| Literature DB >> 32551216 |
Rienna Russo1, Yan Li2, Stella Chong1, David Siscovick3, Chau Trinh-Shevrin1, Stella Yi1.
Abstract
Prior reviews describing approach, methodological quality and effectiveness of dietary policies and programs may be limited in use for practitioners seeking to introduce innovative programming, or academic researchers hoping to understand and address gaps in the current literature. This review is novel, assessing the "where, who, and in whom" of dietary policies and programs research in the United States over the past decade - with results intended to serve as a practical guide and foundation for innovation. This study was conducted from October 2018 to March 2019. Papers were selected through a tailored search strategy on PubMed as well as citation searches, to identify grey literature. A total of 489 papers were relevant to our research objective. The largest proportion of papers described school-based strategies (31%) or included economic incentives (19%). In papers that specified demographics, the study populations most often included children, adults and adolescents (54%, 46%, and 42% respectively); and White, Black and Hispanic populations (77%, 76% and 70%, respectively). Results highlight opportunities for future research within workplace and faith-based settings, among racial/ethnic minorities, and older adults.Entities:
Keywords: Cardiovascular disease; Diet behaviors; Health policy; Nutrition
Year: 2020 PMID: 32551216 PMCID: PMC7289763 DOI: 10.1016/j.pmedr.2020.101135
Source DB: PubMed Journal: Prev Med Rep ISSN: 2211-3355
Fig. 1Frequency of dietary programs, policies and interventions strategies represented in the literature. There were 641 interventions represented in 489 articles.
Fig. 2Frequency of implementing agency. Total number of implementing agencies is the denominator. There were 703 implementing agencies mentioned in 489 articles.
Fig. 3Frequency of articles reporting study population characteristics in dietary programs, policies and interventions, among those specifying study population characteristics. a) Age. b) Race/ethnicity.
Fig. 4Counts of studies by strategy published over time. * Search conducted in October 2018 – number of studies in 2018 were underestimated.
Ages represented in papers by policy, program or intervention strategy.
| School-based | Economic Incentives | Environmental changes | Media and Education | Food Labeling | Restrictions and mandates | Faith-based | Workplace-based | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | n | % | n | % | n | % | n | % | |
| Children | 153 | 78% | 18 | 15% | 19 | 18% | 12 | 15% | 19 | 18% | 15 | 46% | 1 | 4% | 0 | 0% |
| Adolescents | 111 | 56% | 18 | 15% | 15 | 14% | 11 | 13% | 15 | 14% | 14 | 44% | 1 | 4% | 0 | 0% |
| Adults | 13 | 7% | 66 | 55% | 50 | 46% | 55 | 67% | 50 | 46% | 1 | 3% | 21 | 84% | 8 | 53% |
| Older adults | 7 | 4% | 14 | 12% | 9 | 8% | 6 | 7% | 9 | 8% | 0 | 0 | 2 | 8% | 1 | 6% |
| Not specified | 17 | 8% | 44 | 36% | 44 | 41% | 20 | 24% | 44 | 41% | 16 | 50% | 4 | 16% | 8 | 47% |
| Total | 197 | 121 | 108 | 82 | 59 | 32 | 25 | 17 | ||||||||
*Percentages do not add up to 100% as categories are not mutually exclusive. Denominators are the total number of studies within each category.
% represents percentage of total studies, including those that specified demographics and those that did not.
Races represented in papers by policy, program or intervention strategy.
| School-based | Economic Incentives | Environmental changes | Media and Education | Food Labeling | Restrictions and mandates | Faith-based | Workplace-based | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | n | % | n | % | n | % | n | % | |
| White | 106 | 54% | 53 | 44% | 33 | 31% | 32 | 39% | 9 | 15% | 6 | 19% | 8 | 32% | 6 | 24% |
| Black | 90 | 46% | 56 | 46% | 45 | 42% | 33 | 40% | 8 | 14% | 6 | 19% | 18 | 72% | 4 | 16% |
| Hispanic | 99 | 50% | 47 | 39% | 39 | 36% | 32 | 39% | 8 | 14% | 6 | 19% | 3 | 12% | 3 | 12% |
| Asian | 49 | 25% | 14 | 12% | 12 | 11% | 12 | 15% | 5 | 8% | 1 | 3% | 1 | 4% | 1 | 4% |
| NH/PI | 15 | 8% | 4 | 3% | 2 | 2% | 4 | 5% | 0 | 0% | 0 | 0% | 0 | 0% | 0 | 0% |
| AI/AN | 22 | 11% | 5 | 4% | 8 | 7% | 7 | 9% | 0 | 0% | 0 | 0% | 0 | 0% | 0 | 0% |
| Other | 79 | 40% | 52 | 43% | 36 | 33% | 25 | 30% | 7 | 12% | 4 | 13% | 4 | 16% | 5 | 20% |
| Not specified | 79 | 40% | 53 | 44% | 48 | 44% | 30 | 37% | 46 | 78% | 24 | 75% | 3 | 12% | 10 | 40% |
| Total | 197 | 121 | 108 | 82 | 59 | 32 | 25 | 17 | ||||||||
*Percentages do not add up to 100% as categories are not mutually exclusive. Denominators are the total number of studies within each category.
% represents percentage of total studies, including those that specified demographics and those that did not.