The objective of this study was to evaluate the effect of institutional and policy interventions on reducing the rate of cesarean delivery on maternal request (CDMR) in Wenzhou, China. Institutional interventions included health education, painless delivery introduction, and doula care. Additionally, a series of health policies were developed by the Chinese central and local governments to control cesarean section rates, mostly through controlling CDMR rates. We conducted a pre-/post-intervention study using 131,312 deliveries between 2006 and 2014 in three tertiary-level public hospitals in Wenzhou, China. Chi-square tests and predictive models were used to examine changes in the CDMR rate before and after institutional and policy interventions. After institutional interventions were introduced, the overall CDMR rate increased from 15.76% to 16.34% (p = 0.053), but the average annual growth rate (AAGR) of the overall CDMR rate quickly declined from 20.11% to -4.30%. After policy interventions were introduced, the overall CDMR rate, the AAGR of the overall CDMR rate, and the probability of performing CDMR declined. Further, the overall probability of a woman undergoing CDMR decreased in all three age groups (group one: <24; group two: 24–34; group three: >34) after institutional and policy interventions. These results show that institutional and policy interventions can reduce the CDMR rate. Additionally, the CDMR rate should be included in hospitals’ performance assessment matrix to reduce the CDMR rate further.

Few studies have assessed the impact of home and community-based services (HCBSs) provision on cognitive function among older adults over time. This study examined the longitudinal association between HCBSs provision and cognitive function in Chinese older adults. The study included 5,134 participants aged 65 years and older in the Chinese Longitudinal Healthy Longevity Survey from 2008 to 2014. The Mini-Mental State Examination (MMSE) was used to evaluate cognitive function over

Background Self-rated health (SRH) is a good predictor of morbidity and mortality. Extensive research has shown that females generally report poorer SRH than males but still tend to live longer. Previous studies used cross-sectional or pooled data for their analyses while ignoring the dynamic changes in males’ and females’ SRH statuses over time. Furthermore, longitudinal studies, especially those that focus on older adults, typically suffer from the incompleteness of data. As such, the effect of dropout data on the trajectories of SRH is still unknown. Our objective is to examine whether there are any gender differences in the trajectories of SRH statuses in Chinese older adults. Methods The trajectories of SRH were estimated using the pattern-mixture model (PMM), a special latent growth model, under non-ignorable dropout data assumption. We analyzed the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data of 15,613 older adults aged 65 years and above, collected from 2005 to 2014. Results The results demonstrated the effect of non-ignorable dropout data assumptions in this study. The previous SRH score was negatively associated with the likelihood of dropping out of the study at the next follow-up survey. Our results showed that both males and females in China perceive their SRH as decreasing over time. A significant gender difference was found in the average SRH score (female SRH was lower than male SRH) in this study. Nonetheless, based on the results obtained using the PMM, there are no gender differences in the trajectories of SRH at baseline as well as in the rate of decline among the total sample. The results also show that males and females respond to SRH predictors similarly, except that current drinking has a more pronounced positive effect on males and healthcare accessibility has a more pronounced positive effect on females. Conclusions Our results suggest that missing data have an impact on the trajectory of SRH among Chinese older adults. Under the non-ignorable dropout data assumptions, no gender differences were found in trajectories of SRH among Chinese older adults. Males and females respond to SRH predictors similarly, except for current drinking habit and healthcare accessibility.

Background The increasing trend of Caesarean section (CS) in childbirth has become a global public health challenge. Previous studies have proposed financial intervention strategies for reducing CS rates by limiting caesarean delivery on maternal request (CDMR). This study synthesizes such strategies while evaluating their effectiveness. Methods The sources of data for this study are Cochrane Library, PubMed, EMBASE, and CINAHL. The publication period included in this study is from January 1991 to November 2018. The financial intervention strategies are divide into two categories: healthcare provider interventions and patient interventions. Risk of Bias in Non-randomized Studies - of Interventions (ROBINS-I) was employed to assess the risk of bias of included studies. The outcome of each study was evaluated with Grades of Recommendation, Assessment, Development and Evaluation (GRADE) through the GRADEpro Guideline Development Tool software. Results Nine studies were included in this systematic review: five with high certainty evidence (HCE), three with moderate certainty evidence (MCE), and one with low certainty evidence (LCE). Of the nine studies, seven are centered on the effect of provider-side interventions. Three of the HCE studies found that the diagnosis-related group payment system, risk-adjusted capitation, and equalizing fee for both facilities and physicians were effective intervention strategies. One HCE and one MCE study showed that only equalizing facility fees between vaginal and CS deliveries in healthcare service settings had no significant effect on reducing the CS rate. The MCE study showed that case payment had a negative effect on reducing the CS rates. One LCE study revealed that the effect of a global budget system was uncertain, and one HCE and one MCE study focused on combining both provider and patient-side interventions. However, equalizing fees for vaginal and CS deliveries and a co-payment policy for CDMRs failed to reduce the CS rate. Conclusions The effectiveness of risk-adjusted payment methods appears promising and should be the subject of further research. Financial interventions should consider stakeholders’ characteristics, especially the personal interests of doctors. Finally, high-quality randomized control trials and comparative studies on different financial intervention methods are needed to confirm or refute previous studies’ outcomes. Electronic supplementary material The online version of this article (10.1186/s12889-019-7265-4) contains supplementary material, which is available to authorized users.

Currently, statins are the first-line therapies for dyslipidemia and atherosclerotic cardiovascular disease, however, their hypolipidemic effects have not been satisfactory. We performed a meta-analysis to compare lipid-lowering efficacy and safety of ezetimibe and statin combination therapy with double-dose statin monotherapy in patients with high cardiovascular risk. Fourteen studies involving 3,105 participants were included in the final analysis; 1,558 (50.18%) participants received ezetimibe and statin combination therapy and 1,547 (49.82%) received double-dose statin monotherapy. Eight studies reported the percentages of changes in several lipid parameters from baseline to endpoint in both groups. Lipid parameters changed more significantly in patients co-administered with ezetimibe and statin (low-density lipoprotein cholesterol [LDL-C]: MD = -9.39, 95% CI -13.36 to -5.42; non-high-density lipoprotein cholesterol [non-HDL-C]: MD = -10.36, 95% CI -14.23 to -6.50; total cholesterol [TC]: MD = -8.11, 95% CI -10.95 to -5.26; and triglyceride [TG]: MD = -5.96, 95% CI -9.12 to -2.80), with moderate to high heterogeneity among the studies. Two out of fourteen studies investigated several different statins. Our subgroup analysis showed that, compared with double-dose atorvastatin monotherapy, ezetimibe and atorvastatin combination therapy significantly decreased LDL-C, non-HDL-C, TC, and TG levels by 14.16%, 14.01%, 11.06%, and 5.96%, respectively (p < 0.001). No significant difference was found in the incidence of laboratory-related adverse events (AEs) between statin combination therapy and monotherapy. Overall, ezetimibe and statin combination therapy significantly decreased LDL-C, non-HDL-C, and TC levels in patients with high cardiovascular risk, among which ezetimibe combined with atorvastatin had the best therapeutic effect. Compared with ezetimibe and statin combination therapy, double-dose statin monotherapy did not increase the risk of AEs.

Background Long-term care facilities (LTCFs) were high-risk settings for COVID-19 outbreaks. Objective To assess the impacts of the COVID-19 pandemic on LTCFs, including rates of infection, hospitalisation, case fatality, and mortality, and to determine the association between control measures and SARS-CoV-2 infection rates in residents and staff. Method We conducted a systematic search of six databases for articles published between December 2019 and 5 November 2021, and performed meta-analyses and subgroup analyses to identify the impact of COVID-19 on LTCFs and the association between control measures and infection rate. Results We included 108 studies from 19 countries. These studies included 1,902,044 residents and 255,498 staff from 81,572 LTCFs, among whom 296,024 residents and 36,807 staff were confirmed SARS-CoV-2 positive. The pooled infection rate was 32.63% (95%CI: 30.29 ~ 34.96%) for residents, whereas it was 10.33% (95%CI: 9.46 ~ 11.21%) for staff. In LTCFs that cancelled visits, new patient admissions, communal dining and group activities, and vaccinations, infection rates in residents and staff were lower than the global rate. We reported the residents’ hospitalisation rate to be 29.09% (95%CI: 25.73 ~ 32.46%), with a case-fatality rate of 22.71% (95%CI: 21.31 ~ 24.11%) and mortality rate of 15.81% (95%CI: 14.32 ~ 17.30%). Significant publication biases were observed in the residents’ case-fatality rate and the staff infection rate, but not in the infection, hospitalisation, or mortality rate of residents. Conclusion SARS-CoV-2 infection rates would be very high among LTCF residents and staff without appropriate control measures. Cancelling visits, communal dining and group activities, restricting new admissions, and increasing vaccination would significantly reduce the infection rates.

China is experiencing rapid population ageing: in 2019, the proportion of older adults aged 65 or older was 11.9%, and over 40 million elderly adults were categorised as disabled (Bureau of Medical Administration, 2020), resulting in an increase in the demand for long-term care. In China and many other East Asian countries, older adults previously relied on their children to provide such care (Leung, 2008), but as the implementation of the one-child policy and rapid socioeconomic transformation resulted in the family structure changing to "4-2-1" (a family consisting of four grandparents, two parents, and a single child), an increasing number of Chinese older adults now live alone, and family members, particularly their children, lack the capacity to meet the growing need for elderly care (Yang et al., 2021;Zhu & Osterle, 2019). Therefore, home-and community-based services (HCBSs) have expanded and

Object detection assumes that the training data is identical to the testing data. However, the distributions of training and testing data, in practice, are different, thereby limiting the detection accuracy of objects. To solve this problem, recent works adopt domain adaptation techniques to reduce the domain discrepancy. In this paper, we present a novel deep neural network design for domain adaptive object detection by further improving the localization accuracy of objects. First, we present to refine the pseudo labels generated from the current object detection methods and use these labels with a weighted loss function to train the network on the target domain. Second, we insert several residual blocks into the shallow layers of a convolutional neural network used in the target domain to enhance detailed spatial information, which helps for object localization. We perform various experiments to evaluate our network on three widely-used public benchmark datasets for domain adaptive object detection. The experimental results show that our DALocNet performs favorably against the state-of-the-art methods on all the datasets quantitatively and qualitatively. INDEX TERMS Domain adaptation, object detection, object localization, deep neural network.