Author: Guest Blogger

Literacy and e-Health

By Rachel Kurtzman

Your phone buzzes and you look down- it’s a text that your prescription is ready for pickup, a medication reminder, or a communication from your doctor. For most of us reading health messages like this isn’t an issue, but it is for millions of adults in the U.S. who have low literacy skills. Nearly 14% of U.S. adults have below basic literacy skills and 29% have basic literacy skills- about 92 million people.1 Low literacy is linked to many adverse health outcomes, including increased mortality and poor control of chronic health problems.2

Many mobile health applications rely on individuals being able to read and interpret health messages, and fail to consider those who cannot. Studies have found that most apps available through the app store rely on high literacy and numeracy skills.3 There are very few guidelines on how to develop apps that are more accessible for this population, but some recommendations include using larger font, more images, larger graphics, lowering the reading level, and having the option of text to voice responses.3

Our health care system is evolving and e-health has the potential to help more people engage with the tools necessary to manage their health, but not creating products that are accessible for individuals with a broad range of literacy levels risks widen disparities and leaving behind a vulnerable segment of our population.

References:

  1. Eichner J, Dullabh P. Accessible Health Information Technology (Health IT) for Populations With Limited Literacy: A Guide for Developers and Purchasers of Health IT. (Prepared by the National Opinion Research Center for the National Resource Center for Health IT). AHRQ Publication No. 08-0010-EF. Rockville, MD: Agency for Healthcare Research and Quality. October 2007.
  2. Pignone, M.P., DeWalt, D.A. Literacy and Health Outcomes: Is Adherance the Missing Link? J Gen Intern Med. 2006 Aug; 21(8): 896-7.
  3. Chaudry, B.M., Connelly, K.H., Siek, K.A., Welch, J.L. Mobile interface design for low-literacy populations. IHI’12. January 28-30th, 2012. Miami Florida, USA. DOI: 1145/2110363.2110377

 

Stethoscopes and Smartphones? How Doctors are Using mHealth Apps for Patient Care

By Elizabeth Adams, MA

There was a time when doctors circulated the hallways of hospitals with nothing but a beeper pinned to the waistline of their scrubs.

But today, you might notice your doctor enter the exam room clutching a more advanced communication device – a Smartphone or tablet. A 2014 survey reported that 85% of medical faculty, 90% of medical residents, and 85% of medical students used a Smartphone in a clinical setting1. Modern doctors are increasingly replacing laptops or desktops with Smartphones and tablets2.

Doctors are constantly on their feet, moving throughout hospitals, emergency rooms, or clinics.  They use these devices for variety of job-related tasks, including remote patient monitoring, electronic health record access, e-prescribing, drug reference calculations, reading medical news, and decision-making support3. Now there is a marketplace for health professionals to locate apps designed specifically for clinical practice. In 2011, the iPhone App Store introduced the “Apps for Health Care Professionals” section, which has expanded to include more than 80 app options4.

Here are a few ways doctors are using apps to improve patient care:

 Retrieving Information. Doctors increasingly rely on mhealth to inform complex clinical assessments and decisions. One survey indicated that two-thirds of doctors use medication-interaction assistance apps to aid in the prescription decision-making process5. In addition, medical residents rely on mobile phones in clinical consultation to look up drug information, perform clinical calculations, take notes, or look up clinical guidelines4. Instantaneous access to information can help doctors and trainees make more accurate decisions regarding treatment.

 Communicating with Patients. Electronic health record software, such as Epic (link to: https://www.inova.org/for-physicians/epiccare-apps) – the program used by UNC HealthCare – incorporate apps Haiku and Canto, which facilitate direct correspondence between patients and health care teams. Other third-party apps, such as OhMD (link to: https://www.ohmd.com), TigerText (link to: https://www.tigertext.com/), and Hale (link to: http://hale.co/), are compatible with electronic health record programs and connect patients to doctors through text messaging platforms.

Continuing Education. Mobile continuing education curricula promises to supply doctors and trainees with current medical information and impart recent standards of practice without the time-consuming requirement of sitting at a desktop or in a classroom. In addition, top-tier medical journals, including the New England Journal of Medicine’s This Week app (link to: http://www.nejm.org/doi/full/10.1056/NEJMe1201837) and the American Medical Association’s CPT QuickRef app (link to: https://www.ama-assn.org/practice-management/applying-cpt-codes), deliver scientific articles and guidelines.

 More research is necessary to understand the relationship between mhealth app adoption and improved clinical care outcomes. Smartphones could be considered impediments to patient care, so they must be used with some discretion. But next time your doctor walks in with a tablet or glances at a Smartphone, remember that he or she might be using an app to make better decisions for your health.

References

  1. Ventola, C. Lee. “Mobile Devices and Apps for Health Care Professionals: Uses and Benefits.” Pharmacy and Therapeutics5 (2014): 356–364.
  2. Murfin, M. Know your apps: an evidence-based approach to evaluation of mobile clinical applications. Journal of Physician Assist Education. 2013; 24(3):38-40.
  3. Kaufman, Michele B,PharmD., R.Ph. “Mobile Health Increases as Physicians Seek New Ways to Manage Patients.”Formulary, vol. 47, no. 4, 2012, pp. 161-162, ProQuest, http://libproxy.lib.unc.edu/login?url=https://search-proquest-com.libproxy.lib.unc.edu/docview/1145903653?accountid=14244.
  4. Dolan, B. Apple’s Top 80 Apps for Doctors, Nurses, and Patients. [Online] November 27, 2012. http://www.mobihealthnews.com/19206/apples-top-80-apps-for-doctors-nurses-patients/
  5. Boruff, J. T. M., & Storie, D. M. M. A. Mobile devices in medicine: a survey of how medical students, residents, and faculty use smartphones and other mobile devices to find information. Journal of the Medical Library Association, (2014): 102(1), 22-30.

What Do You Meme? How Memes Can Be Used to Affect Health Behavior Change

By Trevor Bell

In today’s world, memes rule supreme, particularly for teens and young adults. Whether it’s Barack Obama’s portrait, crying Michael Jordan, or dogs, memes have the ability to make light of almost any situation. But, what if memes can help those affected by illnesses? In a qualitative study looking at how Instagram could be used to portray type 1 diabetes (T1D)1, humor – in particular the use of memes – was shown to be a simple way in which teens with T1D were able to both have more positive attitudes and inform the general public about T1D.

In the study, participants used pre-existing T1D memes or created memes themselves and shared these on their personal pages. The authors suggested that humor could be an important point of potential interventions for health behavior changes, and that, “Humor seemed to be a prominent coping strategy and could be used to address the negative feelings that often emerge in adolescents with T1D” (p. 1380). In the context of eHealth, perhaps healthcare providers should take notice of how society deals with issues. For example, eHealth interventions could encourage patients to post memes on their own social media accounts or to a specified website/forum. This is obviously no substitute for clinical care, but it could be a unique way in which we improve the outlook of those affected by chronic illness while also spreading information to the general public. The possibilities are endless, if you know what I meme.

Reference:

1Yi-Frazier, J. P., Cochrane, K., Mitrovich, C., Pascual, M., Buscaino, E., Eaton, L., … Malik, F. (2015). Using Instagram as a Modified Application of Photovoice for Storytelling and Sharing in Adolescents With Type 1 Diabetes. Qualitative Health Research25(10), 1372–1382. http://doi.org/10.1177/1049732315583282

Celebrities, Social Media, and Mental Illness

By Jacob Rohde

Earlier this month, Selena Gomez opened up to Harper’s Bazaar magazine about her struggles with mental illness [1]. When asked about her upcoming plans for the new year, Gomez responded:

“I will always start with my health and my wellbeing. I’ve had a lot of issues with depression and anxiety, and I’ve been very vocal about it, but it’s not something I feel I’ll ever overcome… I think it’s a battle I’m gonna have to face for the rest of my life…”

Gomez is joined by several other celebrities, from Gina Rodriguez to Kid Cudi, who have spoken out about the realities of their mental illnesses and have used social media to publicly vocalize their related experiences [2]. For example, Gomez recently used Instagram to talk about her lupus diagnosis, which she has linked to her depression and anxiety [3]. All too often, celebrities are viewed as immune to such circumstances when, in reality, they share many of our own battles with mental illness. Social media allows celebrities, like Gomez, to connect with their audiences who may also struggle from mental illness, or to those who do not fully understand the complexity of mental illness symptoms.

Fifty percent of Americans will experience some form of mental illness in their lifetime [4], yet public perceptions about mental illness remain highly stigmatized, especially among young adults and college students [5]. In my own experiences, I have witnessed several students express their reluctance to seek mental health services as to avoid being “outed” by peers and stereotyped.

Efforts to reduce mental illness stigma can benefit from the stories and experiences shared by celebrities through their social media accounts. Indeed, a recent study found that college students exposed to celebrity narratives about mental disorders were far less likely to stigmatize mental illness overall and had fewer negative perceptions about those who seek help for mental illness than students in control conditions [6]. Given this, celebrity use of social media as a platform to talk about mental illness may have a positive effect on how the public perceives mental illness.

Of course, I am not advocating for celebrities to share deeply personal experiences. However, if they choose to address certain issues pertaining to their mental health, it may serve to reduce the taboo culture currently surrounding depression, anxiety, and other mental illnesses. At minimum, doing so shows that celebrities, like Gomez, are not so different than ourselves.


Mental illness is a serious concern. If you are struggling, please seek professional help or reach out to the 24/7 suicide prevention hotline: 1-800-273-8255

If you are a UNC student, free support is available through the Counseling and Psychological Services program (CAPS). Information available here: https://caps.unc.edu/


References:

  1. Langford, K. (2018). Selena Gomez’s Wild Ride. Harper’s Bazaar. Retrieved from http://www.harpersbazaar.com/culture/features/a15895669/selena-gomez-intervi ew/
  2. Yang, L. (2017). 23 celebrities who have opened up about their struggles with mental illness. Retrieved from http://www.thisisinsider.com/celebrities-depression-anxiety-mental-health-awaren ess-2017-11#cara-delevingne-struggled-with-depression-as-a-teenager-8
  3. Chiu, M. (2016). Selena gomez taking time off after dealing with ‘anxiety, panic attacks and depression’ due to her lupus diagnosis. People Magazine. Retrieved from http://people.com/celebrity/selena-gomez-taking-a-break-after-lupus-complication s/
  4. Kessler, R. C., Angermeyer, M., Anthony, J. C., De Graaf, R. O. N., Demyttenaere, K., Gasquet, I., … & Kawakami, N. (2007). Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization’s World Mental Health Survey Initiative. World Psychiatry, 6(3), 168.
  5. Eisenberg, D., Downs, M. F., Golberstein, E., & Zivin, K. (2009). Stigma and help seeking for mental health among college students. Medical Care Research and Review, 66(5), 522-541.
  6. Ferrari, A. (2016). Using celebrities in abnormal psychology as teaching tools to decrease stigma and increase help seeking. Teaching of Psychology, 43(4), 329-333.

HIV Medication Adherence Apps: Challenges Faced

By Chunyan Li

The success of HIV medications has changed HIV from a fatal disease to a chronic illness. However, like other chronic diseases that require lifetime medication (at least for now), maintaining good adherence to antiretroviral therapy is not easy for HIV-positive people for reasons such as the complex drug regimens, strict requirements on the time of daily medication, and sometimes intolerable side effects. Having a mobile phone-based application to remind patients of daily medication is a good way out, but the effectiveness of such medication adherence apps remains less studied.

One significant challenge that such apps often face is a lack of behavioral science in design. Some experts described the development of many healthcare apps as a “black box”[1], blaming that app developers often focus too much on technology while neglecting behavior change theories or research evidence. One 2016 research study [2] reviewed all health apps on Google Play, Apple App Store and Windows Phone Store, and found that the reviewed 28 eligible health apps only used 5.6 out of the total 37 behavioral change principles on average. Among the four categories of behavior change principles proposed by the researchers (task support, dialogue support, system credibility and social support), the most used principles were about “system credibility” and “task support”, including features like surface credibility, expertise, authority, and providing general information and function of self-monitoring.  The two categories “dialogue support” and “social support”, which require higher user-provider interactivity and more constructive design based on behavioral science, are somehow neglected.

In another systematic review [3] that reviewed all eHealth-based HIV intervention studies (including smartphone-, Web- and general Internet-based interventions), 10 out of the 14 studies that had a component of adherence improvement were smartphone-based. As HIV patients are usually required to take medicines on quite a strict daily schedule, and sometimes even to be in private if HIV/AIDS is heavily stigmatized, smartphone-based apps are better for portability and privacy protection. However, it could also be challenged when people feel unsafe to disclose HIV status or worry about leaving digital footprints on such apps. In lower-income settings where cell phones are shared with family members, using apps to keep track of medication adherence might not be an ideal option for HIV-positive people.

In a qualitative research study about the HIV treatment continuum that I’m recently working on, a frequently-mentioned desired feature of app-based interventions by HIV-positive people is having communication with human counselors. Many adherence apps may have functions of knowledge education, tracking medications and pushing reminders, but lack an emotional support. Living with HIV is a chronic and multidimensional (physical, psychological and cultural) stress, and a successful coping with such a stress requires consistent support from families, friends and health professionals. Though the advantages of health apps include its mass-reach to users and increasing access to care in limited-resource settings, we should never ignore the needs for human caring and support. How to incorporate human support into HIV medication adherence apps could be one of the future research directions.

 

[1] Tomlinson, M., Rotheram-Borus, M. J., Swartz, L., & Tsai, A. C. (2013). Scaling Up mHealth: Where Is the Evidence? PLoS Medicine, 10(2). https://doi.org/10.1371/journal.pmed.1001382

[2] Geuens, J., Swinnen, T. W., Westhovens, R., de Vlam, K., Geurts, L., & Vanden Abeele, V. (2016). A Review of Persuasive Principles in Mobile Apps for Chronic Arthritis Patients: Opportunities for Improvement. JMIR mHealth and uHealth, 4(4), e118. https://doi.org/10.2196/mhealth.6286

[3] Muessig, K. E., Nekkanti, M., Bauermeister, J., Bull, S., & Hightow-Weidman, L. B. (2015). A Systematic Review of Recent Smartphone, Internet and Web 2.0 Interventions to Address the HIV Continuum of Care. Current HIV/AIDS Reports. https://doi.org/10.1007/s11904-014-0239-3

 

Wearable Health: Who Benefits and Who is Left Out?

By Shazia Manji

There’s no denying the ubiquity of wearable health technology. The global wearables market is expected to grow by more than 15% this year alone, with projected sale of 310.4 million devices worldwide and $30.5 billion generated in revenue. These technologies generate real-time personalized data with the promise to improve individual health by helping to track, manage, incentivize, and improve healthy behaviors and decision making. As wearable tech finds success in the market, it’s important to consider where they can be most effective and where do they face barriers in impact. For example, a device such as a FitBit may be helpful in motivating an individual to make small changes to their diet when they have the necessary resources to make that happen. But what happens if you can’t afford a gym membership and you don’t feel safe running around your neighborhood at night? How well will these devices work for people who live in food swamps, neighborhoods or areas with many fast food and liquor stores but few places to buy healthy foods such as fresh fruits and vegetables?

The overall efficacy and effectiveness of wearable tech is still being determined. A 2015 study published in the Journal of the American Medical Association noted that while these kinds of tracking devices were increasing in popularity, there has been little evidence to show that they are successful in actually changing behavior. Still another suggested that wearables are more likely to be purchased by those who already live a relatively healthy lifestyle, and are less in use by those who might most benefit from a shift in physical activity, or by those with an existing and related health condition. Few studies or initiatives have looked at connecting these mobile health technologies with lower-income individuals in the US or at increasing their prevalence across socioeconomic status. This is largely in part because cost can be prohibitive for those at the lower end of the spectrum. Low-income populations are most at risk for diabetic complications, and may be less likely to have easy access to a physician, but the tools to help improve compliance and self-care have not been made with them in mind. The digital divide in healthcare technology is yet another example of how opportunities and resources for health are inequitably distributed. If we truly want to increase the effectiveness and relevance of wearable health tech, there needs to be a shift in their development and distribution.

A great first step to reducing the cost barrier would be working to get more health tech to be covered by insurers – and not just more robust private or employer-provided insurance plans, but by the insurance plans used by targeted populations, including Medicare and Medicaid. Tech companies could forge partnerships with community-based initiatives working to understand and shift the more structural barriers to health in low-income neighborhoods as part of potential multi-level interventions that go beyond individual behavior change. Wearable health tech used in research studies could combine the tracking technology with forms of interviewing or survey collection aimed at better understanding the barriers to behavior change in the most vulnerable populations, to help collect participant data that can in turn inform chronic disease prevention efforts. At the very least, developers could recognize that tech developed and marketed towards more affluent populations will differ from tech tailored for the most vulnerable.

Perhaps most importantly, I think it’s important to approach investment in and development of wearable health technologies with caution. Investment in digital health technologies is rising tremendously – but it’s crucial to understand who benefits from these technologies and who is left out, and then work proactively toward decreasing the digital divide. Investment in new tech should not trump investment in people and investment in improving the places and conditions in which people live, the conditions which shape and constrain quality of life and health behaviors.

Image: Koolme, Andri. “Fitbit Blaze activity tracker / wristwatch / smartband / smartwatch / smartphone.” 16 July 2016. Licensed under Creative Commons Attribution 2.0 Generic (CC BY 2.0). Accessed 31 Jan 2018.

Are Mobile Mental Health Apps User-Friendly?

by Kat Caskey

Currently, only about half of those affected by mental illness in the United States will receive any kind of treatment[1]. In the past few years, however, experts have begun to look to remote healthcare options that could improve access to mental health treatment. Perhaps most promising is the growing consensus that mental health apps, or mHealth apps, “have unprecedented potential for improving quality of life and public health outcomes” for the tens of millions of people affected by mental health conditions in the U.S. each year.[2]

Mobile apps have the unique potential to reduce many of the traditional barriers to mental health treatment. For example, mHealth apps can be significantly less expensive than traditional treatment and may be accessed anytime, including during times of crisis, without an appointment. In addition, apps can reduce cultural barriers to care as they provide a “discrete mobile environment” free from social stigma.[3]

Evidence-based mHealth apps have been proven effective at treating a variety of mental health conditions, including posttraumatic stress disorder[4], anxiety[5], depression[6], obsessive compulsive disorder[7], bipolar disorder, borderline personality disorder, and substance abuse[8]. Unfortunately, however, although patients frequently download any of the myriad of mental health apps available in the App store, many are deleted after only a few uses, and a staggering 26% are used only once. One study that surveyed mental health app users found that among the most common reasons for deleting mental health apps included “not engaging” and “not user friendly,” with “ease of navigation” being the top feature that makes eHealth apps for mental health favorable.[9]

What good are evidence-based mental health apps if people won’t use them? These results indicate trouble in the realm of user experience, which considers “user emotions, affects, motivations, and values” as well as “ease of use, ease of learning and basic subjective satisfaction.”[10] Understanding user experience has been identified as “a key step in realizing the role of mental health apps”[11] and reminds us that it is not enough to understand the clinical basis of new health technologies; equally significant is consideration of the best ways to design and implement apps for people with mental health conditions. Ideally, user experience and usability testing evaluations should involve all relevant stakeholders, including patients and providers.[12]

Especially considering the wide reach of mHealth apps, “even minor efforts to further refine the usability and utility of the app” have the potential to decrease app attrition rates and increase user exposure to evidence-based treatment recommendations.[13] As apps designed to improve mental health continue to proliferate, app designers and researchers should continue to investigate how an emphasis on user experience can improve mHealth tools for mental health.

[1] National Institute of Mental Health. https://www.nimh.nih.gov/health/statistics/index.shtml.  Accessed February 4, 2018.

[2] Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: using novel data to examine the reach, use, and impact of PTSD coach. JMIR mental health2(1).

[3] Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: using novel data to examine the reach, use, and impact of PTSD coach. JMIR mental health2(1).

[4]Rodriguez-Paras, C., Tippey, K., Brown, E., Sasangohar, F., Creech, S., Kum, H. C., … & Benzer, J. K. (2017). Posttraumatic Stress Disorder and Mobile Health: App Investigation and Scoping Literature Review. JMIR mHealth and uHealth5(10).;
Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: using novel data to examine the reach, use, and impact of PTSD coach. JMIR mental health2(1).

[5] Sucala, M., Cuijpers, P., Muench, F., Cardoș, R., Soflau, R., Dobrean, A., … & David, D. (2017). Anxiety: There is an app for that. A systematic review of anxiety apps. Depression and anxiety.

[6] Lattie, E. G., Schueller, S. M., Sargent, E., Stiles-Shields, C., Tomasino, K. N., Corden, M. E., … & Mohr, D. C. (2016). Uptake and usage of IntelliCare: a publicly available suite of mental health and well-being apps. Internet interventions4, 152-158.

[7] Ameringen, M., Turna, J., Khalesi, Z., Pullia, K., & Patterson, B. (2017). There is an app for that! The current state of mobile applications (apps) for DSM‐5 obsessive‐compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders. Depression and anxiety.

[8] Rizvi, S. L., Dimeff, L. A., Skutch, J., Carroll, D., & Linehan, M. M. (2011). A pilot study of the DBT coach: an interactive mobile phone application for individuals with borderline personality disorder and substance use disorder. Behavior therapy42(4), 589-600.

[9] Smith, D. Motivating Patients to use Smartphone Health Apps. Consumer Health Information Corporation. http://www.consumer-health.com/motivating-patients-to-use-smartphone-health-apps/. Published 2014. Accessed February 4, 2018.

[10] Abrahão, S., Bordeleau, F., Cheng, B., Kokaly, S., Paige, R. F., Störrle, H., & Whittle, J. (2017, September). User Experience for Model-Driven Engineering: Challenges and Future Directions. In 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS) (pp. 229-236). IEEE.

[11] Lemon, Christopher. “The User Experience: A Key Step in Realizing the Role of Mental Health Apps.” Psychiatric Times, 7 Feb. 2018, www.psychiatrictimes.com/telepsychiatry/user-experience-key-step-realizing-role-mental-health-apps.

[12] Price, M., Yuen, E. K., Goetter, E. M., Herbert, J. D., Forman, E. M., Acierno, R., & Ruggiero, K. J. (2014). mHealth: a mechanism to deliver more accessible, more effective mental health care. Clinical psychology & psychotherapy21(5), 427-436.

[13] Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: using novel data to examine the reach, use, and impact of PTSD coach. JMIR mental health2(1).

Funding AMR Research Straight from the Source: Agriculture

By Raj Topiwala

Alexander Fleming’s discovery of Penicillin in 1928 is undoubtedly one of history’s crowning achievements in medicine. In the 89 years that have since followed, antibiotics have saved countless lives and reduced once fatal maladies to easily treatable diseases. However, with the list of antimicrobial resistant pathogens growing at an alarming rate, we risk soon encountering diseases that are resistant to every available method of treatment, regressing us back into the pre-antibiotic age (and its diminished life outcomes).

With that in mind, one would expect research and development (R&D) into antimicrobial resistance (AMR) to be a major objective in the pharmaceutical industry. However, it turns out that AMR innovation is a rather unattractive option for pharmaceutical firms. Because patents for new pharmaceuticals expire quickly, there is a narrow window of time for firms to make up the massive costs of R&D and turn a profit, a near-impossible task for new antibiotics. With the plethora of inexpensive generics already on the market, why would a consumer choose the new expensive antibiotic when they could get a generic for nearly free? To offset this lack of profitability, a prize-system that rewards new AMR innovation has been proposed. In searching for a way to fund the prize, I propose we focus our gaze on what is arguably the biggest contributor to AMR there is: the agriculture industry.

The largest consumer – and waster – of antibiotics is the agriculture industry. More drugs are used for animals that produce food than the people that eat them (CDC, 2013) and an estimated 75-90% of antibiotics used in feed is excreted from livestock completely unmetabolized (O’neil 2016). The industry is routinely exposing pathogens to antibiotics without killing them – directly fostering the development of drug resistance. Taxing this practice presents a win-win scenario for the health sector. If the industry opts to continue using antibiotics at such

a dangerous rate, the tax revenue generated would be more than sufficient to fund the prize. If instead, the industry responds to the tax by decreasing antibiotic use in feed, then an entirely different, but equally beneficial, victory in reducing the dangerous practice will have been achieved. On some level, it is fitting to have the very practices that are creating AMR enable to solving of it. Though the agriculture industry is clearly unequipped to “clean up their own mess” in this case, having them pay for the AMR prize comes in at a close second – one that is both feasible and effective.

Works Cited

CDC: Centers for Disease Control and Prevention. (2013). Antibiotic / Antimicrobial Resistance. Retrieved November 19, 2017, from https://www.cdc.gov/drugresistance/threat-report-2013/index.html

O’Neil, J. (May 2016). Tackling Drug-Resistant Infections Globally: Final Report and Recommendations. The Review on Antimicrobial Resistance.

Health Disparity in Alameda County

By Elleni Hailu

In Alameda county, African Americans have the lowest life expectancy, compared to all other racial groups [1]. This trend in adverse health outcomes is also correlated with income levels, as individuals with lower incomes have higher morbidity and mortality rates, not only in the U.S. but also everywhere in the world. Combined with biologic and behavioral factors, ensuring health care access can reduce health disparities. However, having access to a health care professional and adequate medical care is simply not enough for many individuals, as they are not able to follow through with their doctor’s recommendations to improve their health and to prevent adverse outcomes. This is because there are a number of underlying factors besides access to care that affect a person’s well being such as neighborhood effects (i.e. access to fresh produce and parks). Here in the Alameda county alone, 23% of the Black population lives in poverty, compared to 8% of White residents who live in poverty [1]. This gap in income is what affects the health status of many Americans and their ability to maintain their health. Hence, creating ways to ensure income equality, such as passing bills that encourage public and private sector partnerships to build more affordable housing, would be instrumental in promoting healthy living.

Reference:

[1] Lee, T. (2017, September). Epidemiology as a Tool for Social Justice. Lecture presented at Seminar for MPH Students in UC Berkeley.

Should telehealth be the way of the future in pre-natal care?

By Leah Jennings

Telehealth (also called telemedicine, eHealth, and others) is a method of delivering health information remotely, often through e-mail, phone conversations or video conferencing with care providers. Previous posts have acknowledged that telehealth has the potential to lower costs for patients and providers, eliminate time spent commuting to a central location, and allow patients to choose their care provider based on comfort rather than physical location.

Pregnant women represent an especially receptive population to health messaging due in part to their hyper-awareness of needs for their developing child and the desire to minimize harm. Women with healthy pregnancies typically have 10-15 doctor visits over the course of 9 months, and even more care is often necessary for high-risk individuals.

Clearly, pregnant women would benefit from any technology that provides equal quality of care with much more convenience. A recent study demonstrated that a novel telehealth intervention was just as effective as an in-person delivery of the same information at reducing excessive gestational weight gain (a risk factor for adverse outcomes for mother and child). Further, the telehealth intervention saved money all around: the cost to patients was 3.5 times less in the remote group and marked 50% less expenditure for the clinic.

For better or for worse, pregnant women already look to the internet as a primary source of health information. Pre-natal care providers should consider leveraging technology to maximize efficiency of health communication to their target population.

Image: celera, samantha. “cell phone.” 21 January 2008. Licensed under Attribution-NoDerivs 2.0 Generic (CC BY-ND 2.0). Accessed 30 January 2018. https://www.flickr.com/photos/scelera/2215069210