Discrepancies in Local, State, and National Alcohol Outlet Listings
Implications for Research and Interventions
Bibliographic Data
| ID | 21645045 |
|---|---|
| Authors | Adam J Milam (0000-0001-9306-1598, Michigan State University, corresponding author), Clara B Barajas (0000-0003-4361-9164, Michigan State University), Zachary Buchalski (Michigan State University), Ling Wang (0000-0002-5015-7706, Michigan State University), Richard C Sadler (0000-0002-5130-8561, Michigan State University), C Debra M Furr-Holden (0000-0002-9337-9886, Michigan State University) |
| Year | 2020 |
| Volume | 55 |
| Issue | 14 |
| Pages | 2348-2356 |
| Publication date | 2020-11-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Substance Use & Misuse (JOURNAL) |
| Journal identifiers | ISSN: 1082-6084 • E-ISSN: 1532-2491 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10826084.2020.1817080 |
| PMID | 32917123 |
| OpenAlex | W3084513220 |
| Language | EN |
| Citations received | 3 |
| References cited | 33 |
Background The availability of local, state, and national data on alcohol outlet density have important implications for policies and interventions aiming to reduce alcohol-related problems. High-quality data on locations of alcohol outlets is important to accurately inform community interventions and public health initiatives, but such data is often not maintained, readily available, or of sufficient quality. Objectives: This study aims to examine the discrepancies between alcohol outlet databases and how neighborhood characteristics (i.e. income, majority racial population, urbanicity) are associated with the discrepancies between databases. Methods: Data was collected from national (n = 1), local (n = 2), and state databases (n = 3). Negative binomial regression models were used to assess discrepancies in alcohol outlet count at the ZIP code level based on the data source. Results: The average density of alcohol outlets (per 1000 residents) ranged from 0.71 to 2.17 in Maryland, 1.65 to 5.17 in Wisconsin, and 1.09 to 1.22 in Oregon based on different sources of data. Findings suggest high income areas (>200% poverty level) have fewer discrepancies (IR = 0.775, p < 0.01), low income areas (below poverty level) have greater discrepancies (IR = 4.990, p < 0.01), and urban areas tend to have fewer discrepancies (IR = 0.378, p < 0.01) between datasets. Conclusion: Interventions and policies depend on valid and reliable data; researchers, policymakers, and local agencies need to collaborate to develop methods to maintain accurate and accessible data
Business · Economic growth · Economics · Environmental health · Geography · Population · Poverty · Psychological intervention · Public health · Sociology · Statistics · Survey data collection · Alcohol Consumption and Health Effects · Demography · Homelessness and Social Issues · Mathematics · Medicine · Substance Abuse Treatment and Outcomes · Gerontology
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| Unique citing works | 3 |
|---|---|
| Citations per year | 0.6 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |