Diagnostic accuracy of eHealth literacy measurement tools in older adults: a systematic review.

BACKGROUND: In Canada, virtual health care rapidly expanded during the COVID-19 pandemic. There is substantial variability between older adults in terms of digital literacy skills, which precludes equitable participation of some older adults in virtual care. Little is known about how to measure older adults' electronic health (eHealth) literacy, which could help healthcare providers to support older adults in accessing virtual care. Our study objective was to examine the diagnostic accuracy of eHealth literacy tools in older adults. METHODS: We completed a systematic review examining the validity of eHealth literacy tools compared to a reference standard or another tool. We searched MEDLINE, EMBASE, CENTRAL/CDSR, PsycINFO and grey literature for articles published from inception until January 13, 2021. We included studies where the mean population age was at least 60 years old. Two reviewers independently completed article screening, data abstraction, and risk of bias assessment using the Quality Assessment for Diagnostic Accuracy Studies-2 tool. We implemented the PROGRESS-Plus framework to describe the reporting of social determinants of health. RESULTS: We identified 14,940 citations and included two studies. Included studies described three methods for assessing eHealth literacy: computer simulation, eHealth Literacy Scale (eHEALS), and Transactional Model of eHealth Literacy (TMeHL). eHEALS correlated moderately with participants' computer simulation performance (r = 0.34) and TMeHL correlated moderately to highly with eHEALS (r = 0.47-0.66). Using the PROGRESS-Plus framework, we identified shortcomings in the reporting of study participants' social determinants of health, including social capital and time-dependent relationships. CONCLUSIONS: We found two tools to support clinicians in identifying older adults' eHealth literacy. However, given the shortcomings highlighted in the validation of eHealth literacy tools in older adults, future primary research describing the diagnostic accuracy of tools for measuring eHealth literacy in this population and how social determinants of health impact the assessment of eHealth literacy is needed to strengthen tool implementation in clinical practice. PROTOCOL REGISTRATION: We registered our systematic review of the literature a priori with PROSPERO (CRD42021238365).

Single-Layer Inverted Internal Limiting Membrane Flap Versus Conventional Peel for Small- or Medium-Sized Full-Thickness Macular Holes.

PURPOSE: To analyze the outcomes of using an internal limiting membrane (ILM) flap and the conventional ILM peel technique for small- or medium-sized full-thickness macular hole (FTMH) repair. DESIGN: Retrospective, interventional case series. METHODS: Eyes with an FTMH ≤400 µm that underwent vitrectomy with a single-layer inverted ILM flap (flap group, 55 eyes) or an ILM peel (peel group, 62 eyes) were enrolled. Best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) measurements were obtained preoperatively and at 1, 3, 6, and 12 months postoperatively. RESULTS: Primary hole closure was achieved in 54 (98%) and 60 (97%) eyes in the flap and peel groups, respectively. The preoperative and postoperative 12-month BCVA values were comparable between the groups but were significantly better in the flap than in the peel group at 1 month (mean ± SD logMAR: 0.83 ± 0.43 vs 1.14 ± 0.50; P = .001), 3 months (0.58 ± 0.33 vs 0.82 ± 0.43; P = .002), and 6 months (0.56 ± 0.32 vs. 0.72 ± 0.48; P = .028). In the flap group, foveal gliosis was less common than in the peel group at 1 month (P = .030), and restored external limiting membrane and interdigitation zone was more common at 3 months (P = .046 and P < .001, respectively). CONCLUSIONS: The single-layer ILM flap and conventional ILM peel techniques both closed FTMHs and improved vision. ILM flaps were associated with better visual outcomes up to 6 months postoperatively and should be considered in FTMHs ≤400 µm.

Panel Forecasts of Country-Level Covid-19 Infections

We use dynamic panel data models to generate density forecasts for daily Covid-19 infections for a panel of countries/regions. At the core of our model is a specification that assumes that the growth rate of active infections can be represented by autoregressive fluctuations around a downward sloping deterministic trend function with a break. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous coefficients and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. According to our model, there is a lot of uncertainty about the evolution of infection rates, due to parameter uncertainty and the realization of future shocks. We find that over a one-week horizon the empirical coverage frequency of our interval forecasts is close to the nominal credible level. Weekly forecasts from our model are published at https://laurayuliu.com/covid19-panel-forecast/.

Factors associated with virtual care access in older adults: a cross-sectional study.

BACKGROUND: virtual care has been critical during the COVID-19 pandemic, but there may be inequities in accessing different virtual modalities (i.e. telephone or videoconference). OBJECTIVE: to describe patient-specific factors associated with receiving different virtual care modalities. DESIGN: cross-sectional study. SETTING AND SUBJECTS: we reviewed medical records of all patients assessed virtually in the geriatric medicine clinic at St. Michael's Hospital, Toronto, Canada, between 17 March and 13 July 2020. METHODS: we derived adjusted odds ratios (OR), risk differences (RDs) and marginal and predicted probabilities, with 95% confidence intervals, from a multivariable logistic regression model, which tested the association between having a videoconference assessment (vs. telephone) and patient age, sex, computer ability, education, frailty (Clinical Frailty Scale score), history of cognitive impairment and immigration history; language of assessment and caregiver involvement in assessment. RESULTS: our study included 330 patients (227 telephone and 103 videoconference assessments). The median population age was 83 (Q1-Q3, 76-88) and 45.2% were male. Frailty (adjusted OR 0.62, 0.45-0.85; adjusted RD -0.08, -0.09 to -0.06) and absence of a caregiver (adjusted OR 0.12, 0.06-0.24; adjusted RD -0.35, -0.43 to -0.26) were associated with lower odds of videoconference assessment. Only 32 of 98 (32.7%) patients who independently use a computer participated in videoconference assessments. CONCLUSIONS: older adults who are frail or lack a caregiver to attend assessments with them may not have equitable access to videoconference-based virtual care. Future research should evaluate interventions that support older adults in accessing videoconference assessments.

Panel forecasts of country-level Covid-19 infections.

We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our specification that assumes the growth rate of active infections can be represented by autoregressive fluctuations around a downward sloping deterministic trend function with a break. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of slopes and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. We find some evidence that information from locations with an early outbreak can sharpen forecast accuracy for late locations. There is generally a lot of uncertainty about the evolution of active infection, due to parameter and shock uncertainty, in particular before and around the peak of the infection path. Over a one-week horizon, the empirical coverage frequency of our interval forecasts is close to the nominal credible level. Weekly forecasts from our model are published at https://laurayuliu.com/covid19-panel-forecast/.