To identify factors driving the rapid increase in caesarean section in China between 1988 and 2008. Data from four national cross-sectional surveys (1993, 1998, 2003 and 2008) and modified Poisson regression were used to determine whether changes in household income, access to health insurance or women's education accounted for the rise in caesarean sections in urban and rural areas. In 2008, 64.1% of urban women and 11.3% of women in the poorest rural region reported giving birth by caesarean section. A fast rise was occurring in all socioeconomic groups. Between 1993 and 2008, the risk of caesarean section had increased more than threefold in urban areas (relative risk, RR: 3.63; 95% confidence interval, CI: 2.61-5.04) and more than 15-fold in rural areas (RR: 15.46; 95% CI: 10.46-22.86). After adjustment for improvements in income, education and access to health insurance over the study period, the RR dropped minimally in urban areas (RR: 3.07; 95% CI: 2.32-4.07), which suggests that these factors do not explain the rise; in rural areas, the adjusted RR dropped to 7.18 (95% CI: 4.82-10.69), which shows that socioeconomic change is only partly responsible for the rise. Socioeconomic region of residence was a more important driver of the caesarean section rate than individual socioeconomic status. The large variation in caesarean section rate by socioeconomic region--independent of individual income, health insurance or education--suggests that structural factors related to service supply have influenced the increasing rate more than a woman's ability to pay.

To monitor hypertension prevalence, awareness, treatment and control in China two to three years after major reform of the health system. Data from a national survey conducted in 2011-2012 among Chinese people aged 45 years or older - which included detailed anthropometric measurements - were used to estimate the prevalence of hypertension and the percentages of hypertensive individuals who were unaware of, receiving no treatment for, and/or not controlling their hypertension well. Modified Poisson regressions were used to estimate relative risks (RRs). At the time of the survey, nearly 40% of Chinese people aged 45 years or older had a hypertensive disorder. Of the individuals with hypertension, more than 40% were unaware of their condition, about 50% were receiving no medication for it and about 80% were not controlling it well. Compared with the other hypertensive individuals, those who were members of insurance schemes that covered the costs of outpatient care were more likely to be aware of their hypertension (adjusted RR, aRR: 0.737; 95% confidence interval, CI: 0.619-0.878) to be receiving treatment for it (aRR: 0.795; 95% CI: 0.680-0.929) and to be controlling it effectively (aRR: 0.903; 95% CI: 0.817-0.996). In China many cases of hypertension are going undetected and untreated, even though the health system appears to deliver effective care to individuals who are aware of their hypertension. A reduction in the costs of outpatient care to patients would probably improve the management of hypertension in China.

The COVID-19 pandemic might lead to more mental health problems. However, few studies have examined sleep problems, depression, and posttraumatic symptoms among the general adult population during the COVID-19 outbreak, and little is known about coping behaviors. This survey was conducted online in China from February 1st to February 10th, 2020. Quota sampling was used to recruit 2993 Chinese citizens aged ≥18 years old. Mental health problems were assessed with the Post-Traumatic Stress Disorders (PTSD) Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), the Center for Epidemiological Studies Depression inventory, and the Pittsburgh Sleep Quality Index. Exposure to COVID-19 was measured with questions about residence at outbreak, personal exposure, media exposure, and impact on livelihood. General coping style was measured by the brief Coping Style Questionnaire (SCSQ). Respondents were also asked 12 additional questions about COVID-19 specific coping behaviors. Direct exposure to COVID-19 instead of the specific location of (temporary) residence within or outside the epicenter (Wuhan) of the pandemic seems important (standardized beta: 0.05, 95% confidence interval (CI): 0.02–0.09). Less mental health problems were also associated with less intense exposure through the media (standardized beta: −0.07, 95% CI: −0.10–−0.03). Perceived negative impact of the pandemic on livelihood showed a large effect size in predicting mental health problems (standardized beta: 0.15, 95% CI: 0.10–0.19). More use of cognitive and prosocial coping behaviors were associated with less mental health problems (standardized beta: −0.30, 95% CI: −0.34–−0.27). Our study suggests that the mental health consequences of the lockdown impact on livelihood should not be underestimated. Building on cognitive coping behaviors reappraisal or cognitive behavioral treatments may be most promising.

Background: Detection of depression gained prominence soon after this troublesome disease emerged as a serious public health concern worldwide. Objective: This systematic review aims to summarize the findings of previous studies concerning applying machine learning (ML) methods to text data from social media to detect depressive symptoms and to suggest directions for future research in this area. Methods: A bibliographic search was conducted for the period of January 1990 to December 2020 in Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed. Two reviewers retrieved and independently assessed the 418 studies consisting of 322 articles identified through database searching and 96 articles identified through other sources; 17 of the studies met the criteria for inclusion. Results: Of the 17 studies, 10 had identified depression based on researcher-inferred mental status, 5 had identified it based on users’ own descriptions of their mental status, and 2 were identified based on community membership. The ML approaches of 13 of the 17 studies were supervised learning approaches, while 3 used unsupervised learning approaches; the remaining 1 study did not describe its ML approach. Challenges in areas such as sampling, optimization of approaches to prediction and their features, generalizability, privacy, and other ethical issues call for further research. Conclusions: ML approaches applied to text data from users on social media can work effectively in depression detection and could serve as complementary tools in public mental health practice.