Postpartum depression is a common complication of childbearing and up to 12 months postpartum. This study aimed to determine the prevalence of postpartum depressive mood (PDM) in China by performing a meta-analysis of published studies. Studies that reported the prevalence of PDM in China were identified by searching the PubMed, Embase, CNKI, and CQVIP databases. Three thousand, one hundred, and two articles were obtained, and after careful evaluation, 26 studies were finally included in the meta-analysis. The combined studies included a total of 7618 cases with 1621 cases of PDM. The studies were assessed on the basis of heterogeneity testing and the potential for publication bias. Stata software 11.0 was used to perform the meta-analysis. The random-effect model showed that the prevalence of PDM was 21% with a 95% confidence interval (CI) of 17-25%. PDM was the highest 0 to 1.5 months after delivery. PDM levels decreased to 10.4% (95% CI 9.7-11.1%, P < 0.001) after publication bias were corrected. Sensitivity analyses evaluated the stability of our results and showed no significant change when any single study was excluded. Subgroup analyses showed that region, instruments used, cut-off score, and time points for depression assessment were positively associated with PDM prevalence. The prevalence of PDM differed among regions, with South Central China and East China exhibiting the lowest prevalence. The prevalence was higher in regions with poor economic development, suggesting that more attention should be devoted to Southwest and North China and that the prevalence of PDM should be evaluated 0 to 1.5 months after delivery.

Introduction: Self-medication with antibiotics (SMA) is common among university students in low and middle-income countries (LMICs). However, there has been no meta-analysis and systematic review in the population. Methodology: A literature search was conducted using PubMed, Embase and Web of Science for the period from January 2000 to July 2018. Only observational studies that had SMA among university students from LMICs were included. A random-effects model was applied to calculate the pooled effect size with 95% confidence interval (CI) due to the expected heterogeneity (I2 over 50%). Results: The pooled prevalence of SMA of overall included studies was 46.0% (95% CI: 40.3% to 51.8%). Africa had the highest pooled prevalence of SMA among university students (55.30%), whereas South America had the lowest prevalence (38.3%). Among individual LMICs, the prevalence of SMA among university students varied from as low as 11.1% in Brazil to 90.7% in Congo. Conclusions: The practice of SMA is a widespread phenomenon among university students in LMICs and is frequently associated with inappropriate use. Effective interventions such as medication education and stricter governmental regulation concerning antibiotic use and sale are required to be established in order to deal with SMA properly.

Aim To determine the efficacy of Internet‐based interventions in decreasing the prevalence of postpartum depression in perinatal women. Design This review was conducted according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses statement. Methods We performed a systematic meta‐analysis of randomized controlled trials on the efficacy of Internet‐based interventions for postpartum depression. Studies (2008–2018) were identified through a search conducted on PubMed, EMBASE and the Cochrane Library. Risk ratios or weighted mean differences with 95% confidence intervals were calculated using a fixed‐effects model or a random‐effects model. Stata software 11.0 was used to perform the meta‐analysis. Results Most of the seven eligible studies were randomized controlled trials. The random‐effects model indicated that Internet‐based interventions significantly improved postpartum depression (d = 0.642, N = 7). Attrition rates ranged from 4.5%–86.9% and from 0%–87.1% for the intervention and control groups, respectively.

Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of Datacenter Networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, network traffic includes long-lived (elephant) and short-lived (mice) flows with partitioned/aggregated traffic patterns. Although SDN-based approaches can efficiently allocate networking resources for such flows, the overhead due to network reconfiguration can be significant. With limited capacity of Ternary Content-Addressable Memory (TCAM) deployed in an OpenFlow enabled switch, it is crucial to determine which forwarding rules should remain in the flow table and which rules should be processed by the SDN controller in case of a table-miss on the SDN switch. This is needed in order to obtain the flow entries that satisfy the goal of reducing the long-term control plane overhead introduced between the controller and the switches. To achieve this goal, we propose a machine learning technique that utilizes two variations of Reinforcement Learning (RL) algorithms—the first of which is a traditional RL-based algorithm, while the other is deep reinforcement learning-based. Emulation results using the RL algorithm show around 60% improvement in reducing the long-term control plane overhead and around 14% improvement in the table-hit ratio compared to the Multiple Bloom Filters (MBF) method, given a fixed size flow table of 4KB.

Objective In the context of an increased focus on geriatric depression in recent years, this study examined the associations between different types of self-care disability, the number of self-care disabilities, and depressive symptoms among middle-aged and elderly Chinese people. Method The data for this study were extracted from the follow-up survey (conducted in 2018) of the China Health and Retirement Longitudinal Study (CHARLS). The sample comprised 10808 participants aged 45 years and older. The Activities of Daily Living (ADL) scale and the Center for Epidemiological Studies Depression (CESD-10) Scale were used to assess self-care disability and depressive symptoms, respectively. Result The prevalence of depressive symptoms and self-care disability among the surveyed residents was 45.1% and 23.4%, respectively. Overall, there was a significant positive association between self-care disability and depressive symptoms. Participants who reported having a self-care disability in relation dressing, bathing, transferring in and out of bed, using the toilet, and controlling urination and defecation were found to have a significantly higher risk of depressive symptoms. In addition, participants with a greater cumulative quantity of self-care disabilities had a higher risk of depressive symptoms, and higher CESD-10 scores. Conclusion Self-care disability is a risk factor for depressive symptoms among middle-aged and elderly Chinese people. A positive correlation between the number of self-care disabilities and the risk of depressive symptoms was found.

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BackgroundChina is presently facing the challenge of meeting enormous health demands because of its rapidly aging society. Enrolling older persons in eldercare institutions is a helpful alternative for relieving family caregivers and promoting healthy aging. However, changes in the living environment may negatively affect the mental health of the elderly.ObjectiveTo explore the association between different living arrangements and depressive symptoms among over-65-year-old people in China and the moderating role of outdoor activities.MethodThe 2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) used a mixed sampling method to collect the health and demographic information of 15,874 older adults over 65 years from 23 provinces in China. After considering this study's inclusion and exclusion criteria, the final sample comprised 12,200 participants. The participants' risk of depressive symptoms was assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). The potential association between the two elements was tested using a regression model.ResultThis study's findings suggested a significant relationship between depressive symptoms and living arrangements (P &lt; 0.001). Participants living alone and those living in eldercare institutions had 1.26-times (95%CI: 1.10–1.44) and 1.39-times (95%CI: 1.09–1.77) higher risks of depressive symptoms, respectively, than those living with household members. Outdoor activities play a moderating role between different living arrangements and depressive symptoms. Among participants who engaged in outdoor activities, no significant difference was observed in the risk of depressive symptoms between those living in eldercare institutions and those living with household members (adjusted odds ratio = 1.15, 95%CI = 0.81–1.64, P = 0.426).ConclusionThe high risk of depressive symptoms among older Chinese people living alone or in eldercare institutions requires considerable attention. The evidence from this study suggests that older people living alone and those living in eldercare institutions should regularly engage in appropriate outdoor activities.

Background At present, improving the accessibility to traditional Chinese medicine (TCM) health resources is an important component of China’s health policy. This study evaluated the trends in the disparities and equity of TCM health resource allocation from 2010 to 2020 to inform optimal future local health planning and policy. Method The data for this study were extracted from the China Health Statistical Yearbook (2011–2021) and China Urban Statistical Yearbook (2020). The equity and rationality of the allocation of TCM health resources at the national and provincial levels were evaluated using the Gini coefficient and the health resource aggregation degree, respectively. Result The number of TCM-related institutions, beds, health staff, outpatients and admissions increased by 1.97, 2.61, 2.35, 1.72 and 2.41 times, respectively, between 2010 and 2020. The population-based Gini coefficients for health staff, beds and institutions were 0.12, 0.23 and 0.13, respectively, indicating acceptable equity, while the geographical area-based Gini index for health staff, beds and institutions were 0.65, 0.62 and 0.62, respectively, indicating serious inequity. The agglomeration degree as a function of geographical area was as follows: eastern region > central region > western region. Moreover, the institutional and health staff gaps between the geographical areas increased from 2012 to 2020. In addition, there was a relatively balanced agglomeration degree based on the population in these three regions and an increasingly equitable allocation of institutions and health staff. Conclusion In recent years, China’s TCM health resources and services have increased rapidly, but their proportions within the overall health system remain low. The equity and rationality of TCM health allocated by the population was better than that by the geographic area. Regional differences and inequalities, especially for institutions, still exist. A series of policies to promote the balanced development of TCM need to be implemented.