Investigating the Self-Reported Health Status of Domestic and Overseas Chinese Populations during the COVID-19 Pandemic

Validity of self-reported height, weight and BMI as applied to trends in malnutrition in Davao City, Philippines

BACKGROUND

Self-reported height, weight and body mass index (BMI) data are widely used to monitor trends in malnutrition. However, several studies expressed concerns about its reliability-citing trends of over-reporting and underreporting anthropometric data. This study aims to: (1) identify the validity of self-reported height and weight and BMI as compared with measured values and (2) examine the potential recurrence of malnutrition in an urban-based population.

METHODS

Paired t-tests and Pearson's correlation coefficients were conducted to identify potential discrepancies between self-reported and measured anthropometric data. These values were collected among 255 male and 400 female participants in the Davao City.

RESULTS

Height overestimation in females and underestimation in males were observed to be statistically significant (P < 0.05). Researchers also note an alarming rise in malnutrition cases when the Asia-Pacific Index was applied to the BMI study data. A 40.79 and 22% increase in obese cases among male and female respondents were recorded.

CONCLUSION

Modifying participant-gathered height and weight values is likely to result in discrepancies between self-reported and measured values. Identifying a person's height and weight status is crucial to understanding who among the population experience malnutrition. Thus, policymakers are called to strengthen educational support that trains respondents to report reliable and valid health data.

Assessment of Perceived Health Status and Access to Health Service during the COVID-19 Pandemic: Cross-Sectional Survey in Italy

(1) Background: The aims of this survey were to assess the perceived health status and to evaluate the use of healthcare services during the pandemic period. (2) Methods: This cross-sectional survey was conducted from May to October 2021 in the Campania and Calabria regions, Southern Italy. The sample was selected among 655 subjects attending vaccination or primary care physician clinics. (3) Results: More than half (57.2%) of the respondents were female, the mean age was 49 years (range 19-97), and 61.3% had at least one chronic disease. Among the respondents, 56.3% declared that they had accessed healthcare at least once during the pandemic and, among all respondents who did not access healthcare, 23.2% gave reasons related to the COVID-19 pandemic. The two scores obtained from the Short-Form-12 Health Survey (SF-12), physical health summary (PCS) and mental health summary (MCS), had a means of 48.4 and 45.9, respectively. Among the respondents, 2.3% of respondents considered their health poor, 43.1% good and 6.4% excellent. (4) Conclusions: Our results suggest the need to ensure, in similar health emergency situations, a quick response from the National Health System so that ordinary medical assistance activities can be guaranteed in full safety, avoiding the risk of missed access or lack of assistance.

Self-reported health-related quality of life of the general population in Alberta, Canada during the COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic has impacted various aspects of people's life and wellbeing around the world. This study aimed to examine the impact of the COVID-19 pandemic on health-related quality of life (HRQL), measured by the EQ-5D-5L, amongst the general population in the province of Alberta, Canada, and explore whether the impact varied across population subgroups based on age, gender, and dwelling.

METHODS

Data came from two waves of a repeated cross-sectional population-based survey, the COVID-19 Experiences and Impact Survey, administered by the Health Quality Council of Alberta. The first data collection (survey 1: n = 8790) was during May/June 2020 and the second (survey 2: n = 9263) during Oct 2020. We examined the comparability of weighted survey data and their representativeness to Alberta's general population. We then explored between-survey differences in EQ-5D-5L index, EQ-VAS and dimension responses, and differences across subgroups within each survey. We compared HRQL of the pooled sample (survey 1&2) with the Alberta population norms data from the pre-pandemic period.

RESULTS

Mean EQ-5D-5L index and EQ-VAS scores were 0.81 (0.15) and 72.54 (18.57), and 0.82 (0.14) and 71.98 (18.96) in surveys 1 and 2, respectively. The anxiety/depression dimension had the most reported problems (survey 1: 69.5%, survey 2: 70.2%). Respondents aged 16-24 or 75 and older, who identified themselves as a woman, or residing in urban areas had significantly lower EQ-5D-5L index scores compared to their counterparts in both surveys. Between-survey differences were not substantially different. Comparing the pooled sample with the pre-pandemic Alberta population norms, EQ-5D-5L index scores (0.82 vs. 0.84) and EQ-VAS scores (72.26 vs. 77.40) were significantly lower, and respondents aged 16-44, women, or urban residents were more impacted. More problems were reported in the anxiety/depression (69.9% vs. 37.2%) and usual activities dimensions (40.5% vs. 26.0%) during the pandemic period, especially for respondents aged 16-44, women, and those residing in urban areas.

CONCLUSIONS

Lower HRQL was reported during the COVID-19 pandemic compared to pre-pandemic HRQL in this population, with anxiety/depression and usual activities affected the most. People who were younger, women, and residing in urban areas were most impacted. The government responses to COVID-19 policies during population outbreaks should consider the needs of Albertans in these particular groups.

A lexicon-based approach to examine depression detection in social media: the case of Twitter and university community

Globally, the number of people who suffer from depression is consistently increasing. Because both detecting and addressing the early stage of depression is one of the strongest factors for effective treatment, a number of scholars have attempted to examine how to detect and address early-stage depression. Recent studies have been focusing on the use of social media for depression detection where users express their thoughts and emotions freely. With this trend, we examine two-step approaches for early-stage depression detection. First, we propose a depression post-classification model using multiple languages Twitter datasets (Korean, English, and Japanese) to improve the applicability of the proposed model. Moreover, we built a depression lexicon for each language, which mental health experts verified. Then, we applied the proposed model to a more specific user group dataset, a community of university students (Everytime), to examine whether the model can be employed to address depression posts in more specific user groups. The classification results present that the proposed model and approach can effectively detect depression posts of a general user group (Twitter), as well as specific user group datasets. Moreover, the implemented models and datasets are publicly available.