Essay
sumurgen
governmentpaper3.pdf
The 2013 US Government Shutdown (#Shutdown) and Health: An Emerging Role for Social Media
In October 2013, multiple
United States (US) federal
health departments and agen-
cies posted on Twitter, “We’re
sorry, but we will not be
tweeting or responding to
@replies during the shut-
down. We’ll be back as soon
as possible!” These “last
tweets” and the millions of
responses they generated re-
vealed social media’s role as
a forum for sharing and dis-
cussing information rapidly.
Social media are now
among the few dominant
communication channels
used today. We used social
media to characterize the
public discourse and senti-
ment about the shutdown.
The 2013 shutdown rep-
resented an opportunity to
explore the role social media
might play in events that could
affect health. (Am J Public
Health. 2014;104:2248–2250.
doi:10.2105/AJPH.2014.302118)
Raina M. Merchant, MD, MSHP, Yoonhee P. Ha, MSc, MPhil, Charlene A. Wong, MD, H. Andrew Schwartz, PhD, Maarten Sap, BS, Lyle H. Ungar, PhD, and David A. Asch, MD, MBA
BEHIND THE SUPER BOWL, THE
government shutdown was the second most talked about topic on Facebook in 2013.1 The October 2013 government shutdown af- fected and furloughed millions of people in the Unites States, was the second longest shutdown since 1980, had a projected $2 billion to $6 billion lost in output, and stopped or reduced services pro- vided by several departments and agencies that protect and promote health.2 Nothing similar was ob- served during the 1995 to 1996 shutdown when the Internet was emerging as a social medium. The Centers for Disease Control and Prevention (CDC) surveillance support for infectious disease out- breaks (e.g., tuberculosis, hepatitis), annual flu monitoring programs, and vaccine campaigns was re- duced.2 Approval of medical products, devices, and drugs by the Food and Drug Administration was delayed.2 The Vaccine Injury Compensation Claims process directed by the Health Resources and Services Administration was disrupted. Hundreds of new patient enrollments for certain National Institutes of Health (NIH)-sponsored clinical trials were slowed, along with support services for planned, new, and existing trials as three quarters of NIH and two thirds of CDC employees were furloughed.2
The Children’s Hospitals Gradu- ate Medical Education Payment Program, which supports the training of future pediatric health care workforce, stopped.2,3 Head Start Program grantees, which
provide services for more than 6300 children, had to close cen- ters for nine days before being reopened with funding from private foundations or states.2
Because of the broad base of health’s social determinants, in- cluding nutrition, education, and employment, the impact of the government shutdown on health was much larger than what would be estimated if only those effects on traditional health care were considered.
SOCIAL MEDIA AND REAL-TIME SURVEILLANCE
In the hours surrounding the start of the government shutdown, numerous articles were written, tweeted, and posted online that attempted to answer the questions “What will be open and closed?” and “How might the shutdown affect health?” A fog of uncertainty surrounded the disruption of in- formation and trust brought on by the partisan conflict in the government. National Public Radio tweeted, “Are you affected by the #shutdown?” CNN Health asked, “Were you or your child supposed to start a clinical trial that may be delayed b/c of the @shutdown? Let us know.” Early news stories also covered the po- tential funding loss for the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), which provides nutritional support to low-income women, infants, and children. In response to the National WIC Association’s
Facebook posts about the shut- down, several individuals sought more information by asking ques- tions such as, “How bad will it affect WIC?” These online posts quickly generated their own at- tention, revealing social media’s reach and ability to clear the fog by providing on-the-ground information.
CHARACTERIZING PUBLIC DISCOURSE AND SENTIMENT
Using an observational study design and data publicly available through the Twitter application programming interface (https:// dev.twitter.com and keyword search), we identified 2 998 285 English language tweets geocoded in the United States (mapped by state, county, or city coordinates), beginning with the first increase in mentions of “shutdown” and “#shutdown” from September 22 to October 6, 2013. Relative to the week before the shutdown, mentions of several health-related terms and agencies (flu, WIC, cancer, NIH, CDC) increased sig- nificantly on or after October 1, 2013 (Figure 1). Other related terms (e.g., US Department of Agriculture, food safety, clinical trials) did not have enough men- tions to conduct a time-series analysis. To characterize the emo- tions associated with the content generated on Twitter, we per- formed sentiment analyses using sentiment lexicons and natural language processing.4 This ap- proach identified the mean positive
COMMENTARIES
2248 | Commentaries | Peer Reviewed | Merchant et al. American Journal of Public Health | December 2014, Vol 104, No. 12
or negative valence of words men- tioned in tweets at the onset of the shutdown. In this timeframe, the sentiment associated with health terms of interest was primarily neg- ative. For example, terms associated with negative valence and WIC included terms such as, “shameful,” “horrible,” “poor,” “abuse,” and “smh” (shaking my head). This suggested Twitter could be used for tracking public discussions in real time and their associated sen- timents around disruptive events that affect health. Further informa- tion about the volume of people engaged in a topic (e.g., number of followers), topic dissemination
(e.g., retweets), and concerns about a topic (e.g., content analysis) could also provide additional information that health care agencies could use to follow public discourse during disruptive events.
RAPIDLY LINKING RESOURCES WITH UNMET NEEDS
In the hours immediately fol- lowing the start of the shutdown, a major unmet need was the pub- lic’s access to detailed information regarding which federal health programs were affected. As up- dates from some federal health
departments and agencies became limited or absent from the social media discussion, other health care organizations stepped up to pro- vide both information and services. HealthMap.org tweeted, “Help us track the #flu while the CDC is temporarily not allowed to do so. Join @FluNearYou here: flunearyou. org #shutdown #alert.” Use of social media by federal health agencies varied; some posted that they would not be updating their accounts, some stopped posting, and some posted throughout the shutdown. The Veterans Health Administration, for example, used its Facebook (> 92 000 likes) and
Twitter (> 40 000 followers) ac- counts to specifically inform pa- tients about their current status, status of previous arrangements, and how to find additional informa- tion via nonsocial media channels.
All VA medical centers and clinics will remain fully opera- tional and will continue to pro- vide health care services to our nation’s Veterans during the government shutdown. Previ- ously scheduled VA health care appointments are not affected.
These messages were accom- panied by telephone numbers and links to Web sites with additional information. The Veterans Health Administration and others’ use of social media for providing timely communication about ser- vices in a disruptive event could serve as a model for the several other health agencies that were not able to use their “last tweets” to provide resources about where the public could turn for answers or assistance if needed. Previous work highlighted best practices and lessons learned for social media communication during dis- ruptive events, and further work is still needed to evaluate optimal messages and message platforms.5
LIMITATIONS OF SOCIAL MEDIA
Although social media can play a unique and important role dur- ing disruptive events that affect the health care infrastructure, social media also present unique challenges.5---7 Determining the best approach to detecting a true signal from the noise can be chal- lenging. Terms used on social media may carry multiple mean- ings, depending on the context in which they are used. In addition, social media users may not repre- sent a national sample of the public, limiting the generalizability of
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3 Note. CDC = Centers for Disease Control and Prevention; NIH = National Institutes of Health; WIC = Women, Infants, and Children. The volume
of select health-related content generated on Twitter around the start of the US government shutdown. The x-axis shows days preceding
and following the start of the shutdown on October 1, 2013. The y-axis represents the standardized relative frequency, computed as the
standardized percentage of tweets for each day that contained a given term across all days in the sample timeframe. The vertical line
represents the start of the shutdown on October 1, 2013.
FIGURE 1—Health-related terms and agencies discussed on Twitter around the time of the US government
shutdown.
COMMENTARIES
December 2014, Vol 104, No. 12 | American Journal of Public Health Merchant et al. | Peer Reviewed | Commentaries | 2249
discussions and sentiments. Al- though the “crowd” often identifies and dispels misinformation and misinterpretation, incorrect infor- mation can spread rapidly, and identifying how authoritative sour- ces can best moderate these online forums is still being determined. Finally, although social media data have been extracted and analyzed to improve public heath emergency response or recovery by govern- ment agencies,5,8 determining how these data-mining approaches can be scalable for a multitude of public health organizations requires fur- ther study. The Department of Health and Human Services and other departments and agencies have launched innovation chal- lenges to partner with public and nongovernment groups to use so- cial media for tasks such as tracking local health trends, improving emergency preparedness of indi- viduals with durable medical equipment, and creating a Face- book app for disaster preparedness and response.9
While the impact of the US Government shutdown of 2013 on actual health outcomes may not become evident for some time, the ability to understand the pub- lic’s perceptions and identify con- cerns at an early stage could be useful for determining how to best address the needs of the public in a temporarily disrupted health care system. Previous reports il- lustrated how social media could be used by health care organiza- tions in public health emergencies
(e.g., pandemics, natural disasters, terrorist attacks) for timely infor- mation exchange and situational awareness.3 Political events across the world (e.g., Egypt, Turkey) illustrated how social media could be a critical communication channel when Internet service is signifi- cantly disrupted across geographic regions.3 The 2013 shutdown rep- resented a different use of social media that occurred when signifi- cant contributors (e.g., government health agencies) were unable to fully participate in the conversation. In this context, social media could allow for identification of potential problems and facilitate mobilization of temporary solutions. Social me- dia generated more than 45 million tweets, posts, images, videos, and other related output during the shutdown, and it could serve as a unique tool for identifying prob- lems through real-time surveillance, rapidly disseminating information, and connecting individuals to re- sources.5,10,11 Lessons learned from the use of social media during the shutdown could be applied to other events in which policymakers must decide how to disburse limited resources. The social media accounts of some US federal health departments and agencies were figuratively “furloughed,” but they could have provided key information to the public before going offline. In addition, social media could be used to guide the discussion around the shutdown by identifying public perceptions and concerns
about important health-related topics. j
About the Authors Raina M. Merchant, Yoonhee P. Ha, and Charlene A. Wong are with the Penn Medicine Social Media and Health Innovation Lab, Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia. H. Andrew Schwartz, Lyle H. Ungar, and Maarten Sap are with the Department of Computer and Information Science, Positive Psychology Center, University of Pennsylvania. David A. Asch is with the Center for Health Equity Research and Promotion, Philadelphia Veteran’s Affairs Medical Center, Philadelphia, PA. Correspondence should be sent toRaina M.
Merchant, MD, MSHP, Penn Medicine Social Mediaand HealthInnovation Lab,University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104 (e-mail: Raina. [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link.
This article was accepted June 4, 2014.
Contributors R. M. Merchant provided overall direction and was the primary author of and researcher for this article. R. M. Merchant, Y. P. Ha, C. A. Wong, and D. A. Asch contributed to the article by idea generation, data collection, data analysis, drafting of the secondary article, and editing of the article. H. A. Schwartz, M. Sap, and L. H. Ungar provided extensive Twitter mining, extraction, and analysis, as well as drafting and editing of the article.
Human Participant Protection Human participant protection was not required because no human participants were involved in this research.
References 1. D’Onofrio R. 2013 year in review. Available at: https://newsroom.fb.com/ news/2013/12/2013-year-in-review. Accessed June 4, 2014.
2. Executive Office of the President of the United States. Impacts and costs of the October 2013 federal
government shutdown. Available at: http://www.whitehouse.gov/sites/ default/files/omb/reports/impacts- and-costs-of-october-2013-federal- government-shutdown-report.pdf. Accessed June 4, 2014.
3. Wong CA, Davis JC, Asch DAA. Political tug-of war and pediatric resi- dency funding. N Engl J Med. 2013; 369(25):2372---2374.
4. Mohammad SM, Kiritchenko S, Zhu X. NRC-Canada: building the state- of-the-art in sentiment analysis of tweets. Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Seventh International Work- shop on Semantic Evaluation (SemEval 2013), Atlanta, GA: Association for Com- puational Linquistics; 2013;321---327.
5. Rubin S, Bouri N, Jolani N, Minton K. The adoption of social media and mobile health technologies for emergency pre- paredness by local health departments: a joint perspective from NACCHO and the UPMC center for health security. J Public Health Manag Pract. 2014;20(2): 259---263.
6. Merchant RM, Elmer S, Lurie N. Integrating social media into emergency- preparedness efforts. N Engl J Med. 2011;365(4):289---291.
7. Coiera E. Social networks, social media, and social diseases. BMJ. 2013;346:f3007.
8. Bennett KJ, Olsen JM, Harris S, Mekaru S, Livinski AA, Brownstein JS. The perfect storm of information: com- bining traditional and non-traditional data sources for public health situational awareness during hurricane response. PLoS Curr. 2013;5.
9. Challenge.gov. A partnership be- tween the public and the government to solve important challenges. Available at: https://challenge.gov. Accessed June 4, 2014.
10. Salathé M, Freifeld CC, Mekaru SR, Tomasuto AF, Brownstein JS. Influenza A (H7N9) and the importance of digital epidemiology. N Engl J Med. 2013; 369(5):401---404.
11. Dugas AF, Jalalpour M, Gel Y, et al. Influenza forecasting with Google Flu Trends. PLoS ONE. 2013;8:e56176.
COMMENTARIES
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Mitigating HIV Health Disparities: The Promise of Mobile Health for a Patient-Initiated Solution
The HIV epidemic is an
ongoing public health prob-
lem fueled, in part, by un-
dertesting for HIV. When
HIV-infected people learn
their status, many of them
decrease risky behaviors and
begin therapy to decrease vi-
ral load, both of which pre-
vent ongoing spread of HIV
in the community.
Some physicians face bar-
riers to testing their patients
for HIV and would rather their
patients ask them for the HIV
test. A campaign prompting
patients to ask their physi-
cians about HIV testing could
increase testing.
A mobile health (mHealth)
campaign would be a low-
cost, accessible solution to
activate patients to take greater
control of their health, espe-
cially populations at risk
for HIV. This campaign could
achieve Healthy People 2020
objectives: improve patient–
physician communication, im-
prove HIV testing, and increase
use of mHealth. (Am J Public
Health. 2014;104:2251–2255.
doi:10.2105/AJPH.2014.302120)
Monisha Arya, MD, MPH, Disha Kumar, Sajani Patel, Richard L. Street Jr, PhD, Thomas Peter Giordano, MD, MPH, and Kasisomayajula Viswanath, PhD
WORLD AIDS DAY EACH DE-
cember reminds us of the ongoing HIV epidemic in the United States and its disproportionate toll on racial and ethnic minority com- munities. HIV testing is an essen- tial strategy to curb the ongoing epidemic. When people infected with HIV learn their status, many of them decrease risky behaviors to prevent spread to others1 and begin antiretroviral therapy to decrease viral load, the main bio- logical predictor of the ongoing spread of HIV in the community.2
Despite national recommenda- tions to make HIV testing routine for all adults,3---6 HIV testing rates— particularly among the racial and ethnic communities hardest hit— remain low.7 Patients want to be tested.8 However, physicians face numerous HIV testing barriers, in- cluding physician discomfort with initiating HIV testing discussions,9
physicians not realizing that pa- tients expect HIV testing to be done,8 time,10,11 and competing clinical priorities.11,12
A pioneering intervention to improve HIV testing in health care settings may be a patient- initiated approach. The push---pull capacity model offers a framework to guide a solution to improve patient-initiated HIV testing.13,14
With a push---pull model, health information can be provided—or pushed—to many patients. This push creates a demand—or pull— for health services that address patient concerns. The ubiquity of cell phones and the pervasive use of text messaging provide an in- novative platform for promoting
an effective HIV testing campaign. Operationalizing the push---pull model through mobile health (mHealth) could be a novel ap- proach to improving HIV testing in health care settings. This ini- tiative would reduce demands on physicians, increase patients’ engagement in their own health, and address a significant ongoing public health problem.15 Goals of Healthy People 2020 include eliminating health disparities and increasing the number of people who have been tested for HIV.16
THE ONGOING US HIV EPIDEMIC
In the United States, more than one million people are infected with HIV,17 and approximately 50 000 new HIV infections occur yearly.18a People in the United States of lower socioeconomic status face a greater burden of HIV.18b Individuals who live below the poverty line are twice as likely to be infected with HIV as those who live above the poverty line.18
HIV also has a disproportional impact on racial and ethnic mi- norities in the United States. De- spite making up only 14% and 16% of the US population, African Americans and Hispanics make up 44% and 19%, respectively, of those living with HIV.18 Despite the high prevalence of HIV in African American and Hispanic communities, African Americans and Hispanics make up a dispro- portionate share of those unaware of their HIV-positive status.7 Al- though nationally nearly 16% are
unaware they are infected with HIV,18 22% of African Americans and18% of Hispanics with HIV do not know they are infected be- cause they have not been tested.7
In a national survey, 26% of Af- rican Americans and 44% of Hispanics reported never hav- ing been tested for HIV.19 When African Americans are finally tested, they are tested late: more than 46% of African Americans and more than 48% of Hispanics receive an AIDS diagnosis within three years of their initial HIV di- agnosis.20 By contrast, 42% of Whites are diagnosed within three years of an HIV diagnosis.20
MISSED OPPORTUNITIES FOR HIV TESTING IN HEALTH CARE SETTINGS
Although the Centers for Dis- ease Control and Prevention (CDC)6 and professional medical societies3---5 have advocated rou- tine HIV testing for all adults in high HIV prevalence health care settings since 2006, HIV testing is not widespread. HIV testing reached a peak of 39.8% of adults in 2009 but dropped to 34.7% in 2012.21 Many opportunities for HIV testing have been missed. In a 2012 national survey, 72% of participants noted that their health care provider had never suggested HIV testing to them.22
Additionally, studies in primary care settings in areas with high HIV prevalence have found that 74% of patients reported that their health care provider had not offered them HIV testing23 and
COMMENTARIES
December 2014, Vol 104, No. 12 | American Journal of Public Health Arya et al. | Peer Reviewed | Commentaries | 2251
89% reported that their health care provider had never recom- mended HIV testing.24 Even when HIV indicators are present, many health care providers are not testing. In one study in which patients presented to care with clinical conditions suggestive of HIV, only 18% received a recommendation for HIV testing.25 A CDC report noted that patients who were di- agnosed late with HIV had a me- dian of four visits to a health care facility before their eventual HIV testing and diagnosis.26 Studies have found that of HIV-positive patients, more than 80% did not receive their diagnostic HIV test during their recent routine medi- cal examination,27 and 71% had one or more health care encoun- ters in the preceding year during which their health care provider did not test them for HIV.28
PATIENTS WANT TO BE TESTED BY THEIR PHYSICIANS
Numerous studies have shown that a health care provider’s rec- ommendation to get tested for HIV can influence a patient’s decision to undergo testing.22,24,29---31 A recent national survey found that for more than one third of those sur- veyed, a health care provider’s recommendation to test for HIV was the reason they underwent HIV testing.22 Conversely, among participants who had never been tested for HIV, one third reported that not receiving a recommenda- tion from a health care provider was the reason they had not been tested.22 Notably, some studies have found that patients in health care settings actually expect HIV testing to be done by their health care provider. In one study done in health care settings, nearly one in four patients expected that their provider would do HIV testing, and
more than 40% of patients wanted their health care provider to do the testing.8 These findings highlight the importance of health care pro- vider HIV testing recommenda- tions for improving HIV testing in health care settings.
PUSH–PULL MODEL FOR IMPROVED HIV TESTING
Because of an ongoing need to improve HIV testing in the United States, in April 2013 the US Pre- ventive Services Task Force rec- ommended routine testing of all adolescents and adults aged 15 to 65 years.5 Given the limited suc- cess of the 2006 CDC HIV testing recommendations in making HIV testing a routine practice, for this 2013 recommendation to be re- alized in health care settings, a new paradigm is needed to engage both physicians and patients.6 One third of participants in a national survey reported wanting strategies to discuss HIV with their health care provider; only 46% had ever had a discussion about HIV with their health care provider.19 Fur- thermore, 42% wanted to know who should get tested for HIV.19
An innovative and successful strategy to improve HIV testing may be to activate the patient to initiate the discussion and request HIV testing. A recent survey found that 70% of physicians wanted their patients to ask them for an HIV test.32 Campaigns are needed that will push patients to pull their physicians to offer HIV testing. The push---pull capacity model is increasingly being recognized as important for adoption of evidence-based practices and im- proving public health outcomes.13,14
Push includes communicating health information to wide popula- tions, and pull refers to creating demand for health services.13 In fact, when consumers are provided
with the right information, they are then able to demand high-quality health care.15 A way to push health information to wide populations would be to use the media. Tradi- tional media options include televi- sion, radio, and print, and new media options include the Internet, mobile phones, and social media.
A health campaign push---pull model using mobile phones— a near-universal media—could be used to create consumer demand for HIV testing in health care settings. In one study, nearly one in three patients felt that text messages about HIV prevention would be helpful.33 Periodic prompts delivered via media have been shown to be effective in reminding and motivating people to change health behaviors.34
Figure 1 illustrates how an mHealth campaign could remind and push the patient to engage and pull the physician into a dis- cussion about HIV testing, thereby leading the physician to order the HIV test. Ideally, this mHealth campaign message using cell phones would be delivered when it is most relevant: just before a patient’s appointment with his or her physician. Notably, the health campaign push---pull model could also involve strategies that push the physician to offer HIV testing.
PROMISE OF MOBILE PHONES FOR AN mHEALTH CAMPAIGN
According to a National Insti- tutes of Health Consensus Group, mHealth is the use of mobile devices to improve health re- search, services, and outcomes.35
Mobile phones are the most widely available and used new media technology for mHealth. Recent surveys have found that 91% of US adults own a mobile phone36 and 61% of mobile
phone owners have smartphones, which have many of the functions of a computer.37 Mobile phone ownership is high even among racial and ethnic minority groups and across all education and in- come levels (Table 1).36 Because only 19% of smartphone users have health-related mobile appli- cations,39 a text message---based mHealth campaign could have greater reach and impact. According to a 2013 national survey, text messaging is the most frequent activity done on a mobile phone, with 81% of US adults using their mobile phones to send and receive text mes- sages.38 Text messaging is ubiqui- tous across race/ethnicity, educa- tion level, and household income (Table 1).38 Text messaging is also more frequently used than other forms of Internet messaging. Only 20% of e-mails are opened and 12% of Facebook feeds are viewed, whereas 99% of text messages are opened.40
Because so many people own mobile phones and use text mes- saging, health campaign messages could be delivered to a large number of people at a time when the message is most relevant, for example near the time of a physi- cian’s appointment. According to a recent survey, every 10 minutes one in five people check their phone and every 30 minutes one in four people check their phones.41Unlike health campaigns using static media such as billboards and television, the portability of mobile phones and asynchronous nature of text messaging facilitate access of messages at a time conve- nient to the message receivers. Health text messages can be per- sonalized or interactive, if desired, and these are salient features for health campaign promotion and effectiveness.42 As summarized by Cole-Lewis and Kershaw,43 text
COMMENTARIES
2252 | Commentaries | Peer Reviewed | Arya et al. American Journal of Public Health | December 2014, Vol 104, No. 12
messaging offers great promise as a tool for improving health because it is commonly available, relatively low cost, in widespread use, and technologically easy to use and can be applicable to many health con- ditions. Studies have found that text message campaigns are successful at encouraging preventive health behaviors.42 In particular, text message interventions have been successful and engaging in promot- ing sexual health44 and HIV pre- vention.45
Although all of these qualities are attributes that make the mo- bile phone a highly effective op- tion for a health campaign, only 9% of mobile phone owners say they receive text message updates or alerts about any health issue.39
Among those that do, African Americans are the most likely to receive health information via text.39 An mHealth solution may be ideal given that in a survey of more than 1000 patients, nearly one in three people felt that
mHealth would offer them greater control of their health.46 Owing to the widespread ownership and use of mobile phones and their porta- bility, people could conveniently and frequently access health text messages. On the basis of con- structs from the health belief model of behavior change, which has been used to guide successful health communications cam- paigns,47 an mHealth campaign could provide self-efficacy and a cue to action for patients to talk to their doctors and be more engaged in their health. Further research is needed to understand what messages will stimulate be- havior change.
IMPLEMENTING AN mHEALTH CAMPAIGN
A successful text message mHealth campaign should be lo- cally driven. Locally driven cam- paigns can address the needs of the target audience and their spe- cific health concerns, be tailored to the information technology (IT)
infrastructure of the local health system, and therefore more di- rectly engage the local community. Such a campaign would be man- aged by the health system’s IT department. This type of cam- paign could be implemented with minimal cost because the IT department could contract with companies such as SMS Market- ing 360,48 Frontline SMS,49 or Magpi,50 which offer free text- messaging platforms. Because the IT department maintains records of patients’ appointments, they could therefore easily send a health text message concomitant with preexisting appointment re- minders. This timing would facili- tate a cue to action for patients to discuss the text message health content with their physician at the time of their appointment.
Additionally, physicians would encourage their own patients to sign up for this local mHealth campaign. Working with the IT department would facilitate per- sonalized messages signed by the patient’s physician. A recent study found that patients would prefer campaigns promoted by their own health care provider.32 When physicians recommend health be- haviors, patients are more likely to adopt these behaviors, thus im- proving their health. Such a cam- paign could be modeled after the Kaiser Permanente Mobile Storm campaign, which has successfully sent appointment reminders, treatment reminders, and notifi- cation of completed lab results.51
Another campaign prototype is the Oregon Reminders mHealth campaign.52 This campaign sends personalized messages regarding HIV and other sexually trans- mitted infection testing, appoint- ment reminders, and prescrip- tion refills.52 Patient engagement campaigns could be further strength- ened by also implementing parallel
a
b
PUSH
mobile phone sends HIV testing reminder to patient
patient requests HIV test
physician orders HIV test
PUSH PULL
PULL
FIGURE 1—Representation of an HIV testing push–pull model between patients and physicians using
mobile health, both (a) graphically and (b) descriptively.
TABLE 1—Mobile Phone Ownership and Text Message Usage by
Sociodemographic Characteristics: United States, 2013
Sociodemographic Characteristic Mobile Phone Ownership, % Text Message Use, %
Race/ethnicity
African American 93 85
Hispanic 88 87
White 90 79
Education
Some college education > 90 > 80
Without a college education > 80 > 70
Income < $30 000 annually 86 78
Source. Rainie 36 and Duggan.
38
COMMENTARIES
December 2014, Vol 104, No. 12 | American Journal of Public Health Arya et al. | Peer Reviewed | Commentaries | 2253
physician-targeted campaigns. These campaigns would remind busy physicians to offer patient- specific preventive health screen- ings recommended by national guidelines. Such physician-targeted campaigns could also be mHealth based or could be done through traditional avenues, such as aca- demic conferences, electronic medical record reminders, or con- tinuing medical education courses.
CONCLUSIONS
More than 30 years into the HIV epidemic, the infection is still spreading because many are un- aware of their positive status as a result of testing rates remaining low. The current HIV epidemic is having a disproportionate impact on African American and Hispanic communities in the United States, in part because people have not been tested for HIV. “Communi- cation inequalities based on edu- cation, income, race, and ethnicity are likely to influence what people know about health,”53(p833) and may be contributing to HIV testing and health disparities.53---55 Because of the widespread ownership and use of mobile phones—particularly among the populations most affected by HIV—mHealth is a promising avenue for reducing these inequalities and breaching the digital divide. Because only 0.3% of health-related mobile ap- plications were dedicated to HIV and sexually transmitted infec- tions in 2012,39,56 a mobile phone text message---based campaign may be an ideal solution to help mitigate the ongoing HIV epi- demic by improving HIV testing. It is possible that an innovative patient-initiated health campaign promoting testing—in conjunction with physicians routinely offering HIV testing—may offer the synergy needed to improve HIV testing
rates in health care settings and decrease the ongoing spread of HIV in the community. This type of intervention could facilitate improved patient---physician communication and increase the use of mHealth, both key objec- tives of the Healthy People 2020 goals.16
Given that many people with HIV remain unaware of their HIV infection and that the US Preven- tive Services Task Force recently recommended routine HIV testing of all people aged 15 to 65 years, novel strategies are needed to encourage more widespread HIV testing in health care settings.5
Cross-disciplinary partnerships between health and technology fields could catalyze innovative strategies. As noted by former US Secretary of Health and Human Services Kathleen Sebelius, mHealth is “the biggest technology breakthrough of our time,” which could empower tra- ditionally hard-to-reach patient populations to take control of their own health.57 This underused technology offers great promise to improve the health of commu- nities facing the greatest disease burdens. j
About the Authors Monisha Arya, Richard L. Street Jr, and Thomas P. Giordano are with the IQuESt, Michael E. Debakey VA Medical Center, Houston, TX, and with the Department of Medicine, Baylor College of Medicine, Houston. Disha Kumar and Sajani Patel are with the Weiss School of Natural Sciences, Rice University, Houston. Kasisomayajula Viswanath is with the Department of Social and Behavioral Science, Harvard School of Public Health, Dana-Farber Cancer Institute, Boston, MA. Correspondence should be sent to Monisha
Arya, MD, MPH, Section of Infectious Diseases and Section of Health Services Research, Department of Medicine, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030 (e-mail: monishaa@ bcm.edu). Reprints can be ordered at http://www.ajph.orgbyclickingthe “Reprints” link.
This article was accepted June 7, 2014.
Contributors M. Arya planned the thematic content, conducted literature reviews, contributed to the writing of all sections and the development of the model, and syn- thesized revision. D. Kumar contributed to literature reviews on HIV testing and mHealth and the writing of the sections about the HIV epidemic and HIV testing disparities. S. Patel contributed to liter- ature reviews on health campaigns and mHealth and the writing of the section on the use of mHealth in a health campaign. D. Kumar and S. Patel con- tributed to the revision of the commen- tary in its entirety. R. L. Street Jr con- tributed to the literature research on mHealth in health communication, the organization of the article, and the in- clusion of theory-based evidence in the push---pull mHealth model. T. P. Giordano contributed to the revision of the HIV epidemic, HIV testing disparities, and push---pull model sections; the organi- zation of the commentary; and the de- sign of the mHealth push---pull figure. K. Viswanath contributed to the revision of the push---pull model section and the implications of mHealth in breaching the digital divide. All authors approved the final version of the article.
Acknowledgments This research was supported by the Na- tional Institute of Mental Health (grant MH094235-01A1; PI: M. A.). This work was also supported in part by the Center for Innovations in Quality, Effectiveness, and Safety (grant CIN 13-413), Michael E. DeBakey VA Medical Medical Center, Houston, TX.
We thank Ashley Phillips for her thoughtful comments on and editorial assistance with the article.
Note. The views expressed in this article are those of the authors and do not necessarily represent the views of the National Institutes of Health or of the US Department of Veterans Affairs.
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30. Stefan MS, Blackwell JM, Crawford KM, et al. Patients’ attitudes toward and factors predictive of HIV testing of aca- demic medical clinics. Am J Med Sci. 2010; 340(4):264---267.
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32. Arya M, Kallen MA, Street RL, Jr., Viswanath K, Giordano TP. African American patients’ preferences for a health center campaign promoting HIV testing: an exploratory study and future directions. J Int Assoc Provid AIDS Care. 2014;Epub ahead of print.
33. Person AK, Blain ML, Jiang H, Rasmussen PW, Stout JE. Text messaging for enhancement of testing and treatment for tuberculosis, human immunodeficiency virus, and syphilis: a survey of attitudes toward cellular phones and healthcare. Telemed J E Health. 2011;17(3):189---195.
34. Fry JP, Neff RA. Periodic prompts and reminders in health promotion and health behavior interventions: systematic review. J Med Internet Res. 2009;11(2):e16.
35. US Department of Health and Human Services. mHealth. 2013. Available at: http://www.webcitation.org/6FRD6pbPV. Accessed September 25, 2014.
36. Rainie L. Cell phone ownership hits 91% of adults. 2013. Available at: http://www.pewresearch.org/fact-tank/ 2013/06/06/cell-phone-ownership-hits- 91-of-adults/. Accessed October 4, 2014.
37. Smith A. Smartphone ownership— 2013 update: Pew Research Center’s Internet & American Life Project. Avail- able at: http://pewinternet.org/Reports/ 2013/Smartphone-Ownership-2013. aspx. Accessed October 4, 2014.
38. Duggan M. Cell phone activities 2013. Available at: http://www.pewinternet.org/ files/old-media//Files/Reports/2013/ PIP_Cell%20Phone%20Activities% 20May%202013.pdf. Accessed October 4, 2014.
39. Fox S, Duggan M. Mobile health 2012. Available at: http://pewinternet. org/Reports/2012/Mobile-Health.aspx. Accessed October 4, 2014.
40. Yovanoff J. Infographic: How to Reach, Educate, and Enroll the Uninsured with Mobile. Mobile Commons; 2014. Available at: https://www.mobilecommons. com/resources/infographic-reach-educate- enroll-uninsured-mobile. Accessed September 16, 2014.
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51. mobileStorm. Kaiser Permanente goes mobile with health care. 2013. Available at: http://www.mobilestorm. com/wp-content/uploads/CASE_ STUDY_Kaiser_Permanente_v1-1.pdf. Accessed April 28, 2014.
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55. Viswanath K, Ackerson LK. Race, ethnicity, language, social class, and health communication inequalities: a nationally-representative cross- sectional study. PLoS ONE. 2011;6(1): e14550.
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57. Sebelius K. mHealth Summit keynote address. Available at: http:// www.cancer.gov/ncicancerbulletin/ 121311/page4. Accessed November 15, 2013.
COMMENTARIES
December 2014, Vol 104, No. 12 | American Journal of Public Health Arya et al. | Peer Reviewed | Commentaries | 2255
Science and Public Health Principles Used to Reduce Road Deaths An editorial in a previous
issue of this journal falsely
claims that the US govern-
ment’seffortstoreduceroad
fatalities are not based on
science. It says that, as a re-
sult, the United States has
fallen behind other countries
in road death prevention.
A large body of research
and evaluation informed
federal and state safety pro-
grams from the outset.
Evans’s comparisons of
death trends among coun-
tries without adjustment
for changes in relevant risk
factors or specification of
the injury reduction policies
among the countries tell us
nothing about the causes of
the declines or the effects of
specific ameliorative efforts.
(Am J Public Health. 2014;
104:2256–2258. doi:10.2105/
AJPH.2014.302352)
Leon S. Robertson, PhD
IN AN ARTICLE1 AND EDITORIAL2
on motor vehicle fatalities, Evans asserts that science was ignored in efforts to reduce motor vehicle injuries in the United States. He writes that difference in reductions in motor vehicle deaths among countries is a result of a differential emphasis on driver behavior and vehicle factors in the causation of road fatalities. In fact, he ignores the sound science that informed motor vehicle safety standards, state law enforcement and behav- ior change programs in counter- measure choice. His data tell us nothing about the factors that in- fluence trends in deaths among different countries.
HISTORY
An extensive review of the sci- entific study of injury risk factors and effectiveness of attempts at prevention was published in a 1964 book.3 The first author, William Haddon Jr, MD, MPH, was a scientist based in the New York State Health Department where he conducted ground breaking research on the role of alcohol in fatal crashes.4 A review of the book in The New York Times came to the attention of Senator Abraham Ribicoff who subse- quently convened hearings on the federal government’s activities to prevent motor vehicle related injuries. The legislative process eventually led to two acts of Congress—one authorizing the regulation of motor vehicle perfor- mance characteristics that could be improved to reduce the incidence and severity of injury and another authorizing aid to the states to enhance law enforcement and
other behavioral change ap- proaches to injury reduction, as well as to improve emergency medical response. Haddon be- came the first administrator of the federal agency created to imple- ment the laws.5
Evans relies on studies of pre- crash fault finding to argue that driver behavior causes most acci- dents. James J. Gibson, a psycholo- gist who authored the 1938 arti- cle cited by Evans, later identified degree of energy exchange with the human anatomy during crashes as the necessary condition for incidence and severity of in- jury and noted that the word “accident” has no meaningful op- erational definition for scientists.6
During Haddon’s tenure at the new agency, he and his deputy Robert Brenner developed a ma- trix of the combination of factors and phases of crashes that could be studied and the results possi- bly applied to reduce injuries. The factors were the behaviors of individuals (car and truck drivers, pedestrians, bicyclists, and motorcyclists), vehicle attri- butes that increase risk of a crash or the severity of injury given a crash, and environment factors that exacerbate incidence and severity of injury or postcrash ameliorative efforts. The phases were before, during, and after the crash.7 For example, road deaths can be reduced by reduction in drunk driving before the crash, modifying vehicles to absorb en- ergy before it injures or kills occupants during the crash, and prompt medical treatment after the crash. This matrix guided the agency’s work and has become a standard reference for injury
epidemiologists and injury pre- vention practitioners (a recent search for Haddon Matrix on Google Scholar [scholar.google. com] produced 17 900 results).
Now known as the National Highway Traffic Safety Adminis- tration (NHTSA), the agency from the outset had scientists on staff who conducted research on the factors and phases of the matrix and contracted with outside re- searchers as well. Several US states also supported research ef- forts that inform their legislators and injury prevention efforts. As in all government, academic, and independent research institutes, some of the research is excellent and some mediocre or inept, but to say that science was ignored is simply wrong. The implementa- tion of vehicle safety regulations and support for state programs has waxed and waned depending on the ideologies of those in political power and budget con- straints that affect such efforts by any government.
For more than 20 years, NHTSA’s work at the federal level has been supplemented by research and promotion of preventive efforts by the National Center for Injury Prevention and Control at the Centers for Disease Control and Prevention.8 The Injury Center supports academic injury preven- tion research centers that have produced numerous studies in in- jury epidemiology and evaluation of injury prevention programs, many directed at road deaths.
SCIENCE
Those who carefully conduct scientific studies of disease and
COMMENTARIES
2256 | Commentaries | Peer Reviewed | Robertson American Journal of Public Health | December 2014, Vol 104, No. 12
injury know that the attribution of causation requires meticulous ac- counting of known risk factors that might impact a conclusion regard- ing the contribution of any one. In his article, Evans displays reduc- tions in motor vehicle fatalities in various countries from their high- est points without correlating them to any risk factors. He makes no adjustment among countries for changes over time in economic conditions, vehicle safety stan- dards, laws and programs to change driver behavior, the mix of vehicles types such as bicycles, motorcycles, trucks and cars, or differences in rollover propensity and other vehicle factors that vary within these vehicle categories.9
For example, motorcycle occupant death rates per vehicle or per mile are far higher than those of other road vehicles and are concen- trated among more recently sold vehicles.10 Motorcycle sales in the United States more than doubled from 1999 to 2006 and motorcy- clist deaths almost doubled from 2483 in 1999 to 4837 in 2006 and they remained above 4500 per year through the last pub- lished data for 2012.11 By 2006, there were more motorcyclist deaths than pedestrian deaths. Any trend analysis, singly or among countries, that doesn’t ac- count for such changes is fatally flawed.
The graphs that Evans displays show acceleration in the decline in deaths in all of the countries beginning in 2007 to 2008 when the world economy went into deep economic recession follow- ing near collapse of banks in many countries and government bailouts to keep them afloat. Ups and downs in road fatality rates are well known to be correlated to economic prosperity and reces- sion respectively.12 I wouldn’t recommend policies that reduce
economic growth to reduce road fatalities because of the myriad other negative consequence of re- cession, including an increase in suicides.13 I would recommend that anyone comparing trends in road deaths among countries ac- count for the effects of differential economic ups and downs among the countries.
Once risk factors that account for substantial variance of inci- dence and severity of a reason- ably homogeneous set of diseases or injuries are identified, choice of interventions should be based on the extent to which risk factors can be changed, the demon- strated effectiveness of attempts to change them and potential barriers to implementation. Evans says that US road deaths would have been reduced on the order of 20 000 more per year if the US reduction from its peak had matched that of the Netherlands. He emphasizes issuance of speeding tickets based on auto- mated photographic equipment and speed measurement as a ma- jor means of accomplishing the change. The Netherlands has 117 430 kilometers of roads compared with 6 341 421 kilo- meters in the United States. The population density in the Nether- lands is 493 per square kilome- ter, more than 14 times that of the United States (34 per km2). It is much easier to enforce speed and other laws where the population is concentrated in a small area such as urban areas of the United States where the population den- sity is more like the Netherlands. In the United States, deaths occur disproportionately on rural roads at night. I leave to the imagination of the reader what would happen to automated ticketing machines on rural US roads where people roam in vehicles with assault weapons in their gun racks.
Some of the decline in deaths in recent decades in all countries is the result of manufacturer adoption of vehicle crashwor- thiness and crash avoidance in their designs, sometimes volun- tarily and sometimes by gov- ernment regulation. The coun- tries in Evan’s article also have consumer protection agencies that issue recalls of defective vehicles and other products.1
Governments have employed voluntary behavior-change pro- grams and laws with some success as well. Seat belt and child re- straint use laws have reduced deaths as has some alcohol control legislation in many countries in- cluding the United States. In the United States, motorcycle helmet use laws and the 55-mile-per-hour speed limit reduced deaths for a time but eventually were aban- doned in most US states because of political pressure on legisla- tures, not for lack of scientific knowledge of their effectiveness. Any valid analysis of differences in fatality rate trends among coun- tries must account for the effects of changes in relevant laws.
In 1980, a US General Ac- counting Office report claimed that the NHTSA administered program of grants to states for highway safety had little effect on fatality risk among the states.14
Since studies of high school driver education found that they in- creased fatalities because they in- creased teenaged driving without reducing the risk per driver,15
a study separated the federal funds that were used by the states to support that program and those supporting motorcyclist education and other countermeasures. Ad- justed for age distribution and unemployment in the 48 contigu- ous states, the fatalities among the states from 1975 to 1980 increased in relation to more
funding of high school driver ed- ucation, were unrelated to motor- cyclist fatalities and were lowered in relation to increases in funds per capita that the states used for other programs.16 NHTSA subse- quently did a controlled experi- ment of high school driver educa- tion and found no effect on subsequent crashes between those who took the course and a control group. It stopped funding the courses in its state grant programs. It responded appropriately to the science.
NHTSA has spent billions of dollars on highway safety grants to the states, $527.9 million in fiscal year 2013.17 In 2013, the agency spent less than $20 million on vehicle defect investigation and recall enforcement, against which Evans rants in his editorial. Of course, that is not the total cost of recalls because the manufacturers, not NHTSA, bear the brunt of the cost of recalls. Evans notes that some recalls are done before there are any associated injuries or deaths reported. Many deaths resulting from vehicle defects are never reported as such. Most crash investigations are done by police who are oriented toward driver violations, not vehicle defects, particularly defects that increase severity of injury given a crash. If a manufacturer discovers a prob- lem, such as a defective fuel line that increases the probably of fire in a crash, it is obligated to recall the vehicles and remedy the problem. Would Evans prefer them to wait until some people are fried to initiate the recall?
Evans says that science shows that “crash test results, new vehicle safety technology, and biome- chanics research . . . have rela- tively little to do with traffic safety.”2(p1349) That is not what the science finds. The original US vehicle safety standards for
COMMENTARIES
December 2014, Vol 104, No. 12 | American Journal of Public Health Robertson | Peer Reviewed | Commentaries | 2257
passenger cars based on extant research and engineering at the time accounted for a 40% reduc- tion in deaths per vehicles in use, controlling for other risk factors.18
Rates in subsequent model years of passenger vehicles also showed declines as crashworthiness im- proved.19 Electronic stability con- trol that corrects for driver errors that lead to skids and somewhat for vehicle stability was found to reduce single vehicle fatal crashes more than 40%.20 An important issue is whether various vehicle attributes prevent the same deaths or have independent effects. A study of1995 to 2005 model cars, vans, pickup trucks and SUVs in the United States found 42% fewer deaths in vehicles that had electronic stability control sys- tems. Improved crashworthiness as measured by offset frontal and side crash tests, if applied to all vehicles, would have produced an additional 28% reduction, and static stability improvement would have reduced the deaths 11%. Because the study controlled for the relative effects of each attri- bute given the presence or ab- sence of the other, the effects are additive. The total is 81% of the deaths. There was no correlation of the presence or absence of behavioral or environmental risk factors among vehicles with and without the modifications that could account for the difference in deaths among vehicles associated with the vehicle modifications.21
Long lists of scientifically stud- ied countermeasures that reduce particular subsets of fatalities are available.22 Efficient deployment requires data on when, where and how severe injuries occur. Envi- ronmental health officers in the US Indian Health Service have dem- onstrated how to identify clusters of subsets of injuries, choose ap- propriate countermeasures and
persuade tribal governments and state officials to implement effec- tive countermeasures.23 Applica- tion of these methods nationwide by public health agencies would be a boon to further reductions in fatal and severe injuries. j
About the Author Leon S. Robertson (retired) was with the Department of Epidemiology and Public Health, Yale University, New Haven, CT. Correspondence should be sent to
Leon S. Robertson, 3551 S Via de la Grulla, Green Valley, AZ 85622 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link.
This article was accepted September 16, 2014.
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COMMENTARIES
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