INDEPTH Network Research Articles

Randomised comparison of two household survey modules for measuring stillbirths and neonatal deaths in five countries: the Every Newborn-INDEPTH study

An estimated 5·1 million stillbirths and neonatal deaths occur annually. Household surveys, most notably the Demographic and Health Survey (DHS), run in more than 90 countries and are the main data source from the highest burden regions, but data-quality concerns remain. We aimed to compare two questionnaires: a full birth history module with additional questions on pregnancy losses (FBH+; the current DHS standard) and a full pregnancy history module (FPH), which collects information on all livebirths, stillbirths, miscarriages, and neonatal deaths. Women residing in five Health and Demographic Surveillance System sites within the INDEPTH Network (Bandim in Guinea-Bissau, Dabat in Ethiopia, IgangaMayuge in Uganda, Matlab in Bangladesh, and Kintampo in Ghana) were randomly assigned (individually) to be interviewed using either FBH+ or FPH between July 28, 2017, and Aug 13, 2018. The primary outcomes were stillbirths and neonatal deaths in the 5 years before the survey interview (measured by stillbirth rate [SBR] and neonatal mortality rate [NMR]) and mean time taken to complete the maternity history section of the questionnaire. We also assessed between-site heterogeneity. This study is registered with the Research Registry, 4720. 69 176 women were allocated to be interviewed by either FBH+ (n=34 805) or FPH (n=34 371). The mean time taken to complete FPH (10·5 min) was longer than for FBH+ (9·1 min; p<0·0001). Using FPH, the estimated SBR was 17·4 per 1000 total births, 21% (95% CI -10 to 62) higher than with FBH+ (15·2 per 1000 total births; p=0·20) in the 5 years preceding the survey interview. There was strong evidence of between-site heterogeneity (I2=80·9%; p<0·0001), with SBR higher for FPH than for FBH+ in four of five sites. The estimated NMR did not differ between modules (FPH 25·1 per 1000 livebirths vs FBH+ 25·4 per 1000 livebirths), with no evidence of between-site heterogeneity (I2=0·7%; p=0·40). FPH takes an average of 1·4 min longer to complete than does FBH+, but has the potential to increase reporting of stillbirths in high burden contexts. The between-site heterogeneity we found might reflect variations in interviewer training and survey implementation, emphasising the importance of interviewer skills, training, and consistent implementation in data quality. Children's Investment Fund Foundation.

Diterpene Ginkgolides Exert an Antidepressant Effect Through the NT3-TrkA and Ras-MAPK Pathways.

BackgroundDepression is a highly prevalent mental illness that severely impacts the quality of life of affected individuals. Our recent studies demonstrated that diterpene ginkgolides (DG) have antidepressant effects in mice. However, the underlying molecular mechanisms remained much unclear.MethodsIn this study, we assessed the antidepressant effects of chronic DG therapy in rats by evaluating depression-related behaviors, we also examined potential side effects using biochemical indicators. Furthermore, we performed an in-depth molecular network analysis of gene–protein–metabolite interactions on the basis of metabolomics.ResultsChronic DG treatment significantly ameliorated the depressive-like behavioral phenotype. Furthermore, the neurotrophin signaling-related NT3-TrkA and Ras-MAPK pathways may play an important role in the antidepressant effect of DG in the hippocampus.ConclusionThese findings provide novel insight into the mechanisms underlying the antidepressant action of DG, and should help advance the development of new therapeutic strategies for depression.

Practice of China's New High-level University of Science and Engineering’s International Development: A Case Study of Dongguan University of Technology

Higher education’ internationalization connects well with the Belt and Road Initiative and Guangdong-Hong Kong-Macao Greater Bay Area construction. New type of high-level university of science and engineering’ international development is of great significance. Take Dongguan University of Technology as an example and analyze its strategy and practice. We find DGUT constructed an extensive and in-depth network of international cooperation, pushed forward multiple, characteristic and cross-border cooperation in running universities and colleges, created disciplines and subjects which have prominent advantages, attached importance to both external and internal training, enhanced internationalization ability of teachers and management teams.

Enhancing fieldworkers’ performance management support in health research: an exploratory study on the views of field managers and fieldworkers from major research centres in Africa

IntroductionFieldworkers are part of the system that promotes scientific and ethical standards in research, through data collection, consenting and supporting research, due to their insider cultural knowledge and fluency in...

Non-communicable diseases in sub-Saharan Africa: a scoping review of large cohort studies.

BackgroundNon-communicable diseases (NCDs) cause a large and growing burden of morbidity and mortality in sub-Saharan Africa. Prospective cohort studies are key to study multiple risk factors and chronic diseases and are crucial to our understanding of the burden, aetiology and prognosis of NCDs in SSA. We aimed to identify the level of research output on NCDs and their risk factors collected by cohorts in SSA.MethodsWe conducted a scoping review to map the extent of current NCDs research in SSA by identifying studies published after the year 2000 using prospectively collected cohort data on any of the six NCDs (cardiovascular diseases, diabetes, obesity, chronic kidney disease, chronic respiratory diseases, and cancers), ≥1 major risk factor (other than age and sex), set only within SSA, enrolled ≥500 participants, and ≥12 months of follow-up with ≥2 data collection points (or with plans to). We performed a systematic search of databases, a manual search of references lists from included articles and the INDEPTH network website, and study investigators from SSA were contacted for further articles.ResultsWe identified 30 cohort studies from the 101 included articles. Eighteen countries distributed in West, Central, East and Southern Africa, were represented. The majority (27%) set in South Africa. There were three studies including children, twenty with adults, and seven with both. 53% of cohorts were sampled in general populations, 47% in clinical populations, and 1 occupational cohort study. Hypertension (n = 23) was most commonly reported, followed by obesity (n = 16), diabetes (n = 15), CKD (n = 6), COPD (n = 2), cervical cancer (n = 3), and breast cancer (n = 1). The majority (n = 22) reported data on at least one demographic/environmental, lifestyle, or physiological risk factor but these data varied greatly.ConclusionsMost studies collected data on a combination of hypertension, diabetes, and obesity and few studies collected data on respiratory diseases and cancer. Although most collected data on different risk factors the methodologies varied greatly. Several methodological limitations were found including low recruitment rate, low retention rate, and lack of validated and standardized data collection. Our results could guide potential collaborations and maximize impact to improve our global understanding of NCDs (and their risk factors) in SSA and also to inform future research, as well as policies.

Strategies for complex supply networks: findings from the offshore wind power industry

Purpose The purpose of this paper is to explore the challenges facing companies that operate within complex supply networks and the strategies they use to manage such complex supply networks. Design/methodology/approach The paper uses mixed methods by combining in-depth case studies with an executive forum with those of senior industry stakeholders. The two in-depth supply network case studies were carried out through multiple interviews with focal (or ‘developer’) firms that supply energy through offshore wind power and key suppliers such as wind turbine manufacturers. Findings The findings show the challenges the offshore wind power industry faces because of complex supply networks, including attempts by several actors to exert their power and control. Despite the networks facing similar complexities and challenges, two distinctly different strategies for orchestrating and governing supply networks are uncovered: one strategy resembles an interventionist strategy, while the other is based on delegation. Research limitations/implications Based on the findings, the authors identify and develop a classification of complex supply network divided into intervention and delegation strategies, thereby adding to existing research on ways to manage complex supply networks. Practical implications The authors identify strategies for focal firms for managing in complex supply networks, based on control and intervention or coordination and delegation. Originality/value Existing research on supply network strategies has largely focused on non-complex contexts. This paper draws from complex adaptive systems and organisational behaviour perspectives to contribute original insights into supply network strategies in complex supply networks.

Network Structure in Coastal and Marine Tourism: Diving into the Three Clusters

ABSTRACT This study identifies the stakeholders of coastal marine tourism (CMT) and their structural patterns of relationships using theories of network structure. While the network analysis examines the relational aspects, content analysis gained through interviews and focus group discussions were used to understand the specific issues of CMT. The Thousand Islands as one of the priority destinations set up by the Indonesian Ministry of Tourism was chosen as the case study. The overall network graph confirms that CMT in the Thousand Islands is a community-based tourism surrounded mostly by government institutions. Marine transportation organisations are mainly located within the inner circle. Using modularity, the network splits into three clusters, delivering a more in-depth network structure of the CMT. Practical implications of the study are for policy makers to improve stakeholder cooperation to address issues related to the management of marine transportation, water and energy supplies, innovation in natural and cultural attractions and greater development of human resources.

The state of social science research on antimicrobial resistance

This paper investigates the genealogy of social science research into antimicrobial resistance (AMR) by piecing together the bibliometric characteristics of this branch of research. Drawing on the Web of Science as the primary database, the analysis shows that while academic interest in AMR has increased substantially over the last few years, social science research continues to constitute a negligible share of total academic contributions. More in-depth network analysis of citations and bibliometric couplings suggests how the impact of social science research on the scientific discourse on AMR is both peripheral and spread thin. We conclude that this limited social science engagement is puzzling considering the clear academic and practical demand and the many existing interdisciplinary outlets.

Citywide Transmission of Multidrug-resistant Tuberculosis Under China's Rapid Urbanization: A Retrospective Population-based Genomic Spatial Epidemiological Study.

Population movement could extend multidrug-resistant tuberculosis (MDR-TB) transmission and complicate its global prevalence. We sought to identify the high-risk populations and geographic sites of MDR-TB transmission in Shenzhen, the most common destination for internal migrants in China. We performed a population-based, retrospective study in patients diagnosed with MDR-TB in Shenzhen during 2013-2017. By defining genomic clusters with a threshold of 12-single-nucleotide polymorphism distance based on whole-genome sequencing of their clinical strains, the clustering rate was calculated to evaluate the level of recent transmission. Risk factors were identified by multivariable logistic regression. To further delineate the epidemiological links, we invited the genomic-clustered patients to an in-depth social network investigation. In total, 105 (25.2%) of the 417 enrolled patients with MDR-TB were grouped into 40 genome clusters, suggesting recent transmission of MDR strains. The adjusted risk for student to have a clustered strain was 4.05 (95% confidence interval, 1.06-17.0) times greater than other patients. The majority (70%, 28/40) of the genomic clusters involved patients who lived in different districts, with residences separated by an average of 8.76 kilometers. Other than household members, confirmed epidemiological links were also identified among classmates and workplace colleagues. These findings demonstrate that local transmission of MDR-TB is a serious problem in Shenzhen. While most transmission occurred between people who lived distant from each other, there was clear evidence that transmission occurred in schools and workplaces, which should be included as targeted sites for active case finding.The average residential distance between genomic-clustered cases was more than 8 kilometers, while schools and workplaces, identified as sites of transmission in this study, deserve increased vigilance for targeted case finding of multidrug-resistant tuberculosis.

Diterpene Ginkgolides Exert an Antidepressive Effect Through the NT3-TrkA and Ras-MAPK Pathways

Depression is a highly prevalent mental illness that severely impacts the quality of life of affected individuals. Our recent studies demonstrated that Diterpene Ginkgolides (DG) have antidepressive effects in mice. However, the underlying molecular mechanisms remained much unclear. In this study, we assessed the antidepressive effects of chronic DG therapy in rats by evaluating depression-related behaviors, and we examined potential side effects using biochemical indicators. Furthermore, we performed an in-depth molecular network analysis of gene-protein-metabolite interactions on the basis of metabolomics. Chronic DG treatment significantly ameliorated the depressive-like behavioral phenotype. Furthermore, the neurotrophin signaling-related NT3-TrkA and Ras-MAPK pathways may play an important role in the antidepressive effect of DG in the hippocampus. These findings provide novel insight into the mechanisms underlying the antidepressive action of DG, and should help advance the development of new therapeutic strategies for depression. Funding: This work was supported by the National Key R&D Program of China (Grant No. 2017YFA0505700), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (No.2019PT320002), the National Natural Science Foundation of China (Grant No. 8190050191), the National Key Program International Cooperation Project (No. 81820108015) and the Natural Science Foundation of Chongqing, China (Grant No. cstc2019jcyj-msxm0236). Declaration of Interest: The authors declare that they have no conflicts of interest. Ethical Approval: All animal experiments were approved by the Ethics Committee of Chongqing Medical University (permit number: 20120126), and were in accordance with the Guide for the Care and Use of Laboratory Animals.

Possibilities of Neural Network Powder Diffraction Analysis Crystal Structure of Chemical Compounds

Some possibilities of using convolutional artificial neural networks (ANN) for powder diffraction structural analysis of crystalline substances have been investigated. First, ANNs are used to classify crystalline systems and space groups according to calculated full-profile diffractograms calculated from the crystal structures of the ICSD database (2017 year). The ICSD database contains 192004 structures, of which 80% was used for in-depth network training, and 20% for independent testing of recognition accuracy. The accuracy of classification by a network of crystalline systems was 87.9%, and that of space groups was 77.2%. Secondly, the ANN is used for a similar classification of structural models generated by the stochastic genetic algorithm in the search processes for triclinic crystal structures of test compound K4SnO4 according to their full-profile diffraction patterns. The classification criterion was the entry of one or several atoms into their crystallographic positions in the structure of a substance. Independent deep network training was performed on 120 thousand structural models of the K4PbO4 triclinic structure generated in several runs of the genetic algorithm. The accuracy of the classification of K4SnO4 structural models exceeded 50%. The results show that deeply trained convolutional ANNs can be effective for classifying crystal structures according to the structural characteristics of their powder diffraction patterns

Cross-District Transmission of Multidrug-Resistant Tuberculosis in Shenzhen, a Newly Developed Metropolis of China with Massive Young Migrant Workers

Background: Population movement could extend multidrug-resistant tuberculosis (MDR-TB) transmission and complicate its global prevalence. We sought to identify the high-risk populations and geographic sites of MDR-TB transmission in Shenzhen, the most common destination for internal migrants in China. Methods: We performed a population-based, retrospective study of patients who were diagnosed with MDR-TB in Shenzhen during 2013-2017. By defining genomic clusters with a threshold of 12 SNP distance based on whole-genome sequencing their clinical strains, the clustering rate was calculated to evaluate the level of recent transmission. Risk factors for MDR-TB transmission were identified by multivariable logistic regression. To further delineate the epidemiological links, we invited the genomic-clustered patients to an in-depth social network investigation. Results: In total, 105 (25·2%) of the 417 enrolled MDR-TB patients were grouped into 40 genome clusters, suggesting recent transmission of MDR strains. The adjusted risk for students to have a clustered strain was 4·05 (95% confidential intervals [CI], 1·06-17·0) times greater than other patients. The majority (70%, 28/40) of the genomic clusters involved patients who lived in different districts, with residences separated by an average of 8·76 kilometers. Other than household members, confirmed epidemiological links were also identified among classmates and workplace colleagues. Interpretation: These findings demonstrate that local transmission of MDR-TB is a serious problem in Shenzhen city. While most transmission occurred between people who lived distant from each other, there was clear evidence that transmission occurred in schools and workplaces, which should be included as targeted sites for active case finding. Funding: Sanming Project of Shenzhen (SZSM201611030); and National Science and Technology Major Project of China (2017ZX10201302 & 2018ZX10715012-005) Declaration of Interest: We declare that we have no conflicts of interest. Ethical Approval: The study protocol was approved by the Ethical Review Board of Shenzhen CCDC (No. 20180310).

Identifying gender-responsive approaches in rural advisory services that contribute to the institutionalisation of gender in Pakistan

ABSTRACTPurpose:Unequal reach and access to information is an issue that affects women involved in agricultural activities around the world. Recent initiatives to address gender unequal access to agricultural information have been clumsy, overlooking participatory approaches that focus on transformative change. This study uses Pakistani rural advisory services to compare farmers' and extension workers’ perceptions of access to agricultural information, to identify culturally acceptable gender-responsive schemes.Design/methodology/approach:One-hundred and eleven extension workers in Pakistan’s public rural advisory services were interviewed and crosstabulated with farmers’ answers in previous studies.Findings:Male extension workers are aware that women access less information less often; however they might not be aware of its importance in the gender inequality debate. Lead farmers could offer a potentially transformative knowledge pathway because of its blend of formal and informal interactions – both systems favoured by female smallholders. An exclusively female-led lead farmer approach could be developed and trialled in specific areas of the province.Practical implications:Targeted initiatives focusing on improving awareness and importance of gender inequalities in information access as well as specific extension system development centred on lead female farmers and extension agents are important in institutionalising gender and creating transformative change.Theoretical implications:Linking these activities to in-depth social network and agricultural innovation system analyses would provide further evidence of the importance of focused gender activities and their impact on food security.Originality/value:This paper highlights the importance of analysing individual perceptions to understand the types of initiatives that could be considered for a wider institutionalisation of gender in RAS.

Design and implementation of DRFCN in-depth network for military target identification

Automatic target recognition (ATR) technology has always been the key and difficult point in the military field. This paper designs and implements a new DRFCN in-depth network for military target identification. Firstly, the part of DRPN is densely connected by the convolution module to reuse the features of each layer in the deep network model to extract the high quality goals of sampling area; Secondly, in the DFCN part, we fuse the information of the semantic features of the high and low level feature maps to realize the prediction of target area and location information in the sampling area; Finally, the deep network model structure and the parameter training method of DRFCN are given. Further, we conduct experimental analysis and discussion on the DRFCN algorithm: 1) Based on the PASCAL VOC dataset for comparison experiments, the results show that DRFCN algorithm is obviously superior to the existing algorithm in terms of average accuracy, real-time and model size because of the convolution module dense connection method. At the same time, it is verified that the DRFCN algorithm can effectively solve the problem of gradient dispersion and gradient expansion. 2) Using the self-built military target dataset for experiments, the results show that the DRFCN algorithm implements the military target recognition task in terms of accuracy and real-time.

How do Moroccan-Dutch parents (re)construct their parenting practices? Post-migration parenthood as a social site for learning and identity

Abstract In this paper we investigate how mothers and fathers living in culturally heterogeneous contexts learn about the practice of parenting. By applying a communities of practice perspective (Wenger, 1998; 2010) on the (re)construction of parenting practice postmigration, this study highlights the under-examined processes of social negotiation over meaning making and identity formation underlying cultural transformations within the family context. Using a discourse analytical approach embedded in an ethnographically inspired methodology, we include 1) in-depth social network interview data with 23 Moroccan-Dutch parents and 2) observational data of bottom-up parenting programme sessions taking place at participants' neighborhood-based migrant organizations. Analyses revealed how a social learning dynamic is at work when parents experience clashes at boundaries of cultural meanings. Navigating and negotiating multiple cultural frameworks in interaction with others, parents use and adopt renewed senses of belonging to claim direction in this cultural heterogeneity. As such, they re-interpret meanings in social learning interactions with others and create space to (re)construct parenting practices situated in their urban postmigration residence. This analysis exemplifies how applying a sociocultural learning approach to parenting postmigration reconsiders our understanding of parenthood as a social learning site on which renewed communities and ‘glocalized’ practices emerge.

A comparison of all-cause and cause-specific mortality by household socioeconomic status across seven INDEPTH network health and demographic surveillance systems in sub-Saharan Africa

ABSTRACTBackground: Understanding socioeconomic disparities in all-cause and cause-specific mortality can help inform prevention and treatment strategies.Objectives: To quantify cause-specific mortality rates by socioeconomic status across seven health and demographic surveillance systems (HDSS) in five countries (Ethiopia, Kenya, Malawi, Mozambique, and Nigeria) in the INDEPTH Network in sub-Saharan Africa.Methods: We linked demographic residence data with household survey data containing living standards and education information we used to create a poverty index. Person-years lived and deaths between 2003 and 2016 (periods varied by HDSS) were stratified in each HDSS by age, sex, year, and number of deprivations on the poverty index (0–8). Causes of death were assigned to each death using the InterVA-4 model based on responses to verbal autopsy questionnaires. We estimated rate ratios between socioeconomic groups (2–4 and 5–8 deprivations on our poverty index compared to 0–2 deprivations) for specific causes of death and calculated life expectancy for the deprivation groups.Results: Our pooled data contained almost 3.5 million person-years of observation and 25,038 deaths. All-cause mortality rates were higher among people in households with 5–8 deprivations on our poverty index compared to 0–2 deprivations, controlling for age, sex, and year (rate ratios ranged 1.42 to 2.06 across HDSS sites). The poorest group had consistently higher death rates in communicable, maternal, neonatal, and nutritional conditions (rate ratios ranged 1.34–4.05) and for non-communicable diseases in several sites (1.14–1.93). The disparities in mortality between 5–8 deprivation groups and 0–2 deprivation groups led to lower life expectancy in the higher-deprivation groups by six years in all sites and more than 10 years in five sites.Conclusions: We show large disparities in mortality on the basis of socioeconomic status across seven HDSS in sub-Saharan Africa due to disparities in communicable disease mortality and from non-communicable diseases in some sites. Life expectancy gaps between socioeconomic groups within sites were similar to the gaps between high-income and lower-middle-income countries. Prevention and treatment efforts can benefit from understanding subpopulations facing higher mortality from specific conditions.

Online learning method based on artificial neural network to optimize magnetic shielding characteristic parameters

Magnetic shielding plays an important role in magnetically susceptible devices such as cold atom clocks, atomic interferometers and other precision equipment. The residual magnetic field in a magnetic shield under a varying external magnetic field can be calculated by the Jiles-Atherton (J-A) hysteresis model and magnetic shielding coefficient. According to the calculation results, the variation of internal magnetic field can be compensated for the active compensation coils. However, it is difficult to practically obtain the exact values of the five magnetic-shielding-related parameters in the J-A hysteresis model and the other two magnetic-field-attenuation-related parameters. It usually takes a lot of time to match the parameters manually according to the measured hysteresis loop and it is difficult to ensure that the final parameters are the global optimal values. The machine learning method based on artificial neural network has been used as an efficient method to optimize the parameters of complex systems. Owing to the powerful computing capability of modern computers, using the artificial neural network to optimize parameters is usually much faster than manual optimization method, and has a greater probability of finding the global optimal parameters. In this paper, the five J-A parameters and the other two parameters relating to magnetic field attenuation are optimized by the method of online learning based on artificial neural network, and the residual magnetic field in the magnetic shield is predicted under the simulated satellite magnetic field environment. By comparing the measured residual magnetic field with the predicted value, it is found that the machine learning method can optimize the magnetic shielding characteristic parameters more quickly and accurately than the manual optimization method. This result can not only help us to compensate for the magnetic field better and optimize the parameters of our cold atom system, but also validate the application of neural network in a multi-parameter physical system. This proves that the in-depth learning neural network can be conveniently applied to other physical experiments with multi-parameter interaction, and can quickly determine the optimal parameters needed in the experiment. This application is especially effective for remote experiments with slow response to parameter adjustment, such as scientific experiments carried out on satellites or deep space.

Uncovering the mechanism of Maxing Ganshi Decoction on asthma from a systematic perspective: A network pharmacology study

Maxing Ganshi Decoction (MXGSD) is used widely for asthma over thousands of years, but its underlying pharmacological mechanisms remain unclear. In this study, systematic and comprehensive network pharmacology was utilized for the first time to reveal the potential pharmacological mechanisms of MXGSD on asthma. Specifically, we collected 141 bioactive components from the 600 components in MXGSD, which shared 52 targets common to asthma-related ones. In-depth network analysis of these 52 common targets indicated that asthma might be a manifestation of systemic neuro-immuno-inflammatory dysfunction in the respiratory system, and MXGSD could treat asthma through relieving airway inflammation, improving airway remodeling, and increasing drug responsiveness. After further cluster and enrichment analysis of the protein-protein interaction network of MXGSD bioactive component targets and asthma-related targets, we found that the neurotrophin signaling pathway, estrogen signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway might serve as the key points and principal pathways of MXGSD gene therapy for asthma from a systemic and holistic perspective, and also provides a novel idea for the development of new drugs for asthma.

Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory

A more accurate hourly prediction of day-ahead wind power can effectively reduce the uncertainty of wind power integration and improve the competitiveness of wind power in power auction markets. However, due to the inherent stochastic and intermittent nature of wind energy, it is very difficult to sharply improve the multi-step wind power forecasting (WPF) accuracy. According to theory of direct and recursive multi-step prediction, this study firstly proposes the models of R (recursive)-VMD (variational model decomposition)-LSTM (long short-term memory) and D (direct)-VMD-LSTM for the hourly forecast of day-ahead wind power by using a combination of a novel and in-depth neural network forecasting model called LSTM and the variational model decomposition (VMD) technique. The data from these model tests were obtained from two real-world wind power series from a wind farm located in Henan, China. The experimental results show that LSTM can achieve more precise predictions than traditional neural networks, and that VMD has a good self-adaptive ability to remove the stochastic volatility and retain more adequate data information than empirical mode decomposition (EMD). Secondly, the R-VMD-LSTM and D-VMD-LSTM are comparatively studied to analyze the accuracy of each step. The results verify the effectiveness of the combination of the two models: The R-VMD-LSTM model provides a more accurate prediction at the beginning of a day, while the D-VMD-LSTM model provides a more accurate prediction at the end of a day.

Socioeconomic and demographic factors associated with caesarean section delivery in Southern Ghana: evidence from INDEPTH Network member site

BackgroundIn recent years, caesarean section rates continue to evoke worldwide concern because of their steady increase, lack of consensus on the appropriate caesarean section rate and the associated short- and long-term risks.This study sought to identify the rate of caesarean section and associated factors in two districts in rural southern Ghana.MethodsPregnancy, birth, and socio-demographic information of 4948 women who gave birth between 2011 and 2013 were obtained from the database of Dodowa Health and Demographic Surveillance System. The rate of C-section was determined and the associations between independent and dependent variables were explored using logistic regression. The analyses were done in STATA 14.2 at 95% confidence interval.ResultsThe overall C-section rate for the study period was 6.59%. Women aged 30–34 years were more than twice likely to have C-section compared to those < 20 year (OR: 2.16, 95% CI: 1.20–3.90). However, women aged 34 years and above were more than thrice likely to undergo C-section compared to those < 20 year (OR: 3.73, 95% CI: 1.45–5.17).The odds of having C-section was 65 and 79% higher for participants with Primary and Junior High level schooling respectively (OR: 1.65, 95% CI: 1.08–2.51, OR:1.79, 95%CI: 1.19–2.70). The likelihood of having C-section delivery reduced by 60, 37, and 35% for women with parities 2, 3 and 3+ respectively (OR:0.60, 95% CI: 0.43–0.83, OR: 0.37, 95% CI: 0.25–0.56, OR:0.35, 95% CI: 0.25–0.54). There were increased odds of 36, 52, 83% for women who belong to poorer, middle, and richer wealth quintiles respectively (OR: 1.36, 95%CI: 0.85–2.18, OR: 1.52, 95% CI: 0.97–2.37, OR: 1.83, 95% CI: 1.20–2.80). Participants who belonged to the richest wealth quintile were more than 2 times more likely to have C-section delivery (OR: 2.14, 95%CI: 1.43–3.20). The odds of having C-section delivery reduced by 76% for women from Ningo-Prampram district (OR: 0.76, 95% CI: 0.59.0.96). Women whose household heads have Junior High level and above of education were 45% more likely to have C-section delivery (OR: 1.45, 95% CI: 1.09–1.93).ConclusionAge of mother, educational level, parity, household socioeconomic status, district of residence, and level of education of household head are associated with caesarean section delivery.