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J Community Health ; 25(1): 79-88, 2000 Feb.
Article in English | MEDLINE | ID: mdl-10706211

ABSTRACT

Increasing concerns regarding the cost of medical care have led to research that has found a relationship between alcohol abuse, increased medical problems, longer hospital stays, and higher medical costs. Research has also found a positive relationship between alcohol availability and crime, car accidents, and liver cirrhosis deaths. One area of interest is how alcohol availability, as measured by the number of alcohol outlets, is related to medical care needs. The purpose of this study was to examine the relationship between the geographic density of alcohol outlets and the number of alcohol-related hospital admissions. Alcohol-related ICD-9 codes were selected based on epidemiologic research in the literature to determine alcohol-related morbidity from the California Discharge Data System, which collects information on all hospital admissions and discharges in California. In San Diego County, in 1996, 3,759 admissions were alcohol-related. Alcohol-related admissions for each zip code were compared to the number of liquor licenses that were held by each zip code through a multiple regression analysis. The regression model demonstrated that the number of liquor outlets was a significant predictor of alcohol-related hospital admissions, net of other predictors. Implications are discussed, including regulation of alcohol availability, which may have a beneficial impact on alcohol morbidity.


Subject(s)
Alcoholic Beverages/supply & distribution , Alcoholism/epidemiology , Commerce/statistics & numerical data , Patient Admission/statistics & numerical data , Adult , Age Distribution , Aged , Alcoholism/etiology , California/epidemiology , Diagnosis-Related Groups/statistics & numerical data , Female , Humans , Least-Squares Analysis , Licensure/statistics & numerical data , Male , Middle Aged , Morbidity , Multivariate Analysis , Needs Assessment , Predictive Value of Tests , Residence Characteristics/statistics & numerical data , Risk Factors , Sex Distribution
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