Effects of Marital Status and Income on Hypertension: The Korean Genome and Epidemiology Study (KoGES)

Objectives: This study aimed to analyze the associations of income, marital status, and health behaviors with hypertension in male and female over 40 years of age in the Korea. Methods: The data were derived from the Korean Genome and Epidemiology Study (KoGES; 4851-302) which included 211 576 participants. To analyze the relationships of income, marital status, and health behaviors with hypertension in male and female over 40 years of age, multiple logistic regression was conducted with adjustments for these variables. Results: The prevalence of hypertension increased linearly as income decreased. The odds ratio for developing hypertension in people with an income of <0.5 million Korean won (KRW) compared to ≥6.0 million KRW was 1.55 (95% confidence interval [CI], 1.25 to 1.93) in the total population, 1.58 (95% CI, 1.27 to 1.98) in male, and 1.07 (95% CI, 0.35 to 3.28) in female. The combined effect of income level and marital status on hypertension was significant. According to income level and marital status, in male, low income and divorce were most associated with hypertension (1.76 times; 95% CI, 1.01 to 3.08). However, in female, the low-income, married group was most associated with hypertension (1.83 times; 95% CI, 1.71 to 1.97). Conclusions: The results of this study show that it is necessary to approach male and female marital status separately according to income in health policies to address inequalities in the prevalence of hypertension

Large-scale learning of generalised representations for speaker recognition

The objective of this work is to develop a speaker recognition model to be used in diverse scenarios. We hypothesise that two components should be adequately configured to build such a model. First, adequate architecture would be required. We explore several recent state-of-the-art models, including ECAPA-TDNN and MFA-Conformer, as well as other baselines. Second, a massive amount of data would be required. We investigate several new training data configurations combining a few existing datasets. The most extensive configuration includes over 87k speakers' 10.22k hours of speech. Four evaluation protocols are adopted to measure how the trained model performs in diverse scenarios. Through experiments, we find that MFA-Conformer with the least inductive bias generalises the best. We also show that training with proposed large data configurations gives better performance. A boost in generalisation is observed, where the average performance on four evaluation protocols improves by more than 20%. In addition, we also demonstrate that these models' performances can improve even further when increasing capacity.Comment: 5pages, 5 tables, submitted to ICASS