Who Are the Local Policy Innovators? Cluster Analysis of Municipal Tobacco Control Policies in Massachusetts
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved..
CONTEXT: Tobacco use is a leading cause of preventable death, yet it is challenging to establish public policy to reduce tobacco use. Massachusetts has been a national leader in tobacco control, and its policy-making patterns can be informative to the country.
OBJECTIVE: To identify factors associated with the adoption of tobacco policy within 351 Massachusetts municipalities.
DESIGN: We obtained the 2019 Massachusetts municipality-level tobacco control policy information from Massachusetts' Tobacco Automated Fact Sheet Information system and compiled it with data from American Community Survey, Massachusetts Municipal Association, and Massachusetts state government's Web sites. We used k -means clustering method to identify statistical clustering patterns and hotspot analysis (Getis-Ord Gi*) and Local Indicators of Spatial Association to identify geographic clustering patterns. We then performed multinomial logistic regression to identify factors associated with policy clusters.
SETTING: Massachusetts.
PARTICIPANTS: Three hundred fifty-one municipalities in Massachusetts.
MAIN OUTCOME MEASURE: Policy clusters-groups of municipalities with similar tobacco control policy behaviors.
RESULTS: Based on the k -means analyses, we identified 3 clusters in Massachusetts municipal tobacco control policy behaviors: 54% (N = 191) of municipalities were "Policy Leaders" with a high adoption rate of the 6 tobacco control policies; 18% (N = 63) were "Peer-Influenced Actors" focused on tobacco purchase restrictions for individuals younger than 21 years; and 28% (N = 97) were "Policy Non-Actors," with no tobacco control policies in place. Policy Leaders were geographically clustered in larger cities and the MetroWest region. Policy Non-Actors were clustered in rural areas of Western and Central Massachusetts. Larger municipal population size, higher municipal tax income, and higher percentages of residents voting Democratic were associated with higher policy adoption activities.
CONCLUSIONS: Local variation in the adoption of tobacco policies may exacerbate inequities in tobacco use and population health. Opportunities remain to implement additional tobacco control regulations at the local level to promote public health.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
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| Enthalten in: |
Journal of public health management and practice : JPHMP - 29(2023), 2 vom: 29. März, Seite 151-161 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Feng, Wenhui [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 01.02.2023 Date Revised 13.12.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1097/PHH.0000000000001649 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM347299113 |
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| 500 | |a Date Revised 13.12.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved. | ||
| 520 | |a CONTEXT: Tobacco use is a leading cause of preventable death, yet it is challenging to establish public policy to reduce tobacco use. Massachusetts has been a national leader in tobacco control, and its policy-making patterns can be informative to the country | ||
| 520 | |a OBJECTIVE: To identify factors associated with the adoption of tobacco policy within 351 Massachusetts municipalities | ||
| 520 | |a DESIGN: We obtained the 2019 Massachusetts municipality-level tobacco control policy information from Massachusetts' Tobacco Automated Fact Sheet Information system and compiled it with data from American Community Survey, Massachusetts Municipal Association, and Massachusetts state government's Web sites. We used k -means clustering method to identify statistical clustering patterns and hotspot analysis (Getis-Ord Gi*) and Local Indicators of Spatial Association to identify geographic clustering patterns. We then performed multinomial logistic regression to identify factors associated with policy clusters | ||
| 520 | |a SETTING: Massachusetts | ||
| 520 | |a PARTICIPANTS: Three hundred fifty-one municipalities in Massachusetts | ||
| 520 | |a MAIN OUTCOME MEASURE: Policy clusters-groups of municipalities with similar tobacco control policy behaviors | ||
| 520 | |a RESULTS: Based on the k -means analyses, we identified 3 clusters in Massachusetts municipal tobacco control policy behaviors: 54% (N = 191) of municipalities were "Policy Leaders" with a high adoption rate of the 6 tobacco control policies; 18% (N = 63) were "Peer-Influenced Actors" focused on tobacco purchase restrictions for individuals younger than 21 years; and 28% (N = 97) were "Policy Non-Actors," with no tobacco control policies in place. Policy Leaders were geographically clustered in larger cities and the MetroWest region. Policy Non-Actors were clustered in rural areas of Western and Central Massachusetts. Larger municipal population size, higher municipal tax income, and higher percentages of residents voting Democratic were associated with higher policy adoption activities | ||
| 520 | |a CONCLUSIONS: Local variation in the adoption of tobacco policies may exacerbate inequities in tobacco use and population health. Opportunities remain to implement additional tobacco control regulations at the local level to promote public health | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Kaplan, Anna |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yihua |e verfasserin |4 aut | |
| 700 | 1 | |a Stopka, Thomas J |e verfasserin |4 aut | |
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