Methods: One hundred participants (50 good sleepers; 50 poor sleepers) were asked to choose between 2 written scenarios to answer 1 of 2 questions: "Which describes a better (or worse) night of sleep?". Each scenario described a self-reported experience of sleep, stringing together 17 possible determinants of sleep quality that occur at different times of the day (day before, pre-sleep, during sleep, upon waking, day after). Each participant answered 48 questions. Logistic regression models were fit to their choice data.
Results: Eleven of the 17 sleep quality parameters had a significant impact on the participants' choices. The top 3 determinants of sleep quality were: Total sleep time, feeling refreshed (upon waking), and mood (day after). Sleep quality judgments were most influenced by factors that occur during sleep, followed by feelings and activities upon waking and the day after. There was a significant interaction between wake after sleep onset and feeling refreshed (upon waking) and between feeling refreshed (upon waking) and question type (better or worse night of sleep). Type of sleeper (good vs poor sleepers) did not significantly influence the judgments.
Conclusions: Sleep quality judgments appear to be determined by not only what happened during sleep, but also what happened after the sleep period. Interventions that improve mood and functioning during the day may inadvertently also improve people's self-reported evaluation of sleep quality.
METHOD: We conducted a retrospective, cross-sectional study using administrative data from three public tertiary hospitals in Malaysia. Data for hospital admissions between 1 March 2019 and 1 March 2020 with International Classification of Diseases 10th Revision (ICD-10) codes for acute myocardial infarction (MI), ischaemic heart disease (IHD), hypertensive heart disease, stroke, heart failure, cardiomyopathy, and peripheral vascular disease (PVD) were retrieved from the Malaysian Disease Related Group (Malaysian DRG) Casemix System. Patients were stratified by T2DM status for analyses. Multivariate logistic regression was used to identify factors influencing treatment costs.
RESULTS: Of the 1,183 patients in our study cohort, approximately 60.4% had T2DM. The most common CVDE was acute MI (25.6%), followed by IHD (25.3%), hypertensive heart disease (18.9%), stroke (12.9%), heart failure (9.4%), cardiomyopathy (5.7%) and PVD (2.1%). Nearly two-thirds (62.4%) of the patients had at least one cardiovascular risk factor, with hypertension being the most prevalent (60.4%). The treatment cost for all CVDEs was RM 4.8 million and RM 3.7 million in the T2DM and non-T2DM group, respectively. IHD incurred the largest cost in both groups, constituting 30.0% and 50.0% of the total CVDE treatment cost for patients with and without T2DM, respectively. Predictors of high treatment cost included male gender, non-minority ethnicity, IHD diagnosis and moderate-to-high severity level.
CONCLUSION: This study provides real-world cost estimates for CVDE hospitalisation and quantifies the combined burden of two major non-communicable disease categories at the public health provider level. Our results confirm that CVDs are associated with substantial health utilisation in both T2DM and non-T2DM patients.