Alcohol Outlet Clusters and Population Disparities

Abstract

Alcohol outlet clusters are an important social determinant of health in cities, but little is known about the populations exposed to them. If outlets cluster in neighborhoods comprised of specific racial/ethnic or economic groups, then they may function as a root cause of urban health disparities. This study used 2016 liquor license data (n = 1204) from Baltimore City, Maryland, and demographic data from the American Community Survey. We defined alcohol outlet clusters by combining SaTScan moving window methods and distances between outlets. We used multiple logistic regression to compare census block groups (CBGs) (n = 537) inside and outside of four types of outlet clusters: total, onpremise, off-premise, and LBD-7 (combined on-/ off-premise). The most robust predictor of alcohol outlet cluster membership was a history of redlining, i.e., racially discriminatory lending policies. CBGs that were redlined had 7.32 times the odds of being in an off-premise cluster, 8.07 times the odds of being in an on-premise cluster, and 8.60 times the odds of being in a LBD-7 cluster. In addition, level of economic investment (marked by vacant properties) appears to be a key characteristic that separates CBGs in on-and off-premise outlet clusters. CBGs with racial/ethnic or socioeconomic advantage had higher odds of being in on-premise clusters and CBGs marked by disinvestment had higher odds of being in off-premise clusters. Off-premise clusters deserve closer examination from a policy perspective, to mitigate their potential role in creating and perpetuating social and health disparities. In addition to addressing redlining and disinvestment, the current negative effects of alcohol outlet clusters that have grown up in redlined and disinvested areas must be addressed if inequities in these neighborhoods are to be reversed.

Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States

“…The CDC collects health outcome data on the state level, categorizing the data with Federal Information Processing (FIPS) two-digit codes that each identify a United States state or territory. We retrieved 12 outcome indicators: (1) obesity and (2) overweight status of adults (percentage of adults aged 18 years or older with an obesity and overweight classification, respectively, in 2017), (3) obesity and (4) overweight status of adolescents (percentage of teenagers in grades 9-12 with an obesity and overweight classification, respectively, in 2017), (5) diabetes in adults (prevalence of adults aged 18 years or older with diagnosed diabetes in 2016), ( 6) aerobic physical activity in adults (percentage of adults who had at least 150 minutes of moderately intense aerobic physical activity, or 75 minutes of vigorously intense aerobic activity, or an equivalent combination per week in 2017), (7) daily physical activity in adolescents (percentage of teenagers in grades 9-12 who had one hour or more of moderate to vigorous physical activity on a daily basis in 2017), (8) premature mortality (cases of premature mortality per 100,000 among adults aged 45 to 64 years), (9) diabetes mortality (cases of diabetes-related mortality per 100,000 in 2014), (10) cardiovascular disease mortality (cases of cardiovascular disease-related mortality per 100,000 in 2014), (11) park access (percent of USA population living within 1/2 mile of a park in 2015), and (12) youth recreational access (percent of youth with playgrounds, community centers, or sidewalks in their neighborhood in 2016). The classification of overweight and obesity is based on body mass index (BMI), with overweight classified between 25 and 30, and 30 or greater classified as obese.…”

“…Discriminatory policies such as redlining, a real estate practice discriminating against money or credit borrowers from certain areas with poverty in the United States, has resulted in uneven urban development, creating distinct geographic areas that still face systemic disadvantages [8]. Many of these neighborhoods have built environment features that are detrimental to residents' long-term health, such as more alcohol outlets [9] and fast food restaurants, fewer recreational facilities [10], and higher levels of intra-urban heat [11].…”

Cancer Stage at Diagnosis, Historical Redlining, and Current Neighborhood Characteristics: Breast, Cervical, Lung, and Colorectal Cancers, Massachusetts, 2001–2015

“…Not merely of historical interest, such questions are highly relevant to current policy debates about potential remedies ( 1 , 2 , 11 , 12 ). To date, 4 published studies, each focused on single cities, have documented associations between historical redlining and health outcomes: tuberculosis incidence in 1951 in Austin, Texas ( 13 ); firearm injury rates in 2013–2014 in Philadelphia, Pennsylvania ( 14 ); self-rated health in 2008–2013 in Detroit, Michigan ( 15 ); and alcohol outlet clusters in 2016 in Baltimore, Maryland ( 16 ), in addition to 2 conference abstracts pertaining to asthma ( 17 , 18 ).…”

The legacy of structural racism: Associations between historic redlining, current mortgage lending, and health

“…HOLC ‘redlining’ has been associated with pre-term birth ( Krieger, Van Wye, et al, 2020 ), later stage cancer diagnosis ( Krieger, Wright, et al, 2020 ), higher rates of emergency department visits for asthma ( Nardone et al, 2020 ), and poorer self-reported health ( McClure et al, 2019 ). HOLC ‘redlining’ has also been associated with neighborhood determinants of health, including alcohol outlet clusters ( Trangenstein et al, 2020 ), urban violence ( Jacoby et al, 2018 ), less tree canopy and more airborne hazards ( Namin et al, 2020 ), and higher intra-urban heat ( Hoffman et al, 2020 ). McClure et al ( McClure et al, 2019 ) is the only other study that we are aware of that has evaluated HOLC ‘redlining’ in relation to current housing instability (i.e., post Great Recession foreclosure rates) and health.…”