Mansoor Malik, MD

Older adults have been hit hard by coronavirus disease 2019 (COVID-19). Mortality data from Oxford COVID-19 Evidence Service indicates a risk of mortality of 3.6% for people in their 60s, which increases to 8.0% and 14.8% for people in their 70s and over 80s.

 An age-related mortality study from China showed that patients of COVID-19 above aged 55 years had 3 times increased mortality. Older individuals are much more likely to develop COVID-19 related complications.

 The increased mortality reflects the underlying biological, social, and psychological vulnerabilities faced by the older population. Elderly individuals are also disproportionately affected by social distancing policies and other restrictions to stop the spread of the virus, resulting in increased loneliness, social isolation, and loss of freedom and support networks.

Over the course of the pandemic, we have seen considerable media coverage about the risk of COVID-19 to older people. Unfortunately, much of this discourse has negatively fueled an already existing ageist sentiment. Ageism, which some define as stereotyping, prejudice, or discrimination toward people on the basis of age, is a serious international concern with important health implications.

 Multiple studies have indicated that ageism toward older adults is highly prevalent across countries and continues to grow with global population aging.

 Negative attitudes toward older adults pose significant risks to the health and well-being of older individuals, such as higher mortality risk, poor functional health, slower recovery from illness, and poor mental health.

Negative attitudes toward this population are particularly prevalent on social media. A Twitter analysis of 18,128 tweets between March 12 and March 21, 2020, showed that about one‐quarter of all tweets could be considered ageist, either because they included jokes or ridicule aimed at older adults or because their content downplayed the relevance of COVID‐19 and/or implied their lives were less valuable.

 Ageist attitudes are nothing new. A previous study of Facebook posts indicated that 74% excoriated older individuals, 27% infantilized them, and 37% advocated banning them from public activities, such as shopping.

 Various social media discourses have surfaced about prejudiced representation and denigration of older people in society. For example, COVID-19 has been characterized with the hashtag #BoomerRemover, which has been trending on social media platforms, often accompanied with ageist disparaging and devaluing memes.

Public discourse during the pandemic has increasingly portrayed those over the age of 70 as helpless, frail, and unable to contribute to society. These views are being spread by social media, the press, and public announcements by government officials throughout the world. There has been considerable media coverage and online discussions about potential for rationing of care of elderly people as they are more expendable than persons in other age groups. Some of the coverage seems to suggest that the deaths of older people somehow are not as important as the younger population.

There have been numerous examples of flagrant discrimination against older individuals during COVID-19. Earlier in its course, COVID-19 was believed primarily to affect elderly people. Many young people did not follow adequate precautions, and strong public health warnings were not issued. Even after COVID-19 outbreaks in many nursing homes, Centers for Disease Control and Prevention (CDC) guidelines did not identify residents in long-term care as a high priority for testing.

 Treatment resources and personal protective equipment have been consistently directed toward the younger population.

 In a press briefing in March, World Health Organization Director-General Tedros Ghebreyesus took unnamed nations to task for failing to treat the death of older adults as a “serious issue” and exhibiting signs of “moral decay.”

One of the disturbing social consequences of COVID-19 includes a significant increase in the number of elder abuse cases, paralleling the rise of ageism. The CDC defines elder abuse as an “intentional act or failure to act by a caregiver or another person in a relationship involving an expectation of trust that causes or creates a serious risk of harm to an older adult.”

 Elder abuse was a huge public health problem globally even before COVID-19. It is estimated that in the United States, approximately 1 in 10 individuals over the age of 60 suffers physical, verbal or sexual abuse, or financial exploitation.

Elder abuse is significantly underreported. Only 1 in 24 cases may be reported.

 In almost 60% of elder abuse and neglect incidents, the perpetrator is a family member.

 Social isolation is a significant risk for elder abuse. Other risk factors include cognitive impairment, physical frailty, and dependence on others for care. Nursing home residents are also particularly vulnerable to elder abuse.

 All of these risk factors have been compounded by COVID-19.

To reiterate, since the start of the COVID-19 pandemic, there has been a massive increase in the reports of elder abuse. Reports range from incidents of family violence, neglect, and financial scams. The Federal Trade Commission and the American Bar Association as well various state departments have issued warnings to the public.

 In Europe, there have been distressing reports of older people abandoned in care homes in Spain.

 In Italy, where 23% of the population is aged 65 years or older, some hospitals have used chronological age-based cutoffs to ration ventilators.

Mortality data from the Oxford COVID-19 Evidence Service indicates a risk of mortality of 3.6% for people in their 60s, which increases to 8.0% and 14.8% for people in their 70s and 80s.

Not only do pervasive ageist attitudes and stereotypes contribute to negative health outcomes, but ageism among health care professionals can also result in discriminatory practices that place older people at risk.

 Studies have found widespread negative attitudes toward older people and old age among physicians, medical students, and nurses.

 Such attitudes can lead to practical consequences for older patients. For example, assumptions regarding functional and cognitive decline can lead to more limited provision of medical information, withholding certain treatment options, and exclusion from clinical trials.

 Ageist attitudes have also been found among mental health practitioners and trainees (such as assuming that depression symptoms are normal among elderly adults), which in turn may restrict access to treatments and contribute to functional decline.

Unfortunately, many well-intentioned efforts to combat the effects of COVID-19 have also resulted in marginalization of elderly adults. For example, recent shift towards telemedicine excludes roughly half of the adults over age 65 in the United States who lack access to broadband services.

Elder abuse and ageism are extremely hard problems to combat. There are no clear ways to reduce ageism and change attitudes toward aging. Combined interventions with education and intergenerational contact seem to show the largest effects on attitudes.

Adult Protection Services (APS) run by local or state health departments are usually responsible for investigating cases of elder abuse. However, few financial resources, insufficient access to information needed to resolve elder abuse cases, inadequate administrative systems, and lack of cross‐training with other disciplines in the aging field serving clients with mental health disabilities—these have hindered the role of APS workers in intervening in abusive situations.

Two important federal laws address the issue of elder abuse: the Older Americans Act (OAA) and the Elder Justice Act (EJA). However, lack of funding and relaxed enforcement have diluted their effectiveness. The EJA has received less than 10% of the funding that was authorized, especially with respect to achieving its main goal of dedicated funding for APS.

 Denial of elder abuse and lack of funding continues to be a significant problem in the enforcement of existing laws. Urgent policy changes are needed to strengthen the protections for the elderly during COVID-19.

is a Professor in the Department of Psychiatry at Johns Hopkins University SOM.

Department of Psychiatry, Johns Hopkins University SOM.

is the Elizabeth Plank Althouse Professor and Chairman of the Department of Psychiatry, Bayview Campus, Johns Hopkins University SOM.

1. Oxford COVID-19 Evidence Service (2020). Global COVID-19 case fatality rates. Accessed September 25, 2020. 

https://www.cebm.net/oxford-covid-19-evidence-service/

2. Liu K, Chen Y, Lin R, Han K. Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. 

3. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. 

. 2020;323(13):1239-1242. Accessed September 18, 2020. 

https://jamanetwork.com/journals/jama/fullarticle/2762130

4. Butler RN. Ageism. In: Maddox GL, ed. 

. New York, Springer Publishing; 2000:38–9.

5. North MS, Fiske ST. Modern attitudes toward older adults in the aging world: a cross-cultural meta-analysis. 

6. Chang ES, Kannoth S, Levy S, Wang SY, Lee JE, Levy BR. Global reach of ageism on older persons’ health: A systematic review. 

. 2020;15(1):e0220857. Accessed September 18, 2020. 

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220857

7. Jimenez‐Sotomayor MR, Gomez‐Moreno C, Soto‐Perez‐de‐Celis E. Coronavirus, Ageism, and Twitter: An Evaluation of Tweets about Older Adults and COVID‐19. 

8. Levy BR, Chung PH, Bedford T, Navrazhina K. Facebook as a site for negative age stereotypes. 

9. Ayalon L. There is nothing new under the sun: ageism and intergenerational tension in the age of the COVID-19 outbreak. 

. 2020;1‐4. Published online April 14, 2020.

10. Ayalon L, Chasteen A, Diehl M, et al. Aging in Times of the COVID-19 Pandemic: Avoiding Ageism and Fostering Intergenerational Solidarity. J Gerontol B Psychol Sci Soc Sci. 2020. Published online April 16, 2020.

11. Overview of Testing for SARS-CoV-2 (COVID-19). CDC. Updated September 18, 2020. Accessed September 25, 2020. 

https://www.cdc.gov/coronavirus/2019-ncov/hcp/testing-overview.html

12. Mahr J. Short staffing. PPE shortages. Few inspections. Why calls are growing for Illinois nursing home regulators to step up efforts on COVID-19. 

 May 13, 2020. Accessed September 18, 2020. 

https://www.chicagotribune.com/coronavirus/ct-coronavirus-illinois-nursing-homes-regulators-pritzker-20200513-3bdofjnaivhadg2yt7kcf2psm4-story.html

13. Carbonaro G. ‘Every life matters’: WHO chief warns against COVID-19 age discrimination. 

 March 11, 2020. Accessed September 18, 2020. 

https://newseu.cgtn.com/news/2020-03-11/-Every-life-matters-WHO-warns-against-COVID-19-age-discrimination-OKDSHuH0gU/index.html

14. Elder Abuse. CDC. Accessed September 18, 2020. 

https://www.cdc.gov/violenceprevention/elderabuse/index.html

15. Acierno R, Hernandez MA, Amstadter AB, et al. Prevalence and correlates of emotional, physical, sexual, and financial abuse and potential neglect in the United States: the National Elder Mistreatment Study. 

16. Lifespan of Greater Rochester Inc, Weill Cornell Medical Center of Cornell University, New York City Department for the Aging. Under the Radar: New York State Elder Abuse Prevalence Study. May 2011. Accessed September 18, 2020. 

https://ocfs.ny.gov/main/reports/under%20the%20radar%2005%2012%2011%20final%20report.pdf

17. Amstadter AB, Cisler JM, McCauley JL, Hernandez MA, Muzzy W, Acierno R. Do incident and perpetrator characteristics of elder mistreatment differ by gender of the victim? Results from the National Elder Mistreatment Study. 

18. Armitage R, Nellums LB. COVID-19 and the consequences of isolating the elderly. 

https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(20)30061-X/fulltext

19. Tressler C. Coronavirus: Scammers follow the headlines. Federal Trade Commission Consumer Information. February 10, 2020. Accessed September 18, 2020. 

https://www.consumer.ftc.gov/blog/2020/02/coronavirus-scammers-follow-headlines

20. Minder R, Peltier E. A deluged system leaves some elderly to die, rocking Spain’s self-image. 

. Updated March 29, 2020. Accessed September 18, 2020. 

https://www.nytimes.com/2020/03/25/world/europe/Spain-coronavirus-nursing-homes.html

21. Burnes D, Sheppard C, Henderson CR Jr, et al. Interventions to Reduce Ageism Against Older Adults: A Systematic Review and Meta-Analysis. 

. 2019;109(8):e1‐e9. Accessed September 18, 2020. 

https://ajph.aphapublications.org/doi/full/10.2105/AJPH.2019.305123

22. Chrisler JC, Barney A, Palatino B. Ageism can be hazardous to women’s health: ageism, sexism, and stereotypes of older women in the healthcare system. 

23. Briggs R, Robinson S, O’Neill D. Ageism and clinical research. 

24. Bodner E, Palgi Y, Wyman MF. Ageism in mental health assessment and treatment of older adults. In: Ayalon L, Tesch-Römer C, eds. International Perspectives on Aging. Cham, Switzerland, Springer Nature; 2018;19:241–262.

25. Blancato B. It’s time to make the Elder Justice Act more effective. 

 August 14, 2019. Accessed September 18, 2020. 

https://www.forbes.com/sites/nextavenue/2019/08/14/its-time-to-make-the-elder-justice-act-more-effective/#6b87f7e45605

Articles

The pandemic hit everyone hard, but what implications and consequences has it had for the older generations? 

Elder Abuse and Ageism During COVID-19

The impact of nonadherence is staggering. It is estimated that at least 50% of patients with chronic health conditions are nonadherent; 20% to 30% of prescriptions are 

 In the US, nonadherence is responsible for an estimated 125,000 deaths and between $100 to $300 billion in medical costs annually.

Treatment nonadherence in patients with psychiatric disorders is similar or higher than with other chronic conditions. At least 61% of patients with schizophrenia, 57% of patients with bipolar disorder, and 52% of patients with depression had problems with adherence.

 Even with depot medications, many patients are treatment nonadherent within one year.

Measuring adherence is tricky. Adherence to treatment occurs on a spectrum from total adherence to total nonadherence. It is also a dynamic process-patients can be selective in their adherence, and their adherence levels can change over time. The definition of adherence also varies: nonadherence is defined as patients missing medications from 20% to 50% of the time.

Adherence is almost always overestimated. Most studies use pharmacy prescription claims data as a proxy for adherence. Most commonly used measures are Medication Possession Ratio (MPR) and the Proportion of Days Covered (PDC). MPR is the ratio of the number of days for which a patient has medications on hand divided by the total number of days the patient was observed. Because the assumption is that nonadherent patients are unlikely to fill their prescriptions, MPR is likely to overestimate adherence for patients who fill their prescriptions. The Proportion of Days Covered (PDC) can be more accurate. It uses several “covered” days for which patients have medications on hand. However, neither MRP nor PDC measure actual ingestion of medications.

Technology-based solutions are available that can closely approximate patients’ adherence. The Medication Event Monitoring System (MEMS) uses microtechnology to detect when a medication container is opened. This technology can be applied to smart pillboxes labeled with the day and time of each medication. The data are transferred via the patient’s cell phone to providers. MEMS can also send text messages to remind patients to take their meds.

Another approach is edible digital sensors embedded in the medication. The sensors are activated by gastric acid and send a signal to a remote monitoring system. In 2018, the FDA approved the first use of this technology for mental health disorders. A tablet formulation of aripiprazole was approved with a small (1 mm x 1 mm) sensor. Activated in the stomach it sends a signal to a wearable sensor worn on the chest. The data are then transmitted to a cloud server via a smartphone app, and can be accessed by the treating clinician.

Multiple factors play a role in nonadherence (

); most is attributable to patients’ perceptual factors. Findings from a 

 of patients with chronic health conditions indicate that 62% of patients forgot to take their medications, 37% reported running out of medications, and 23% were careless about taking medications.

Three themes of medication adherence came to light in a review of the literature on schizophrenia: perceived lack of control, risk of dependence on medications or provider, and stigma associated with medication use. Self-efficacy and an internal locus of control are positively correlated with medication adherence. While poor insight into illness is associated with higher rates of previous admissions and nonadherence.

Another predictor of adherence is physician related-the therapeutic alliance plays a key role in treatment adherence. A collaborative relationship, agreement on treatment tasks, and stability of the alliance are necessary elements of better treatment adherence. The patient needs to be involved in the decision-making process for treatment because factors such as medication dosage, pill burden, and regimen complexity influence adherence. Practical and financial issues also affect adherence: how difficult is it for the patient to get to appointments or pharmacies for prescriptions? Can the patient afford the copays for medications? An analysis of comprehensive Medicaid claims shows that the increase in medication copayment is directly related to nonadherence.

As expected, stigma and perceived criticism about mental health issues can have a negative impact on adherence. In a large study, self-stigma highly correlated with rates of nonadherence: patients with bipolar disorder had the highest rates of medication discontinuation at 65%, but living with a partner lowered the rates of self-stigma and decreased nonadherence.

 Symptom severity and overall level of disability also negatively affect adherence. Patients with depression and substance use disorders are less likely to be adherent.

Most of the research in treatment adherence targets barriers to treatment and relies on self-reported estimates of adherence. Studies have generally shown that improved engagement with patients results in greater adherence. Overall, multilevel interventions are likely to be more effective (

Psychoeducation has been the mainstay of intervention. Other interventions include family/peer support, pharmacist interventions, reminders to fill prescriptions by phone or text, even payment for medication adherence. However, the effect size of these interventions is small, and the relationship to clinical outcomes is unclear.

 found 182 intervention studies published between 2007 and 2013.

 These studies were extremely heterogeneous, and even the best quality studies that used multiple interventions demonstrated only small results. Five studies reported clinical outcome measures but did not show significant improvement; 60% of the studies were underpowered.

In a review of multiple studies, the results were mixed. The most frequent intervention was patient education and counseling, with positive results seen in half the studies. Interventions delivered by nurses and pharmacists had better results than those provided by physicians.

There are no studies that are specific to nonadherence in patients with psychiatric disorders. However, there have been reports of mobile health (mHealth) interventions that target nonadherence. mHealth uses smartphones and other wireless technology. Increasingly popular, a report from the Pew Research Center indicates that in 2017, 46% of patients used their cell phones or tablets to access health information.

 The most common way mHealth is being used is for education via smartphones apps. There are hundreds of thousands of health-related apps available for download.

The apps can be a useful and easy way to get information and measure certain behaviors; however, privacy is not assured, and they can be inaccurate. In a review of 571 mental health apps, 60% did not use validated screening tools and two-third of the apps did not provide a privacy policy. One exception is the app designed by the US Department of Veterans Affairs for the treatment of PTSD, which is of high quality and does not collect or share patient data.

Over the last decade, there has been a lot of interest in using Short Messages Service (SMS) for behavioral interventions. There are an estimated 300 million cell phones in the US, and most of the people use text messaging. Text messaging has been one of the most common interventions used to enhance adherence. A systematic review found that reminders such as phone calls were most effective.

Treatment nonadherence is highly prevalent with significant negative consequences, yet it is rarely addressed in routine clinical care. A high index of suspicion is needed to identify nonadherence and when identified, use evidence-based interventions address nonadherence.

Nonadherence should be addressed at multiple levels. Recent technological advances have made it possible to leverage mobile communications to improve adherence; however, a great need still exists for adequately powered studies to elucidate the mechanisms of and interventions for treatment nonadherence.

This article was originally posted on February 3, 2020, and has since been updated.

 is Clinical Professor, Johns Hopkins University, Baltimore, MD; 

 is Associate Professor, Howard University, Washington, DC.

The authors report no conflicts of interest concerning the subject matter of this article.

1. DiMatteo MR, Giordani PJ, Lepper HS, et al.

 Patient adherence and medical treatment outcomes: a meta-analysis

2. Cutler RL, Fernandez-Llimos F, Frommer M, et al. Economic impact of medication non-adherence by disease groups: a systematic review. 

3. Valenstein M, Gaznoczy D, McCarthy JF, et al. Antipsychotic adherence over time among patients receiving treatment for schizophrenia: a retrospective review. 

4. Jawad I, Watson S, Haddad PM, et al. Medication nonadherence in bipolar disorder: a narrative review. 

5. Gadkari AS, McHorney CA. Unintentional non-adherence to chronic prescription medications: how unintentional is it really? 

6. Medina E, SalvÃ  J, Ampudia R, et al. Short-term clinical stability and lack of insight are associated with a negative attitude towards antipsychotic treatment at discharge in patients with schizophrenia and bipolar disorder. 

7. Pawaskar MD, Xu L, Tang Y, et al. Effect of medication copayment on adherence and discontinuation in medicare beneficiaries with type 2 diabetes: a retrospective administrative claims database analysis. 

8. Kamaradova D, Latalova K, Prasko J, et al. Connection between self-stigma, adherence to treatment, and discontinuation of medication. 

9. Nieuwlaat R, Wilczynski N, Navarro T, et al. 

Interventions for enhancing medication adherence

10. Wilhelmsen NC, Eriksson. Medication adherence interventions and outcomes: an overview of systematic reviews. 

11. Pew Research Center. Mobile Fact Sheet. Jue 20, 2019.

12. Owen JE, Kuhn E, Jaworski BK, et al. VA mobile apps for PTSD and related problems: public health resources for veterans and those who care for them. 

13. Kini V, Ho PM. Interventions to improve medication adherence: a review. 

A number of factors affect treatment adherence, including the therapeutic alliance, perceived perceived lack of control, risk of dependence on medications, stigma associated with medication use, and more.

Mansoor Malik, MD

Articles

Elder Abuse and Ageism During COVID-19

Treatment Nonadherence: An Epidemic Hidden in Plain Sight