployment levels. For chronic diseases and homicides, AYLLs increased as a neighborhood's percent Black, below poverty level, unemployment, and below high school education increased.

Conclusions: Our study highlights the mortality inequity burdening socially disadvantaged San Francisco neighborhoods, which align with areas subjected to historical discriminatory policies like redlining. These data emphasize the need to address past injustices and move toward equal access to wealth and health for all San Franciscans.

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Figures

None
Graphical abstract
Figure 1.
Figure 1.
Map of San Francisco neighborhoods. Image provided courtesy of the San Francisco Department of Public Health
Figure 2.
Figure 2.
Adjusted years of life lost for the top 20 causes of death in San Francisco by neighborhood. AYLL, adjusted years of life lost; Breast Ca, breast cancer; CAD, ischemic heart disease; COPD, chronic obstructive pulmonary disorder; CRC, cancer colon/rectum; CVA, cerebrovascular accident; DM, diabetes mellitus; Drugs, drug use disorder; EtOH, alcohol-attributable diseases and disorders; HIV, human immunodeficiency virus; HTN, hypertensive diseases; Liver Ca, liver cancer; Lung Ca, lung/trachea/bronchial cancer; Lymph/MM, lymphoma/multiple myeloma; Panc Ca, pancreas cancer; flu/PNA, influenza and pneumonia; Unintent, unintentional, nontransport accidents.
Figure 3.
Figure 3.
Association between adjusted years of life lost (AYLL) for the top 20 causes of death and select area-based social factors across San Francisco neighborhoods. The causes of death on the x axis are ranked by the sum of AYLL across San Francisco neighborhoods from highest to lowest. COPD, chronic obstructive; pulmonary disorder; HIV, human immunodeficiency virus.
Figure 4.
Figure 4.
(A) San Francisco neighborhoods with most adjusted years of life lost (AYLL) for homicide, chronic disease, and all-causes for top 20 causes of death juxtaposed with historical redlining. (B) City and County of San Francisco Home Owner’s Loan Corporation (HOLC) Residential Security Map (1937). Image provided courtesy of the San Francisco Department of Public Health
Figure 5.
Figure 5.
Adjusted years of life lost (AYLL) for the top 20 causes of death, percent population Black (%pop black), and percent population below the poverty level (PBPL) across San Francisco neighborhoods. The hierarchies are generated by the hierarchical clustering algorithm, which clusters similar groups based on a distance matrix. The x axis hierarchical clustering groups similar neighborhoods and the y-axis groups similar AYLLs. COPD, chronic obstructive pulmonary disorder; HIV, human immunodeficiency virus; USF, University of San Francisco.

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