COVID-19 Amplifiers on Health Inequity Among the Older Populations

Aging, Race, and Health Disparities: Recommendations From the Research Centers Collaborative Network

Racial disparities in adverse health outcomes with aging have been well described. Yet, much of the research focuses on racial comparisons, with relatively less attention to the identification of underlying mechanisms. To address these gaps, the Research Centers Collaborative Network held a workshop on aging, race, and health disparities to identify research priorities and inform the investigation, implementation, and dissemination of strategies to mitigate disparities in healthy aging. This article provides a summary of the key recommendations and highlights the need for research that builds a strong evidence base with both clinical and policy implications. Successful execution of these recommendations will require a concerted effort to increase participation of underrepresented groups in research through community engagement and partnerships. In addition, resources to support and promote the training and development of health disparities researchers will be critical in making health equity a shared responsibility for all major stakeholders.

Older adults in the digital health era: insights on the digital health related knowledge, habits and attitudes of the 65 year and older population

BACKGROUND

The COVID-19 pandemic has increased internet use by older age groups to an unprecedented level in Hungary mirroring the general tendency in the total population. Nevertheless, international trends indicate that this group is less likely to use digital health technologies than younger ones. The aging population raises the question of successfully integrating elderly people into the digital health ecosystem. Our research aim is to investigate the digital health usage patterns and attitudes of the population aged 65 and over through a representative sample.

METHODS

A national representative questionnaire survey was conducted by telephone (CATI), interviewing 1723 respondents. Within this sample we examined 428 people in the over-65 age group, 246 in the 65-74 age group and 182 in the over-75 age group. Predictors of demand for digital solutions were tested using binary logistic regression model.

RESULTS

50.8% of people aged 65-74 and 37.1. % of people aged 75 + use the internet for health-related purposes, mostly to access websites. 85% of respondents in 65-74 and 74% in 75 + age group have used more than one digital health device and around 70% of both age groups have a need for more than one digital solution. 90.2% (64-75 age group) and 85.7% (75 + age group) of respondents are familiar with e-prescription, 86.4% and 81.4% of them use it. 77.1% of 65-74-year-olds have heard of and nearly half 45.5% have used online appointment. More than half (52.7%) of the respondents in this age group have heard of and used electronic transmission of medical records and data. A similar proportion has heard about and used apps: 54.3% has heard of them, but only 17.3% has used them. The multivariate analyses emphasized that the need for digital solutions increases with the level of education and the more benefits one perceives in using digital solutions.

CONCLUSION

Our research has shown that the senior age group has measurable needs in the field of digital health, so helping them on this journey is in the interest of the whole health ecosystem. Their high level of interest is indicated by the fact that more than a fifth of older adults would like to have access to between 7 and 10 of the maximum number of digital devices available. The differences between the two age groups - with younger people being more open to digital solutions and using them more - and the fact that the under 65s are better adapted digitally in all respects, raises the possibility that the specific trends in digital health for older people may virtually disappear in 10 years' time (when the under 65s now enter this age group).

How Has the Nationwide Public Health Emergency of the COVID-19 Pandemic Affected Older Chinese Adults' Health Literacy, Health Behaviors and Practices, and Social Connectedness? Qualitative Evidence From Urban China

Older Chinese adults' daily lives have been affected significantly during the outbreak phase of the COVID-19 pandemic since January 2020. They were confronted with activity restrictions due to strict pandemic prevention. The older population also had to get accustomed to widely-used modern technologies in community management, such as health codes and WeChat groups. By late 2021, mainland China had reduced the prevalence of COVID-19, and people's daily lives had primarily returned to pre-pandemic normality. Under China's systematic health management during the pandemic, older Chinese adults' responses to this nationwide public health emergency may have influenced their health in the long run. However, it remains unclear what specific health changes or improvements have occurred. Such a void in the literature is worrying, given that older adults are at high health risks due to the pandemic which, might still be with humankind for a while. Thus, it is of necessity to explore and report their health changes after this official, large-scale health intervention. In this study, 17 adults aged 55 and above were recruited as interviewees. All interviewees reside in a community located in Q district, N city of the People's Republic of China. According to the findings, many interviewees now have better literacy in health risk prevention. Information and Communication Technologies (ICTs) play a significant role in getting access to health information. Specifically, television, WeChat chatting groups, and TikTok could be valuable information sources for older adults. As for the understanding and evaluation of health information, although older participants can distinguish COVID-19 rumors, they may sometimes feel confused about the underlying scientific logic. Regarding changes in health behaviors and practices, many older adults can integrate health information and knowledge into their daily lives. Additionally, although interviewees can keep important social connections, not all of them are familiar with using new ICTs, such as online chatting group, for social participation and engagement. The empirical evidence suggests that both the communities and the local governments can offer specific training programs to older residents for the sake of enhancing their health literacy, health behaviors and practices, and social connectedness during and after the pandemic.

Recommendations for the development of Clinical Practice Guidelines

Clinical practice guidelines are statements that include recommendations intended to optimize patient care, are informed by a systematic review of evidence and an assessment of the benefits and harms of alternative care options, and ensure that the best available clinical knowledge is used to provide effective and quality care. They can reduce inappropriate care and variability in clinical practice and can support the translation of new research knowledge into clinical practice. Recommendations from clinical practice guidelines can support health professionals by facilitating the decision-making process, empowering them to make more informed health care choices, clarifying which interventions should be priorities based on a favorable trade-off, and discouraging the use of those that have proven ineffective, dangerous, or wasteful. This review aims to summarize the key components of high-quality and trustworthy guidelines. Articles were retrieved from various libraries, databases, and search engines using free-text term searches adapted for different databases, and selected according to author discretion. Clinical practice guidelines in geriatrics can have a major impact on prevention, diagnosis, treatment, rehabilitation, health care, and the management of diseases and conditions, but they should only be implemented when they have high-quality, rigorous, and unbiased methodologies that consider older adult priorities and provide valid recommendations.

Assessing Predictive Factors of COVID-19 Outcomes: A Retrospective Cohort Study in the Metropolitan Region of São Paulo (Brazil)

Background and Objectives: The aim of this retrospective cohort study was to search individual, sociodemographic and environmental predictors of COVID-19 outcomes. Materials and Methods: A convenience sample of 1036 COVID-19 confirmed patients (3-99 years, mean 59 years; 482 females) who sought treatment at the emergency units of the public health system of Diadema (Brazil; March-October 2020) was included. Primary data were collected from medical records: sex, age, occupation/education, onset of symptoms, presence of chronic diseases/treatment and outcome (death and non-death). Secondary socioeconomic and environmental data were provided by the Department of Health. Results: The mean time spent between COVID-19 symptom onset and admission to the health system was 7.4 days. Principal component analysis summarized secondary sociodemographic data, and a Poisson regression model showed that the time between symptom onset and health system admission was higher for younger people and those from the least advantaged regions (availability of electricity, a sewage network, a water supply and garbage collection). A multiple logistic regression model showed an association of age (OR = 1.08; 1.05-1.1), diabetes (OR = 1.9; 1.1-3.4) and obesity (OR = 2.9; 1.1-7.6) with death outcome, while hypertension and sex showed no significant association. Conclusion: The identification of vulnerable groups may help the development of health strategies for the prevention and treatment of COVID-19.

Practical Solutions to Address COVID-19-Related Mental and Physical Health Challenges Among Low-Income Older Adults

Low-income older adults are disproportionately impacted by the COVID-19 pandemic. In this perspective article, we review the context in which low-income older people experience the pandemic and the mental and physical health consequences they have faced to date. Then, we offer practical solutions to help improve low-income older adults' sleep, physical activity, nutrition, and stress that require no or low financial commitment. We argue that governments, communities, and organizations should make greater efforts to promote healthy living for low-income older adults in times of health emergencies to ensure their ability to be universally adopted, regardless of income and resources.

Quantifying representativeness in randomized clinical trials using machine learning fairness metrics

OBJECTIVE

We help identify subpopulations underrepresented in randomized clinical trials (RCTs) cohorts with respect to national, community-based or health system target populations by formulating population representativeness of RCTs as a machine learning (ML) fairness problem, deriving new representation metrics, and deploying them in easy-to-understand interactive visualization tools.

MATERIALS AND METHODS

We represent RCT cohort enrollment as random binary classification fairness problems, and then show how ML fairness metrics based on enrollment fraction can be efficiently calculated using easily computed rates of subpopulations in RCT cohorts and target populations. We propose standardized versions of these metrics and deploy them in an interactive tool to analyze 3 RCTs with respect to type 2 diabetes and hypertension target populations in the National Health and Nutrition Examination Survey.

RESULTS

We demonstrate how the proposed metrics and associated statistics enable users to rapidly examine representativeness of all subpopulations in the RCT defined by a set of categorical traits (eg, gender, race, ethnicity, smoking status, and blood pressure) with respect to target populations.

DISCUSSION

The normalized metrics provide an intuitive standardized scale for evaluating representation across subgroups, which may have vastly different enrollment fractions and rates in RCT study cohorts. The metrics are beneficial complements to other approaches (eg, enrollment fractions) used to identify generalizability and health equity of RCTs.

CONCLUSION

By quantifying the gaps between RCT and target populations, the proposed methods can support generalizability evaluation of existing RCT cohorts. The interactive visualization tool can be readily applied to identified underrepresented subgroups with respect to any desired source or target populations.