The relationship between sleep health and multimorbidity in community dwelling populations: Systematic review and global perspectives.

Previous research has identified a relationship between sleep problems and multimorbidity, defined as the co-occurrence of two or more chronic health conditions in the same individual. This systematic review sought to summarize the literature on the association between sleep duration and quality and multimorbidity in adult community-dwelling populations. A comprehensive search of the PubMed, Embase and CINAHL databases identified studies published between January 1990 and January 2023. Studies were included if they focused on community-dwelling populations, used an observational design, measured sleep quality or duration, used multimorbidity as the main study outcome, and explored the relationship between sleep and multimorbidity. Two reviewers independently conducted study screening, data extraction, and bias assessments. Twenty-four cross-sectional and five prospective cohort studies met the inclusion criteria, with studies from 16 countries and two with cross-country comparisons, and a total participant number of 481,862. Overall, poorer sleep quality and sleep duration outside current recommendations were consistently associated with multimorbidity, though with varying strength. This association was present regardless of specific multimorbidity definitions from different studies. Definitions of sleep duration and quality were inconsistent across studies, possibly contributing to mixed evidence on the observed association. Most studies were cross-sectional, limiting the assessment of the temporal direction of association. Our results corroborate relationships between poor sleep and risk of multimorbidity in adult community-dwelling populations around the world. Examining this relationship may help increase public health awareness of sleep as a modifiable risk factor for the prevention of chronic disease and healthy aging.

Protocol for a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence for grading of ophthalmology imaging modalities.

BACKGROUND: With the rise of artificial intelligence (AI) in ophthalmology, the need to define its diagnostic accuracy is increasingly important. The review aims to elucidate the diagnostic accuracy of AI algorithms in screening for all ophthalmic conditions in patient care settings that involve digital imaging modalities, using the reference standard of human graders. METHODS: This is a systematic review and meta-analysis. A literature search will be conducted on Ovid MEDLINE, Ovid EMBASE, and Wiley Cochrane CENTRAL from January 1, 2000, to December 20, 2021. Studies will be selected via screening the titles and abstracts, followed by full-text screening. Articles that compare the results of AI-graded ophthalmic images with results from human graders as a reference standard will be included; articles that do not will be excluded. The systematic review software DistillerSR will be used to automate part of the screening process as an adjunct to human reviewers. After the full-text screening, data will be extracted from each study via the categories of study characteristics, patient information, AI methods, intervention, and outcomes. Risk of bias will be scored using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) by two trained independent reviewers. Disagreements at any step will be addressed by a third adjudicator. The study results will include summary receiver operating characteristic (sROC) curve plots as well as pooled sensitivity and specificity of artificial intelligence for detection of any ophthalmic conditions based on imaging modalities compared to the reference standard. Statistics will be calculated in the R statistical software. DISCUSSION: This study will provide novel insights into the diagnostic accuracy of AI in new domains of ophthalmology that have not been previously studied. The protocol also outlines the use of an AI-based software to assist in article screening, which may serve as a reference for improving the efficiency and accuracy of future large systematic reviews. TRIAL REGISTRATION: PROSPERO, CRD42021274441.

Disparities in Physician Compensation by Gender in Ontario, Canada.

Importance: Men and women should earn equal pay for equal work. An examination of the magnitude of pay disparities could inform strategies for remediation. Objective: To examine gender-based differences in pay within a large, comprehensive physician population practicing within a variety of payment systems. Design, Setting, and Participants: This cross-sectional study used data from the Ontario Health Insurance Plan (OHIP) in the 2017 to 2018 fiscal year to estimate differences in gross payments between men and women physicians in Ontario, Canada. Pay gaps were calculated annually and daily. Regression analyses were used to control for observable practice characteristics that could account for individual differences in daily pay. In Canada's largest province, Ontario, medical services are predominantly provided by self-employed physicians who bill the province's single payer, OHIP. All physicians who submitted claims to OHIP were included. Data were analyzed from January 2020 to July 2021. Exposures: Physician gender, obtained from the OHIP Corporate Provider Database. Gender is recorded as male or female. Main Outcomes and Measures: Gross clinical payments were tabulated for individual physicians on a daily and annual basis in conjunction with each physician's practice characteristics, setting, and specialty. Results: A total of 31 481 physicians were included in the study sample (12 604 [40.0%] women; 18 877 [60.0%] men; mean [SD] time since graduation, 23.3 [13.6] years), representing 99% of active physicians in Ontario. The unadjusted differences in clinical payments between male and female physicians were 32.8% (95% CI, 30.8%-34.6%) annually and 22.5% (95% CI, 21.2%-23.8%) daily. After accounting for practice characteristics, region, and specialty, the overall daily payment gap was 13.5% (95% CI, 12.3%-14.8%). The pay gap persisted with differing magnitudes when examined by specialty (ranging from 6.6% to 37.6%), practice setting (8.3% to 17.2%), payment model (13.4% to 22.8% for family medicine; 8.0% to 11.6% for other specialties), and rurality (8.0% to 16.5%). Conclusions and Relevance: This cross-sectional study examined differences in magnitude of annual and daily payment gaps and between unadjusted and adjusted gaps. Comparing the gaps for different specialties, geography, and payment systems illustrated the complexity of the issue by showing that the pay gap varied for physicians in different practice settings. As such, multiple directed interventions will be necessary to ensure that all physicians are paid equally for equal work, regardless of gender.