Social vulnerability and aging of elderly people in the United States

Boosting Resilience: Photovoice as a Tool for Promoting Well-Being, Social Cohesion, and Empowerment Among the Older Adult During the COVID-19 Pandemic

This paper examines how older adults who participated in an online photovoice-based group intervention program reported their experience. In a qualitative-phenomenological study, in which 13 older-adult people participated, data were collected through semi-structured in-depth interviews and analyzed through content analysis. The findings point to three central themes: a) Challenges-technical difficulties, difficulties in finding a subject for photography, investing time in photography, and an emotional-intellectual effort to put their experience into photography; b) Growth: New knowledge and skills-acquiring new knowledge, acquiring skills, experiencing skills regardless of age, and empowerment; c) Meaning-reflexivity, the ability to project feelings onto images, connection to the outside world, mindfulness, ability to choose, creativity, and critical consciousness. The findings share the way in which the use of creative visual engagement with photography contributed to coping with various challenges and enabled various gains within the process among the older-adult participants.

How to construct a frailty index from an existing dataset in 10 steps

BACKGROUND

The frailty index is commonly used in research and clinical practice to quantify health. Using a health deficit accumulation model, a frailty index can be calculated retrospectively from data collected via survey, interview, performance test, laboratory report, clinical or administrative medical record, or any combination of these. Here, we offer a detailed 10-step approach to frailty index creation, with a worked example.

METHODS

We identified 10 steps to guide the creation of a valid and reliable frailty index. We then used data from waves 5 to 12 of the Health and Retirement Study (HRS) to illustrate the steps.

RESULTS

The 10 steps are as follows: (1) select every variable that measures a health problem; (2) exclude variables with more than 5% missing values; (3) recode the responses to 0 (no deficit) through 1 (deficit); (4) exclude variables when coded deficits are too rare (< 1%) or too common (> 80%); (5) screen the variables for association with age; (6) screen the variables for correlation with each other; (7) count the variables retained; (8) calculate the frailty index scores; (9) test the characteristics of the frailty index; (10) use the frailty index in analyses. In our worked example, we created a 61-item frailty index following these 10 steps.

CONCLUSIONS

This 10-step procedure can be used as a template to create one continuous health variable. The resulting high-information variable is suitable for use as an exposure, predictor or control variable, or an outcome measure of overall health and ageing.

Social vulnerability of the people exposed to wildfires in U.S. West Coast states

Understanding of the vulnerability of populations exposed to wildfires is limited. We used an index from the U.S. Centers for Disease Control and Prevention to assess the social vulnerability of populations exposed to wildfire from 2000-2021 in California, Oregon, and Washington, which accounted for 90% of exposures in the western United States. The number of people exposed to fire from 2000-2010 to 2011-2021 increased substantially, with the largest increase, nearly 250%, for people with high social vulnerability. In Oregon and Washington, a higher percentage of exposed people were highly vulnerable (>40%) than in California (~8%). Increased social vulnerability of populations in burned areas was the primary contributor to increased exposure of the highly vulnerable in California, whereas encroachment of wildfires on vulnerable populations was the primary contributor in Oregon and Washington. Our results emphasize the importance of integrating the vulnerability of at-risk populations in wildfire mitigation and adaptation plans.

Reconsidering frailty from a human and social sciences standpoint: towards an interdisciplinary approach to vulnerability

Although frailty is an important, well-characterized concept in the provision of medical care to older adults, it has not been linked to the concept of vulnerability developed in the humanities and social sciences. Here, we distinguish between the two main dimensions of vulnerability: a fundamental, anthropological dimension in which people are exposed to a risk of injury, and a relational dimension in which people depend on each other and on their environment. The relational notion of vulnerability might provide healthcare professionals with a better understanding of frailty (and its potential interaction with precarity). Precarity situates people in their relationship with a social environment that might threaten their living conditions. Frailty corresponds to individual-level changes in adaptation to a living environment and the loss of ability to evolve or react in that environment. Therefore, we suggest that by considering the geriatric notion of frailty as a particular form of relational vulnerability, healthcare professionals could better understand the specific needs of frail, older people-and thus provide more appropriate care.

Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands

Background Frailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. Objective We compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. Methods We used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome "frail". The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). Results The ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs >0.700. Evaluating the importance of the predictors in each model, "diseases or chronic disorders" was the most important predictor in all models (10 times). The predictors "lifestyle" and "monthly income" were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1800 euro increased the likelihood of frailty. Conclusions Although the ten experts all made different graphs, the predictive performance was always satisfying (AUCs >0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of "diseases or chronic disorders", "lifestyle" and "monthly income". All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.

The Association between Social Vulnerability and Frailty in Community Dwelling Older People: A Systematic Review

The aim of this systematic literature review was to determine whether social vulnerability is associated with frailty in older people. Databases were searched for literature from January 2001 to March 2022. Hand searches of reference lists of the selected articles were also used to identify other relevant studies. Studies that met the inclusion criteria were selected. Two independent reviewers assessed the methodological quality using an established tool. Eleven eligible studies from Canada, Europe, USA, Tanzania, Mexico, and China were selected. The level of social vulnerability measured by the Social Vulnerability Index (SVI) from a meta-analysis was 0.300 (95% CI: 0.242, 0.358), with the highest SVI in Tanzania (0.49), while the lowest level of SVI was reported in China (0.15). The highest frailty level of 0.32 was observed in both Tanzania and Europe, with the lowest frailty reported in a USA study from Hawaii (0.15). In all studies, social vulnerability was a significant predictor of mortality for both sexes at subsequent data collection points. The association between SVI and frailty was high in Tanzania (r = 0.81), with other studies reporting stronger correlations for females compared to males, but at small to moderate levels. In one study, an increase of 1SD in SVI was linked to a 20% increase in frailty score at a subsequent evaluation. Additional study is warranted to determine a potential causality between social vulnerability and frailty.

Auditing completion of nursing records as an outcome indicator for identifying patients at risk of developing pressure ulcers, falling, and social vulnerability: An observational study

AIM

To evaluate the completion of nursing records through scheduled audits to analyse risk outcome indicators.

BACKGROUND

Nursing records support clinical decision-making and encourage continuity of care, hence the importance of auditing their completion in order to take corrective action where necessary.

METHOD

This was an observational descriptive study carried out from February to November 2020 with a sample of 1131 electronic health records belonging to patients admitted to COVID-19 hospital units during three observation periods: pre-pandemic, first wave, and second wave.

RESULTS

A significant reduction in nursing record completion rates was observed between pre-pandemic period and first and second waves: Braden scale 40.97%, 28.02%, and 30.99%; Downton scale: 43.74%, 22.34%, and 33.91%; Gijón scale: 40.12%, 26.23%, and 33.64% (p < 0.001). There was an increase in the number of records completed between the first and second waves following the measures adopted after the quality audit.

CONCLUSIONS

The use of scheduled audits of nursing records as quality indicators facilitated the detection of areas for improvement, allowing timely corrective actions.

IMPLICATIONS FOR NURSING MANAGEMENT

Support from nursing managers at health care facilities to implement quality assessment programmes encompassing audits of clinical record completion will encourage the adoption of measures for corrective action.

Social vulnerability and its association with food insecurity in the South African population: findings from a National Survey

Social vulnerability refers to the attributes of society that make people and places susceptible to natural disasters, adverse health outcomes, and social inequalities. Using a social vulnerability index (SVI), we investigated social vulnerability prevalence and its relationship with food insecurity in South Africa (SA). In this nationally representative cross-sectional survey, we calculated SVI scores from 3402 respondents (median age, 35 (26-46) years) using an SVI developed by the United States (US) Centers for Disease Control and prevention (CDC) adapted for a South African context. We measured food insecurity using a modified Community Childhood Hunger Identification Project. Findings classified 20.6% and 20.4% of adults as socially vulnerable and food insecure, respectively. The risk of food insecurity was almost threefold higher in the social vulnerability group (OR 2.76, 95% CI 2.76-2.77, p < 0.001) compared to their counterparts. The SVI could be a useful tool to guide government and policymakers in the facilitation of social relief initiatives for those most vulnerable.