| Literature DB >> 23639765 |
Jenine K Harris1, Nancy L Mueller, Doneisha Snider, Debra Haire-Joshu.
Abstract
INTRODUCTION: Diabetes may affect one-third of US adults by 2050. Adopting a healthful diet and increasing physical activity are effective in preventing type 2 diabetes and decreasing the severity of diabetes-related complications. Educating and informing the public about health problems is a service provided by local health departments (LHDs). The objective of this study was to examine how LHDs are using social media to educate and inform the public about diabetes.Entities:
Mesh:
Year: 2013 PMID: 23639765 PMCID: PMC3652718 DOI: 10.5888/pcd10.120215
Source DB: PubMed Journal: Prev Chronic Dis ISSN: 1545-1151 Impact factor: 2.830
Figure 1Geographic boundaries of local health departments with Twitter accounts that did and did not tweet about diabetes as of June 2012.
Characteristics of Jurisdictions Served by Local Health Departments (LHDs) With Twitter Accounts, by Those Tweeting and Not Tweeting About Diabetes, United Statesa
| Jurisdiction Characteristics | Tweeting About Diabetes (n = 126) | Not Tweeting About Diabetes (n = 91) |
|
| ||||
|---|---|---|---|---|---|---|---|---|
| No. | Median | IQR | No. | Median | IQR | |||
|
| 117 | 230.2 | 462.0 | 80 | 101.5 | 242.0 | 3.5 | <.001 |
|
| 92 | 18.9 | 32.2 | 57 | 7.3 | 11.9 | 3.7 | <.001 |
|
| 92 | 9.0 | 2.9 | 57 | 9.1 | 2.2 | 0.6 | .54 |
|
| ||||||||
| Total no. of FTE | 112 | 115.0 | 303.4 | 77 | 64.8 | 158.2 | 3.2 | .001 |
| Total spending, $ (in millions) | 107 | 9.9 | 30.0 | 72 | 5.1 | 17.8 | 3.2 | .002 |
| FTE per 1,000 constituents | 112 | 0.5 | 0.5 | 77 | 0.5 | 0.4 | 0.03 | .97 |
| Spending per capita, $ | 107 | 42.8 | 47.9 | 72 | 42.8 | 39.6 | 0.5 | .60 |
Abbreviation: IQR, interquartile range; FTE, full-time equivalent staff.
LHD characteristics adopted from the 2010 National Association of County and City Health Officials Profile study (23). County-level diabetes rates obtained from the Centers for Disease Control and Prevention (27). Data are presented in numbers unless otherwise indicated.
Mann-Whitney U test for independent samples.
χ2 test.
Characteristics of Local Health Departments (LHDs) With Twitter Accounts, by Those Tweeting and Not Tweeting About Diabetes, United Statesa
| Health Department Characteristic | Tweeting About Diabetes (n = 126), No. (%) | Not Tweeting About Diabetes (n = 91), No. (%) |
|
|
|---|---|---|---|---|
|
| 7.2 | .07 | ||
| Associates | 2 (1.7) | 0 | ||
| Bachelors | 15 (12.8) | 21 (26.6) | ||
| Masters | 58 (49.6) | 32 (40.5) | ||
| Doctorate | 42 (35.9) | 26 (32.9) | ||
|
| 69 (63.9) | 38 (48.7) | 4.2 | .04 |
|
| ||||
| Performed by LHD directly | 51 (44.3) | 20 (25.6) | 7.0 | .01 |
| Contracted out by LHD | 12 (10.4) | 2 (2.6) | 4.3 | .04 |
| Not performed by LHD or contracted out | 55 (47.8) | 57 (73.1) | 12.2 | <.001 |
|
| ||||
|
| ||||
| Performed by LHD directly | 85 (74.6) | 49 (62.0) | 3.5 | .06 |
| Contracted out by LHD | 13 (11.4) | 3 (3.8) | 3.6 | .06 |
| Not performed by LHD or contracted out | 25 (21.9) | 28 (35.4) | 4.3 | .04 |
|
| ||||
| Performed by LHD directly | 101 (87.1) | 63 (79.7) | 1.9 | .17 |
| Contracted out by LHD | 16 (13.8) | 4 (3.8) | 5.3 | .02 |
| Not performed by LHD or contracted out | 14 (12.1) | 14 (17.7) | 1.2 | .27 |
|
| ||||
| Performed by LHD directly | 85 (75.9) | 50 (64.1) | 3.1 | .08 |
| Contracted out by LHD | 14 (12.5) | 6 (7.7) | 1.1 | .29 |
| Not performed by LHD or contracted out | 22 (19.6) | 24 (30.8) | 3.1 | .08 |
LHD characteristics adopted from the 2010 National Association of County and City Health Officials Profile study (23). County-level diabetes rates obtained from the Centers for Disease Control and Prevention (27). Numbers may not add to totals because of missing data.
Figure 2Geographic distribution of diabetes rates in 2009 (27) and the number of tweets about diabetes tweeted as of June 2012, from local health departments using Twitter.
Examples of Local Health Department Tweets About Diabetes Risks, Benefits, and Cues to Action, United States
| Type of Tweet | Type of Information/Example Tweet |
|---|---|
| Risk tweets (n = 358) | Information about the population at risk or risk levels: |
| Risk based on individual characteristics or behaviors: | |
| Specific consequences or risks and conditions associated with the disease: | |
|
| |
| Benefit tweets (n = 82) | The benefits of healthy preventive behaviors: |
| The benefits of screening: | |
| The benefits of maintenance: | |
|
| |
| Cues to action tweets (n = 699) | Cues to act preventively: |
| Cues to specific self-care and management actions: | |
| Cues to disease awareness activities: | |