Multivariate Analysis Methods Research Articles

Exploring the causal role of immune cells in vascular dementia: A bidirectional mendelian randomization study

BackgroundThe aim of this study was to explore the causal association between immune cells and VaD based on a two-sample bidirectional Mendelian randomization study. MethodsBidirectional two-sample MR analyses based on pooled datasets from publicly available genome-wide association studies were performed using inverse variance weighted (IVW), weighted median (WE), and MR–Egger regressions to evaluate the causal relationships between immune cells and vascular dementia. Heterogeneity was assessed using Cochran's Q statistic. The reliability of the MR analysis results was verified by using the MR-PRESSO method for outlier detection, the MR–Egger method for horizontal multivariate analysis, and the leave-one-out method for sensitivity analysis. ResultsSpecifically, 27 immunophenotypes were associated with VaD pathogenesis, including Sw mem %lymphocyte (P = 0.043), CD38 on CD20- (P = 0.039), CD11c+ monocyte AC (P = 0.024), DC AC (P = 0.002), CCR2 on CD62L+ myeloid DC (P = 0.039), Resting Treg %CD4 (P = 0.042), Activated & resting Treg %CD4+ (P = 0.038), CD28+ CD45RA− CD8br %CD8br (P = 0.047), NK %CD3− lymphocyte (P = 0.042), CD45 on B cell (P = 0.029), FSC-A on NKT (P = 0.033), CD45 on CD33br HLA DR+ CD14− (P = 0.039) were significantly correlated with increased VaD risk. Additionally, four immune phenotypes, namely, CD19 on CD20−, Resting Treg %CD4, Activated & resting Treg %CD4+, and CD11c+ monocyte AC, showed bidirectional effects on VaD. ConclusionsMR analysis revealed potential causal relationships between certain immune cells and VaD. Our preliminary exploration through immune cell infiltration analysis highlights the significant value of immune cells in VaD. Therefore, this study may provide a new perspective for the prevention and treatment of VaD.

Ground-truth validation of uni- and multivariate lesion inference approaches.

Lesion analysis aims to reveal the causal contributions of brain regions to brain functions. Various strategies have been used for such lesion inferences. These approaches can be broadly categorized as univariate or multivariate methods. Here we analysed data from 581 patients with acute ischaemic injury, parcellated into 41 Brodmann areas, and systematically investigated the inferences made by two univariate and two multivariate lesion analysis methods via ground-truth simulations, in which we defined a priori contributions of brain areas to assumed brain function. Particularly, we analysed single-region models, with only single areas presumed to contribute functionally, and multiple-region models, with two contributing regions that interacted in a synergistic, redundant or mutually inhibitory mode. The functional contributions could vary in proportion to the lesion damage or in a binary way. The analyses showed a considerably better performance of the tested multivariate than univariate methods in terms of accuracy and mis-inference error. Specifically, the univariate approaches of Lesion Symptom Mapping as well as Lesion Symptom Correlation mis-inferred substantial contributions from several areas even in the single-region models, and also after accounting for lesion size. By contrast, the multivariate approaches of Multi-Area Pattern Prediction, which is based on machine learning, and Multi-perturbation Shapley value Analysis, based on coalitional game theory, delivered consistently higher accuracy and specificity. Our findings suggest that the tested multivariate approaches produce largely reliable lesion inferences, without requiring lesion size consideration, while the application of the univariate methods may yield substantial mis-localizations that limit the reliability of functional attributions.

Assessment of comprehensive PAH index in roasted Tan lamb by two-dimensional correlation spectroscopy combined with hyperspectral imaging.

Thermally processed meat may contain harmful compounds, including polycyclic aromatic hydrocarbons (PAHs). This study constructed, for the first time, the comprehensive PAH index (CPI) concentration (phenanthrene [26.47%], acenaphthene [21.83%], pyrene [18.64%], fluoranthene [17.11%], fluorene [8.49%], and anthracene [7.46%]). A visible near-infrared (Vis-NIR) hyperspectral image (HSI) system was employed to detect CPI in 150 roasted Tan lamb samples. Furthermore, two-dimensional correlation spectra were used to identify spectral features and reveal the order of chemical bond changes under the characteristic peaks at 579-737-631-449nm. The results indicated that competitive adaptive reweighted sampling-multiple linear regression quantitative prediction model worked the best with calibration set coefficient of determination of 0.9161, calibration set coefficient of root mean square error of 2.3426µg/kg, R-squared prediction of 0.8469, and root mean square error of prediction of 2.4119µg/kg. Finally, PAH content distributions were visualized using the best prediction model. This study aimed to propose a feasible method for CPI in roasted Tan lamb detection based on Vis-NIR HSI coupled with multivariate analysis methods.

Hierarchical and mixed uncertainty quantification for simulation-based structural seismic fragility analysis

Seismic fragility assessments (SFA) encompass significant uncertainties, including ground motions, materials, geometry, boundaries, and modeling, which impart inherent uncertainties to the fragility curve. Quantifying these uncertainties is essential for informed decision-making in earthquake disaster mitigation. This paper presents a hierarchical uncertainty quantification (UQ) framework for simulation-based SFA, which can address mixed aleatory and epistemic uncertainties. The framework comprises two levels: At Level 1, ground motion uncertainties are accounted for through record-to-record variation, while structural parameter uncertainties are captured using well-defined probability distributions. The quantification of uncertainty in seismic fragility is accomplished using mean and quantile fragility curves, representing the average outcome and the dispersion of structural fragility, respectively. Fuzziness is also introduced to enhance the objectivity of failure determination. Level 2 addresses uncertainties in the distribution hyper-parameters of structural variables through evidence theory, facilitating an understanding of their influence on the seismic fragility curve and quantifying uncertainty in mean and quantile fragility curves. This UQ framework builds upon the multivariate seismic fragility analysis method, integrating Kriging regression for seismic demand prediction and logistic regression for generating multivariate seismic fragility functions. The proposed framework is validated numerically on an existing three-span reinforced concrete continuous girder bridge, affirming its practical utility and reliability.

Research on the measurement of general risk preference based on multivariate statistical analysis methods

Research on the measurement of general risk preference based on multivariate statistical analysis methods

Towards the establishment of a green and sustainable analytical methodology for hyperspectral imaging-based authentication of wholemeal bread

The increased interest in whole-grain products, along non unified European regulations on the composition of wholemeal bread could lead to its misleading labelling. Therefore, to ensure the safety of consumers, both health-wise and from possible fraud, a novel hyperspectral imaging-based authentication method is being proposed, given that no previous study has combined imaging techniques with authentication of wholemeal flour content. Quantification based on pixel counting by classification (QPC) utilizes multivariate analysis methods such as PLS-DA and SVM to classify pixels within a bread sample (as Wholemeal and White flour), based on their visible-near-infrared spectra, to later estimate its proportion of wholemeal flour. This is in accordance with the heterogeneous nature of bread samples, in which individual pixels belonging to both wholemeal and white flour can be accounted for, which was proved by implementing unsupervised training techniques such as hierarchical cluster analysis (HCA). Results show that the quantification model was able to successfully predict wholemeal flour content with a maximum deviation of 8 g wholemeal flour/100 g flour from the estimated value.

Monitoring of the Homogeneity of Primer Layers for Ink Jet Printing on Polyester Fabrics by Hyperspectral Imaging.

Untreated polyester films and fibers can be hardly printed or coated, in particular if aqueous inks or lacquers have to be applied. Therefore, an adequate primer layer has to be applied first. A cationic polymer formulation based on poly(dimethylamine-co-epichlorohydrin-co-ethylenediamine) (PDEHED) was used as primer layer for digital printing on polyester fabrics. Because of the exceedingly high requirements on the homogeneity of such layers, hyperspectral imaging was used for qualitative and quantitative monitoring of the distribution of the primer layer on the textiles. Multivariate data analysis methods based on the PLS algorithm were applied for quantification of the NIR reflection spectra using gravimetry as a reference method. Optimization of the calibration method resulted in various models with prediction errors of about 1.2 g/m2. The prediction performance of the models was proven in external validations using independent samples. Moreover, a special ink jet printing technology was tested for application of the aqueous primer formulation itself. Since possible clogging of jet nozzles in the print head might lead to inhomogeneity in the coatings such as missing tracks, the potential of hyperspectral imaging to detect such defects was investigated. It was demonstrated that simulated missing tracks can be clearly detected. Consequently, hyperspectral imaging has been proven to be a powerful analytical tool for in-line monitoring of the quality of printability improvement layers and similar systems.

Bank Supervision and bank’s Outcomes: The Case of Tunisia

ABSTRACT Supervision is important for the entire financial system since it plays the role of a stabilizer; it seeks to create a healthy environment for financial institutions, and in particular for banks. Bank supervision is considerably more challenging than that of other institutions because of the opacity and complexity of bank assets. We explore the impact of supervision on the risk, profitability, and growth of Tunisian banks. The empirical study focused on 210 annual reports of almost all Tunisian banks during the 2010–2019 period. Banking supervision effectiveness is measured by enforcement outputs (i.e. on-site audits and sanctions). The generalized least squares method of multivariate analysis was used to analyze our panel data.The results show that supervisory and regulatory measures applied during this period have a negative impact on bank risk and profitability. This means that stricter banking regulation and supervision reduce both the risk and profitability of banks. Our results also show that intense supervision reduces the growth of loans for Tunisian banks. Our results have important implications for the decision-making of bank managers and regulators in Tunisia as well as for relevant actors in similar emerging economies. Strengthening further banking supervision policy is still needed by the central bank of Tunisia, i.e. the bank regulator.

Rapid quantitative and qualitative analysis of talcum powder in wheat flour by microwave detection combined with multivariate analysis

Rapid and accurate monitoring of talcum powder in wheat flour is of great practical importance for maintaining market order, public health and food safety. In this study, the potential of a home-made microwave detection system for rapid quantitative and qualitative detection of talcum powder adulteration in wheat flour at 2.5–11.5 GHz was investigated. Useful microwave spectral information is selected using three feature selection strategies, the bootstrapping soft shrinkage (BOSS), the multiple feature spaces ensemble with least absolute shrinkage and selection operator (MEF-LASSO), and the competitive adaptive reweighted sampling (CARS). The eXtreme Gradient Boosting (XGBoost) classification and regression models are constructed and evaluated, and three nature-inspired optimization algorithms (NIOA) are used for parameter optimization: the Osprey Optimization Algorithm (OOA), the Crested Porcupine Optimizer (CPO), and the Sparrow Search Algorithm (SSA). The experimental results show that both feature selection strategies and NIOA algorithms can effectively improve the model performance. The SSA-XGBoost classification model has the highest prediction accuracy, and its prediction accuracy and F1-score can reach 100 % and 1, respectively. The MEF-LASSO-SSA-XGBoost regression model has strong robustness. The root mean square error (RMSE) was 1.76 %, the prediction coefficient of determination (R2) was 0.98, and the ratio of performance to deviation (RPD) was 8.66. The results of this study showed that the combination of microwave technology and machine learning model with multivariate analysis method can realize the rapid and accurate determination of qualitative and quantitative talcum powder in wheat flour.

The Influence of Wildfire Climate on Wildfire Incidence: The Case of Portugal

Although the influence of climate on the fire regime is unanimously recognized, most publications and studies on this influence are on a global scale. Therefore, this study aims to demonstrate the role of climate in wildfire incidence at the country and regional scale using multivariate statistical analysis and machine learning methods (clustering and classification algorithms). Mainland Portugal was chosen as a case study due to its climate and because it is the European region most affected by wildfires. The results demonstrate the climate signature in the spatial and temporal distribution of the wildfire incidence. The conclusions of the study include (i) the existence of two pyro-regions, with different types of climate (Csb and Csa) composed of NUTS II regions: the northern region composed of the Norte and Centro regions and the southern region composed of Alentejo and Algarve; (ii) the intra-annual variability in the wildfire incidence, characterized by two peaks, one in the spring and the other in the summer, are a consequence of the country’s type of climate; and (iii) how the annual cycle of wildfire incidence varies over the years depends on the weather conditions throughout each year. These results are of fundamental importance for wildfire managers, especially in the context of climate change.

A Comprehensive Analysis of Fel Ursi and Its Common Adulterants Based on UHPLC-QTOF-MSE and Chemometrics.

As one of the four most valuable animal medicines, Fel Ursi, named Xiong Dan (XD) in China, has the effect of clearing heat, calming the liver, and brightening the eyes. However, due to the special source of XD and its high price, other animals' bile is often sold as XD or mixed with XD on the market, seriously affecting its clinical efficacy and consumers' rights and interests. In order to realize identification and adulteration analysis of XD, UHPLC-QTOF-MSE and multivariate statistical analysis were used to explore the differences in XD and six other animals' bile. XD, pig gall (Zhu Dan, ZD), cow gall (Niu Dan, ND), rabbit gallbladder (Tu Dan, TD), duck gall (Yan Dan, YD), sheep gall (Yang Dan, YND), and chicken gall (Ji Dan, JD) were analyzed by UHPLC-QTOF-MSE, and the MS data, combined with multivariate analysis methods, were used to distinguish between them. Meanwhile, the potential chemical composition markers that contribute to their differences were further explored. The results showed that XD and six other animals' bile can be distinguished from each other obviously, with 27 ions with VIP > 1.0. We preliminarily identified 10 different bile acid-like components in XD and the other animals' bile with significant differences (p < 0.01) and VIP > 1.0, such as tauroursodeoxycholic acid, Glycohyodeoxycholic acid, and Glycodeoxycholic acid. The developed method was efficient and rapid in accurately distinguishing between XD and six other animals' bile. Based on the obtained chemical composition markers, it is beneficial to strengthen quality control for bile medicines.

Gray matter morphometric biomarkers for distinguishing manganese-exposed welders from healthy adults revealed by source-based morphometry

BackgroundChronic overexposure to manganese (Mn) may result in neurotoxicity, which is characterized by motor and cognitive dysfunctions. This study aimed to utilize multivariate source-based morphometry (SBM) to explore the biomarkers for distinguishing Mn-exposed welders from healthy controls (HCs). MethodsHigh-quality 3D T1-weighted MRI scans were obtained from 45 Mn-exposed full-time welders and 33 age-matched HCs in this study. After extracting gray matter structural covariation networks by SBM, multiple classic interaction linear models were applied to investigate distinct patterns in welders compared to HCs, and Z-transformed loading coefficients were compared between the two groups. A receiver operating characteristic (ROC) curve was used to identify potential biomarkers for distinguishing Mn-exposed welders from HCs. Additionally, we assessed the relationships between clinical features and gray matter volumes in the welders group. ResultsA total of 78 subjects (45 welders, mean age 46.23±4.93 years; 33 HCs, mean age 45.55±3.40 years) were evaluated. SBM identified five components that differed between the groups. These components displayed lower loading weights in the basal ganglia, thalamus, default mode network (including the lingual gyrus and precuneus), and temporal lobe network (including the temporal pole and parahippocampus), as well as higher loading weights in the sensorimotor network (including the supplementary motor cortex). ROC analysis identified the highest classification power in the thalamic network. ConclusionsAltered brain structures might be implicated in Mn overexposure-related disturbances in motivative modulation, cognitive control and information integration. These results encourage further studies that focus on the interaction mechanisms, including the basal ganglia network, thalamic network and default mode network. Our study identified potential neurobiological markers in Mn-exposed welders and illustrated the utility of a multivariate method of gray matter analysis.

Impact of Primary Disease Site of Involvement by Early-Stage Follicular Lymphoma on Patient Outcomes

IntroductionFollicular lymphoma is a common non-Hodgkin lymphoma that can start in a diverse array of tissues throughout the body. While the majority of patients may be able to live many years with this disease, cure remains very difficult to achieve. ObjectiveWe sought to investigate the impact of follicular lymphoma primary disease site in early-stage disease on patient outcomes using a large national database. MethodsBaseline demographic and disease data for patients diagnosed with follicular lymphoma from 2000-2015 was identified and extracted from the NCI Surveillance, Epidemiology, and End Results (SEER) database. Primary disease sites were grouped into one of two cohorts: nodal disease (lymph nodes and spleen) and extranodal disease (everything else). Analysis was performed using summary statistics, Kaplan-Meier method, and Cox-proportional hazards models for univariate and multivariate analysis. ResultsA total of 13,400 patients were included in the final analysis and the majority were non-Hispanic white (81%), with stage I (63%), and nodal FL (79%). Median overall survival for nodal disease was 15.1 years [95% CI (14.6-15.6)] while median overall survival for extranodal disease was 15.8 years [95% CI (14.9-16.3)]. Overall survival was slightly better for patients with extranodal disease [HR = 0.89, 95% CI (0.84-0.96); p-value = 0.00012]. This finding remained consistent after controlling for age and race [HR = 0.84, 95% CI (0.79-0.90); p-value <0.0001]. ConclusionsThe primary site of involvement by early-stage follicular lymphoma may have an impact on patient outcomes and warrants further investigation.

Spatio-temporal plant hormonomics: from tissue to subcellular resolution.

Due to technological advances in mass spectrometry, significant progress has been achieved recently in plant hormone research. Nowadays, plant hormonomics is well established as a fully integrated scientific field focused on the analysis of phytohormones, mainly on their isolation, identification, and spatiotemporal quantification in plants. This review represents a comprehensive meta-study of the advances in the phytohormone analysis by mass spectrometry over the past decade. To address current trends and future perspectives, Web of Science data were systematically collected and key features such as mass spectrometry-based analyses were evaluated using multivariate data analysis methods. Our findings showed that plant hormonomics is currently divided into targeted and untargeted approaches. Both aim to miniaturize the sample, allowing high-resolution quantification to be covered in plant organs as well as subcellular compartments. Therefore, we can study plant hormone biosynthesis, metabolism, and signalling at a spatio-temporal resolution. Moreover, this trend has recently been accelerated by technological advances such as fluorescence-activated cell sorting or mass spectrometry imaging.

Quality evaluation and health risk assessment of karst groundwater in Southwest China

Groundwater in karst regions is of immense value due to its vital support for regional ecosystems and residents' livelihoods. However, it is simultaneously threatened by multi-source pollution from agricultural non-point sources, industrial and domestic point sources, and mining activities. This study focuses on the Guangxi of China, which features typical karst topography, aiming to thoroughly assess the groundwater quality and related health risks in Guangxi, especially identifying the impacts of various key pollution sources on the groundwater environment. A total of 1912 groundwater samples were collected, covering an area of approximately 237,600 km2. The spatial distribution of pollutants was analysed using the Nemeroww index method and Kriging interpolation, while multivariate statistical and cluster analysis methods were employed to identify the main types of pollution sources. Furthermore, based on the human health risk assessment model of the U.S. Environmental Protection Agency (US EPA), a risk assessment was conducted for key pollutants. The results revealed widespread heavy metal contamination in Guangxi's groundwater, particularly with concentrations of Mn, As, Al, Pb reaching up to 9.4 mg/L, 2.483 mg/L, 37.95 mg/L, 4.761 mg/L, respectively, significantly exceeding China's national Class III groundwater quality standards. Cluster analysis indicated that mining and industrial activities are the primary sources of pollution. The health risk assessment demonstrated that these activities pose a significant risk to public health. The aim of this study is to provide a scientific basis for the protection of the groundwater environment in Guangxi and other karst areas, the formulation of pollution prevention and control strategies, and the optimization of urban and industrial land use layouts. Future research should focus on advanced isotopic and molecular biological techniques to trace pollution sources more precisely and evaluate the effectiveness of pollution control measures.

The Influence of Quality of Work Life, Mental Workload, and Self-Efficacy on Job Performance Mediated by Job Satisfaction in Nurses at Panglima Besar Soedirman Hospital

Nurses have a very important role in patient care in hospitals, namely integrated and quality nursing care for each patient. One of the most important things in Human Resources management is the performance of nurses. This study aims to analyze the direct influence of quality of work life, mental workload and self-efficacy on nurse performance, as well as the indirect influence of quality of work life, mental workload on nurse performance which is mediated by nurse job satisfaction at the Panglima Besar Soedirman National Defense Central Hospital, Jakarta. This research design is quantitative. Data collection used a non-probability sampling technique with a purposive sampling method. The sample used was 160 active nurses. This research data was collected using a questionnaire via Googleform. The data obtained was processed using the PLS-SEM multivariate analysis method with SmartPLS software. The results showed that quality of work life, mental workload and self-efficacy had a positive effect on nurses' job satisfaction. The results show that there is a positive influence on the quality of work life on nurse performance, then job satisfaction has a positive influence on nurse performance, but mental workload has a negative influence on nurse performance. The research results also show that self-efficacy has no effect on nurse performance. Quality of work life, mental workload and self-efficacy have an indirect influence on nurse performance through nurse job satisfaction. It is hoped that future research can conduct comparative studies between the health sector and other sectors to compare how factors such as quality of work life, mental workload, and self-efficacy influence job satisfaction and job performance.

Multivariate sharp-wave ripples in schizophrenia during awake state.

Schizophrenia (SZ) is a brain disorder characterized by psychotic symptoms and cognitive dysfunction. Recently, irregularities in sharp-wave ripples (SPW-Rs) have been reported in SZ. As SPW-Rs play a critical role in memory, their irregularities can cause psychotic symptoms and cognitive dysfunction in patients with SZ. In this study, we investigated the SPW-Rs in human SZ. We measured whole-brain activity using magnetoencephalography (MEG) in patients with SZ (n = 20) and sex- and age-matched healthy participants (n = 20) during open-eye rest. We identified SPW-Rs and analyzed their occurrence and time-frequency traits. Furthermore, we developed a novel multivariate analysis method, termed "ripple-gedMEG" to extract the global features of SPW-Rs. We also examined the association between SPW-Rs and brain state transitions. The outcomes of these analyses were modeled to predict the positive and negative syndrome scale (PANSS) scores of SZ. We found that SPW-Rs in the SZ (1) occurred more frequently, (2) the delay of the coupling phase (3) appeared in different brain areas, (4) consisted of a less organized spatiotemporal pattern, and (5) were less involved in brain state transitions. Finally, some of the neural features associated with the SPW-Rs were found to be PANSS-positive, a pathological indicator of SZ. These results suggest that widespread but disorganized SPW-Rs underlies the symptoms of SZ. We identified irregularities in SPW-Rs in SZ and confirmed that their alternations were strongly associated with SZ neuropathology. These results suggest a new direction for human SZ research.

Impact of Presenting Stage on Overall Survival in Patients Treated with Neoadjuvant Chemotherapy for Triple Negative Breast Cancer.

For operable triple-negative breast cancer (TNBC) treated with neoadjuvant chemotherapy (NAC), clinical prognostication and postoperative decision-making relies exclusively on whether a pathologic complete response (pCR) is achieved or not. We evaluated whether extent of disease at presentation further influenced overall survival (OS) among patients with pCR or with residual disease (RD) following NAC. Patients with stage I-III TNBC who underwent NAC were identified from the National Cancer Database from 2010 to 2019. Overall survival was assessed by disease extent using the Kaplan-Meier method and Cox proportional hazards regression for univariate and multivariable analysis. A total of 35,598 patients met inclusion criteria, and 11,967 achieved pCR. Ten-year OS was 88.5% and varied by cT and cN categoryat presentation. Best 10-year OS was seen in patients with cT1-2, cN0 (90.9%) and was worst in those with cT3-4, cN2-3 disease (72.0%). A total of 23,631 patients had RD. Ten-year OS was 60.1% and varied by cT and cN categoryat presentation. Best 10-year OS was seen in patients with cT1-2, cN0 (73.0%) and was worst in those with cT3-4, cN2-3 disease (36.3%). Notably, OS was significantly poorer for patients with cT3-4, cN2-3 diseaseat diagnosis and pCR versus those with cT1-2 cN0 and RD (aHR 1.30, 95% confidence interval 1.03-1.63, p = 0.03). Among patients with TNBC, extent of disease at presentation was prognostic for OS independently of response to NAC. Patients with advanced stage at presentation had poorer OS even in the context of pCR. Further investigation is needed to evaluate whether additional adjuvant therapy strategies should be considered for these patients.

Enhancing postgraduate education quality: a comprehensive analysis of factors influencing students’ satisfaction in university-business cooperation

ABSTRACT This study investigates the critical factors influencing students’ satisfaction with university-business cooperation (UBC) in postgraduate education, focusing on Chinese universities of applied sciences. Employing a comprehensive research model, we utilised multivariate statistical analysis methods, including canonical correlation analysis, partial correlation analysis, principal component analysis (PCA), and cluster analysis, to validate our measurement and hypotheses. Our findings reveal that teaching, tutor supervision, practical relevance, and individual development are primary determinants of students’ satisfaction with UBC in postgraduate education. Additionally, significant correlations exist between these factors, consistent with our proposed research model. The study emphasises the importance of fostering effective UBC for enhancing postgraduate education quality. Practical implications and recommendations for stakeholders are discussed, contributing both to the academic understanding of UBC and offering actionable insights for educational institutions and businesses. Future research directions in this field are also suggested.

Risk factors for maternal near-miss in an undeveloped province in south-central China, 2012–2022

ObjectiveTo explore the risk factors for maternal near-miss (MNM) using the WHO near-miss approach.MethodsData were obtained from the Maternal Near-Miss Surveillance System in Hunan Province, China, 2012–2022. Multivariate logistic regression analysis (method: Forward, Wald, α = 0.05) and adjusted odds ratios (aORs) were used to identify risk factors for MNM.ResultsOur study included 780,359 women with 731,185 live births, a total of 2461 (0.32%) MNMs, 777,846 (99.68%) non-MNMs, and 52 (0.006%) maternal deaths were identified. The MNM ratio was 3.37‰ (95%CI: 3.23–3.50). Coagulation/hematological dysfunction was the most common cause of MNM (75.66%). Results of multivariate logistic regression analysis showed risk factors for MNM: maternal age > = 30 years old (aOR > 1, P < 0.05), unmarried women (aOR = 2.21, 95%CI: 1.71–2.85), number of pregnancies > = 2 (aOR > 1, P < 0.05), nulliparity (aOR = 1.51, 95%CI: 1.32–1.72) or parity > = 3 (aOR = 1.95, 95%CI: 1.50–2.55), prenatal examinations < 5 times (aOR = 1.13, 95%CI: 1.01–1.27), and number of cesarean sections was 1 (aOR = 1.83, 95%CI: 1.64–2.04) or > = 2 (aOR = 2.48, 95%CI: 1.99–3.09).ConclusionThe MNM ratio was relatively low in Hunan Province. Advanced maternal age, unmarried status, a high number of pregnancies, nulliparity or high parity, a low number of prenatal examinations, and cesarean sections were risk factors for MNM. Our study is essential for improving the quality of maternal health care and preventing MNM.