Inequity in Health Services Utilization in Economically Underdeveloped Regions of Northeast China.

Background: The Chinese health system has long been committed to eliminating inequalities in health services utilization. However, few studies have analyzed or measured these inequalities in economically underdeveloped regions in China. Methods: A total of 6,627 respondents from 3,000 households in Heilongjiang Province were extracted from the Sixth National Health Services Survey. We measured horizontal inequity in both 2-week outpatient rate and annual inpatient rate, and then identified the factors contributing to inequality. Results: The horizontal inequity indices of the 2-week outpatient and annual impatient rates in Heilongjiang Province were 0.0586 and 0.1276, respectively. Household income, health status, place of residence, basic medical insurance, and commercial health insurance were found to be the main factors affecting inequality in health services utilization. The contributions of household income to these two indices were 184.03 and 253.47%, respectively. Health status factors, including suffering from chronic disease, limitations in daily activities, and poor self-rated health, played positive roles in reducing inequality in these two indices. The contributions of place of residence to these two indices were 27.21 and -28.45%, respectively. Urban Employee Basic Medical Insurance made a pro-rich contribution to these two indices: 56.25 and 81.48%, respectively. Urban and Rural Resident Basic Medical Insurance, Urban Resident Basic Medical Insurance, New Rural Cooperative Medical Scheme, and other basic medical insurance made a pro-poor contribution to these two indices: -73.51 and -54.87%, respectively. Commercial health insurance made a pro-rich contribution to these two indices: 20.79 and 7.40%, respectively. Meanwhile, critical illness insurance made a slightly pro-poor contribution to these two indices: -4.60 and -0.90%, respectively. Conclusions: The findings showed that the "equal treatment in equal need" principle was not met in the health services utilization context in Heilongjiang Province. To address this issue, the government could make policy changes to protect low-income populations from underused health services, and work to improve basic medical insurance, critical illness insurance, and social security systems.

Perceived Academic Stress and Depression: The Mediation Role of Mobile Phone Addiction and Sleep Quality.

BACKGROUND: Although academic stress is a well-known risk factor for students' depression, little is known about the possible psychological mechanisms underlying this association. In this study, we investigated the prevalence of depression and sleep disturbance among Chinese students, examined the relationship between perceived academic stress and depression, considered if mobile phone addiction and sleep quality is a mediator of this relationship, and tested if mobile phone addiction and sleep quality together play a serial mediating role in the influence of perceived academic stress on depression. METHOD: A cross-sectional survey was conducted among students from September to December 2018 in Heilongjiang Province, China. The final analysis included 5,109 students. Mobile phone addiction, sleep quality, and depressive symptoms were assessed using the Mobile Phone Addiction Index, Pittsburgh Sleep Quality Index, and Center for Epidemiologic Studies-Depression scales, respectively. The serial mediation model was used to analyse the relationship between perceived academic stress, mobile phone addiction, sleep quality, and depression. RESULTS: Among all participants, the prevalence of depressive symptoms and sleep disturbance was 28.69 and 27.95%, respectively. High school students showed the highest scores of perceived academic stress (2.68 ± 1.06), and the highest prevalence of depressive symptoms (33.14%) and sleep disturbance (36.47%). The serial mediation model indicated that perceived academic stress was a significant predictor of depression (B = 0.10, SE = 0.02, 95% CI = 0.06 - 0.13). Additionally, mobile phone addiction (B = 0.08, 95% boot CI = 0.06-0.11) and sleep quality (B = 0.27, 95% boot CI = 0.22-0.33) played a mediating role between perceived academic stress and depression. Mobile phone addiction and sleep quality together played a serial mediating role in the influence of perceived academic stress on depression (B = 0.11, 95% boot CI = 0.08-0.14). Furthermore, the indirect effect (i.e., the mediating effect of mobile phone addiction and sleep quality) was significant and accounted for 64.01% of the total effect. CONCLUSIONS: Our research results underscore the need for stakeholders-including family members, educators, and policy makers-to take preventative intervention measures to address depression among Chinese students, especially high school students.

Sleep Quality of Students from Elementary School to University: A Cross-Sectional Study.

BACKGROUND: Sleep affects a wide array of health outcomes and is associated with the quality of life. Among students, sleep quality is affected by school stage and grade; however, data regarding the different sleep-related problems students experience at different school stages are limited. In this study, we aimed to explore sleep quality among a student sample ranging from elementary school to university level. METHODS: Overall, data were examined for 9392 subjects aged 9-22 years. Information on sociodemographic characteristics and other variables were collected through self-administered questionnaires. Sleep quality on school nights was evaluated using the standard Pittsburgh Sleep Quality Index; global score >5 was classified as poor sleep quality. For the high school sample, logistic regression analysis was used to estimate associations between sleep quality and certain factors. RESULTS: Of the elementary school, middle school, vocational high school, senior high school, and university students, 7.5%, 19.2%, 28.6%, 41.9%, and 28.5%, respectively, showed poor sleep quality. The high school students reported the highest prevalence of shorter sleep duration (70.8%), day dysfunction (84.7%), and subjective poor sleep quality (17.2%). The elementary school students showed the highest prevalence of poor sleep efficiency (17.9%). The university students showed the highest prevalence of sleep medication use (6.4%). The vocational high school students reported the highest prevalence of sleep latency (6.3%) and sleep disturbance (7.4%). Logistic regression modeling indicated that sleep quality is positively associated with school stage, grade, family atmosphere, academic pressure, and number of friends. CONCLUSION: Sleep quality and sleep features change greatly from elementary school to university. Interventions to improve sleep quality should consider targeting the specific issues students experience at each school stage. Alarmed by the high prevalence of poor sleep quality among high school students, it is recommended that high school students should be informed of their sleep matter and the consequences.