A systematic review of evidence-based aftercare for older adults following self-harm

Objective: Self-harm is closely associated with suicide in older adults and may provide opportunity to intervene to prevent suicide. This study aimed to systematically review recent evidence for three components of aftercare for older adults: (1) referral pathways, (2) assessment tools and safety planning approaches and (3) engagement and intervention strategies. Methods: Databases PubMed, Medline, PsychINFO, Embase and CINAHL were searched from January 2010 to 10 July 2021 by two reviewers. Empirical studies reporting aftercare interventions for older adults (aged 60+) following self-harm (including with suicidal intent) were included. Full text of articles with abstracts meeting inclusion criteria were obtained and independently reviewed by three authors to determine final studies for review. Two reviewers extracted data and assessed level of evidence (Oxford) and quality ratings (Alberta Heritage Foundation for Medical Research Standard Quality Assessment Criteria for quantitative and Attree and Milton checklist for qualitative studies), working independently. Results: Twenty studies were reviewed (15 quantitative; 5 qualitative). Levels of evidence were low (3, 4), and quality ratings of quantitative studies variable, although qualitative studies rated highly. Most studies of referral pathways were observational and demonstrated marked variation with no clear guidelines or imperatives for community psychiatric follow-up. Of four screening tools evaluated, three were suicide-specific and one screened for depression. An evidence-informed approach to safety planning was described using cases. Strategies for aftercare engagement and intervention included two multifaceted approaches, psychotherapy and qualitative insights from older people who self-harmed, carers and clinicians. The qualitative studies identified targets for improved aftercare engagement, focused on individual context, experiences and needs. Conclusion: Dedicated older-adult aftercare interventions with a multifaceted, assertive follow-up approach accompanied by systemic change show promise but require further evaluation. Research is needed to explore the utility of needs assessment compared to screening and evaluate efficacy of safety planning and psychotherapeutic approaches.

Quality of Educational Programs for Elementary School-Age Students With Autism

The purpose of this study was to assess the quality of educational programs for school-aged children with autism in the United States. Investigators completed the Autism Program Environment Quality Rating Systems-Preschool/Elementary (APERS-PE) in 60 elementary schools enrolling children with autism. The mean total rating scores were near the midpoint rating, indicating schools were providing educational program environments classified as adequate but not of high quality. Domains of the APERS-PE reflecting structural quality tended to be significantly above average and domains reflecting process quality tended to be significantly below average. With a few exceptions, inclusive and special education program did not differ significantly in total program quality ratings and reflected the same pattern of domain quality ratings.

Factors Affecting the Accessibility of Voice Telephony for People with Hearing Loss: Audio Encoding, Network Impairments, Video and Environmental Noise

This paper describes four studies with a total of 114 individuals with hearing loss and 12 hearing controls that investigate the impact of audio quality parameters on voice telecommunications. These studies were first informed by a survey of 439 individuals with hearing loss on their voice telecommunications experiences. While voice telephony was very important, with high usage of wireless mobile phones, respondents reported relatively low satisfaction with their hearing devices’ performance for telephone listening, noting that improved telephone audio quality was a significant need. The studies cover three categories of audio quality parameters: (1) narrowband (NB) versus wideband (WB) audio; (2) encoding audio at varying bit rates, from typical rates used in today's mobile networks to the highest quality supported by these audio codecs; and (3) absence of packet loss to worst-case packet loss in both mobile and VoIP networks. Additionally, NB versus WB audio was tested in auditory-only and audiovisual presentation modes and in quiet and noisy environments. With WB audio in a quiet environment, individuals with hearing loss exhibited better speech recognition, expended less perceived mental effort, and rated speech quality higher than with NB audio. WB audio provided a greater benefit when listening alone than when the visual channel also was available. The noisy environment significantly degraded performance for both presentation modes, but particularly for listening alone. Bit rate affected speech recognition for NB audio, and speech quality ratings for both NB and WB audio. Packet loss affected all of speech recognition, mental effort, and speech quality ratings. WB versus NB audio also affected hearing individuals, especially under packet loss. These results are discussed in terms of the practical steps they suggest for the implementation of telecommunications systems and related technical standards and policy considerations to improve the accessibility of voice telephony for people with hearing loss.

Mentorship Program Content Ratings: Advancing Diversity in Aging and Alzheimer’s Disease Research and Careers

MADURA is an R25 Advancing Diversity in Aging Research (ADAR) undergraduate training program funded by the National Institute on Aging and based at the University of California San Diego. Its objectives are to improve retention and academic success of underrepresented minority (URM) trainees, and to increase graduate/medical school applications and/or entry into Aging/ADRD careers. This multi-component program includes paid research lab experience, research and academic skills training, peer support, faculty-led small groups, guest seminars, and additional professional development opportunities. The Year 1 Cohort of 32 students consisted of Hispanic/Latinx and other students under-represented in collegiate MSTEM studies. Almost ¾ of the mentees also came from disadvantaged backgrounds, per current NIH criteria. Evaluation data have been collected through brief, weekly, anonymous online student surveys, plus more comprehensive quarterly mentee and mentor surveys. This poster will describe the Program structure and 20+ group activities, trainings, seminars, and modalities, and report student ratings of satisfaction and utility. 92% of trainees rated the overall program value as Excellent, and 92% also rated effectiveness in promoting Aging/ADRD careers among underrepresented students as Excellent. Quality ratings of components such as pay, research placements, group training and group mentorship were rated as Excellent or Good by 96%. Relative student component rankings, specific suggestions for improvements, plus Mentee and Mentor perspectives on research-specific training needs, will also be presented. Based upon these experiences and data, the authors will make specific suggestions for future URM research training, mentorship program content, and expanded delivery modalities.

Higher Star, Better Performance in US Nursing Homes during the Covid-19 Pandemic ?

The Covid-19 pandemic has brought terrible difficulties to nursing homes, as they were locations with the highest number of confirmed Covid-19 cases and deaths in the US. The Centers for Medicare & Medicaid Services (CMS) applies the Five-Star Quality Ratings to indicate the quality of care in nursing homes based on health inspection survey, staffing, and resident outcome. Studies to date have contradictory findings regarding the relationship between nursing home reported Quality Ratings and Covid-19 cases and deaths based on US regional data. The purpose of this study is to examine whether nursing homes’ Quality Ratings were related to the total number of resident Covid-19 cases and deaths at the US National level. The study examined US nursing homes (N=13,494) linked with CMS Nursing Home Compare data and Covid-19 Nursing Home data. Using multiple linear regression analyses, results showed nursing home Quality Ratings were significantly associated with Covid-19 residents’ cases and deaths controlling for ownership type, size, occupation rate, and years of operation (p<.001; p<.001). Five-star nursing homes were less likely to have Covid-19 cases and deaths. Further, comparing lower Star Ratings nursing homes, 1-Star nursing homes showed no significant difference to 2-Star and 3-Star nursing homes when examining Covid-19 cases and deaths. Overall, the Five-Star Quality Ratings is a useful measure when investigating nursing homes’ performance during the Covid-19 pandemic. Future policymakers and administrators should also focus on nursing homes with lower star ratings when improving the quality of nursing homes, particularly with regard to resident health.

COVID-19 Nursing Home Deaths in Cook County: Predictors and Modifiable Risks

Investigators at the University of Illinois Chicago, the Illinois Department on Aging (IDOA), the Illinois Department of Public Health, and the Health and Medicine Policy Research Group are collaborating to examine comparative rates of Covid-19-related deaths among older adults who reside in nursing homes vs. the community in Illinois. As a first step, we have examined data from the Cook County Medical Examiner’s office to compare nursing home resident fatalities to those who died in the community. Deaths with Covid-19 listed as primary or secondary cause of death that occurred between January 1, 2020 to September 30, 2020 among older adults ages 60 and over were identified from the Cook County Medical Examiner’s Office case archive file. Location at death and race/ethnicity were obtained from the same source. Location at death was matched with data in the Center for Medicare and Medicaid Services (CMS) Covid-19 Nursing Home Data to identify persons who died in skilled nursing facilities (SNFs) as well as facility and staff characteristics. We found that the 3,937 deaths among persons over the age of 60 comprised 75% of total deaths in Cook County. Of the total older adult deaths, 2,090 (53%) died in the community and 1,837 (47%) died in SNFs. Regression analyses that controlled for CMS quality ratings found that larger, for-profit nursing homes, with high levels of staff infected with Covid-19 were associated with higher mortality. The policy implications of these findings will be discussed.

Importance of Personality Factors in Determining the Psychological Consequences of Infertility: A Systematic Review

Background and purpose The diagnosis of infertility not only involves biological but the psychosocial links have also been established. Individual difference variables, such as personality and temperament have been found playing prominent role in modifying the psychological and biological aspects of infertility. This systematic review aimed to examine evidence-based research on the role of personality traits in determining vulnerability to stress in infertility, understand the gender-based differences, and deliver suggestions for future researches. Method The search for studies relating to the variables was accomplished using various electronic databases. The search was kept limited to a time span of about 20 years, that is, from January 2000 to April 2020. Additional researches were collected from library source and others were retrieved by contacting experts. Studies were selected on the basis of a predetermined inclusion and exclusion criteria, and quality of the studies was also taken into account. Results The search of studies through above methods was fruitful in identifying 23 studies including six longitudinal and prospective studies, and 17 cross-sectional studies covering both individual- and model-based personality attributes related findings. The overall quality ratings of the studies ranged from fair to good. Conclusion The review revealed that personality factors such as neuroticism, harm avoidance, and psychoticism are the potential risk factors, whereas optimism has a protective impact in the context of fertility disorders. The long-term impact of personality on infertility needs further exploration. Trait modification interventions during the treatment of infertility were also suggested in the light of previous findings.

Evaluation of utility and usefulness of webinars on COVID-19 management: a questionnaire-based survey

Background The COVID-19 pandemic and its consequent “social distancing” has fueled the use of social media platforms for educational purposes. Since the start of the pandemic, a plethora of experts and self-proclaimed experts have been keenly delivering webinars on COVID. This begs the question “Do webinars on COVID-19 really help in the improvement of knowledge base or management skills?”. The questionnaire was designed to assess information regarding COVID-webinars and their usefulness from the end-user standpoint. The response to the questions was measured using a 4- or 5-point Likert scale. The survey was open for a 4-week period with the extension of 1 week. Results The response rate was 54% as 270 out of 500 participants responded to the questionnaire. The majority of the respondents were anesthesiologists in-training, post-graduates, fellows, and seniors belonging to tertiary care settings with fewer percentages belonging to physicians and others. Most of the doctors had attended an average of 2 webinars per week. The tests of model effects showed a significant negative correlation of webinar quality ratings for district hospital healthcare setting of the attendees (of p value of 0.013) and for the number of COVID-related webinars attended per week (p value of 0.009). Conclusions Most respondents had favorable perceptions of webinars happening during the pandemic. However, there is a need for improvisation in the volume of webinars, target-audience-based delivery, and participant interaction to add value to this new dimension of teaching-learning.

Wisdom of the Experts versus Wisdom of the Crowd in Hospital Quality Ratings: Cross-Sectional Analyses of Google Ratings (Preprint)

BACKGROUND Popular online portals provide free and convenient access to user-generated quality reviews. Centers for Medicare and Medicaid Services (CMS) also provide patients with Hospital Compare Star Ratings (HCSR), a single public measure of hospital quality aggregating multiple quality dimensions. Consumers often use crowdsourced hospital ratings on platforms such as Google to select hospitals, but it is unknown if these ratings reflect a comprehensive measure of clinical quality. OBJECTIVE We analyze if Google online quality ratings, which reflect the wisdom of the crowd, are associated with HCSR, which reflect the wisdom of the experts. CMS revised the methodology of assigning star ratings to hospitals. Therefore, we analyze these associations before and after the 2021 revisions of the CMS rating system. METHODS We extracted Google ratings using Application Programming Interface (API) in June 2020. The HCSR data of April 2020 (before the revision of HCSR methodology) and April 2021 (after the revision of HCSR methodology) were obtained from CMS’ Hospital Compare (HC) website. We also extracted scores for the individual components of hospital quality for each of the hospitals in our sample using the code provided by HC. Fractional Response Model (FRM) was used to estimate the association between Google Ratings and HCSR and individual components of quality. RESULTS Results indicate that Google ratings are statistically associated with HCSR (P<.001) after controlling for hospital level effects. A one star improvement in CMS ratings before the change in methodology (after the change in methodology) is expected to increase the Google ratings by 0.145 (0.135) on average (95% CI 0.127- 0.163; P<.001, 95% CI 0.116-0.153; P<.001). The analyses with individual components of hospital quality reveal that Google ratings are not associated with components of HCSR that require medical expertise such as ‘Safety of care’ or ‘Readmissions’. The revised CMS rating system ameliorates previous partial inconsistencies in association between Google ratings and component scores of HCSR. CONCLUSIONS Overall, crowd sourced Google hospital ratings are informative about expert CMS hospital quality ratings and several individual quality components that are easier for patients to evaluate. Therefore, hospitals should not expect improvements in quality metrics that require expertise to assess such as safety of care and readmission to result in improved Google star ratings. Hospitals can benefit from using crowd-sourced ratings as timely, easily available, and dynamic indicators of their quality performance.

O033 Predicting subjective sleep quality using multi-day actigraphy data: A machine learning approach

Objectives Insomnia is diagnosed using clinical interview but actigraphy is often used as a consecutive multi-day measurement of activity-rest cycles to quantify sleep-wake periods. However, discrepancies between subjective complaints of insomnia and objective actigraphy measurement exist. The aims of the current study were to (i) predict subjective sleep quality using actigraphic data and, (ii) identify features of actigraphy that are associated with poor subjective sleep quality. Methods Actigraphy data were collected for 14-consecutive days with corresponding subjective sleep quality ratings from participants with Insomnia Disorder and healthy controls. We fitted multiple machine learning algorithms to determine the best performing method with the highest accuracy of predicting subjective quality rating using actigraphic data. Results We analysed a total of 1278 days of actigraphy and corresponding subjective sleep quality ratings from 86 insomnia disorder patients and 20 healthy controls. The k-neighbors classifier provided the best performance in predicting subjective sleep quality with an overall accuracy, sensitivity and specificity of 83%, 74% and 87% respectively, and an average AUC-ROC of 0.88. We also found that activity recorded in the early morning (04:00-08:00) and overnight periods (00:00-04:00) had the greatest influence on sleep quality scores, with poor sleep quality related to these periods.. Conclusions A machine learning model based on actigraphy time-series data successfully predicted self-reported sleep quality. This approach could facilitate clinician’s diagnostic capabilities and provide an objective marker of subjective sleep disturbance.