The development of a road safety policy index and its application in evaluating the effects of road safety policy.

The paper presents an exploratory study of a road safety policy index developed for Norway. The index consists of ten road safety measures for which data on their use from 1980 to 2021 are available. The ten measures were combined into an index which had an initial value of 50 in 1980 and increased to a value of 185 in 2021. To assess the application of the index in evaluating the effects of road safety policy, negative binomial regression models and multivariate time series models were developed for traffic fatalities, fatalities and serious injuries, and all injuries. The coefficient for the policy index was negative, indicating the road safety policy has contributed to reducing the number of fatalities and injuries. The size of this contribution can be estimated by means of at least three estimators that do not always produce identical values. There is little doubt about the sign of the relationship: a stronger road safety policy (as indicated by index values) is associated with a larger decline in fatalities and injuries. A precise quantification is, however, not possible. Different estimators of effect, all of which can be regarded as plausible, yield different results.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.

Regression discontinuity design for the study of health effects of exposures acting early in life.

Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effect of an exposure on later outcomes by exploiting the discontinuity in the exposure probability at an assignment variable cut-off. With the intent of facilitating the use of RDD in the Developmental Origins of Health and Disease (DOHaD) research, we describe the main aspects of the study design and review the studies, assignment variables and exposures that have been investigated to identify short- and long-term health effects of early life exposures. We also provide a brief overview of some of the methodological considerations for the RDD identification using an example of a DOHaD study. An increasing number of studies investigating the effects of early life environmental stressors on health outcomes use RDD, mostly in the context of education, social and welfare policies, healthcare organization and insurance, and clinical management. Age and calendar time are the mostly used assignment variables to study the effects of various early life policies and programs, shock events and guidelines. Maternal and newborn characteristics, such as age, birth weight and gestational age are frequently used assignment variables to study the effects of the type of neonatal care, health insurance, and newborn benefits, while socioeconomic measures have been used to study the effects of social and welfare programs. RDD has advantages, including intuitive interpretation, and transparent and simple graphical representation. It provides valid causal estimates if the assumptions, relatively weak compared to other non-experimental study designs, are met. Its use to study health effects of exposures acting early in life has been limited to studies based on registries and administrative databases, while birth cohort data has not been exploited so far using this design. Local causal effect around the cut-off, difficulty in reaching high statistical power compared to other study designs, and the rarity of settings outside of policy and program evaluations hamper the widespread use of RDD in the DOHaD research. Still, the assignment variables' cut-offs for exposures applied in previous studies can be used, if appropriate, in other settings and with additional outcomes to address different research questions.

Health-related quality of life and its predictors among hypertensive patients on follow up at public hospitals in Addis Ababa, Ethiopia: application of Tobit regression model.

BACKGROUND: Health-related quality of life and its associated factors among hypertensive patients living in Ethiopia are not well studied. Therefore, this study aims to assess the level of health-related quality of life and its associated factors in hypertensive patients on follow-up in Public Hospitals in Addis Ababa, Ethiopia. METHODS: A facility-based cross-sectional study was conducted among 339 hypertensive patients on follow-up at Yekatit 12 &Zewditu Hospitals. Data were collected through face-to-face interviews using Euro Quality of Life Groups 5 Dimensions 5 Levels (EQ-5D-5L) in combination with Euro Quality of Life Groups Visual Analog Scale (EQ-VAS). A multivariable Tobit regression model was employed to assess the association between EQ-5D-5L index, EQ-VAS, and potential predicting factors. RESULTS: The median index value and EQ-VAS Scales score was 0.86 (IQR = 0.74, 0.94) and 69 (IQR = 55, 80) respectively. The proportion of participants reporting anxiety/depression and pain/discomfort problems was highest, while the fewest patients reported problems in the self-care dimension. Older, rural residents, low income, higher stages of hypertension, increased use of antihypertensive medications, and patients with an increased hospitalization rate scored lower on health-related quality of life than others. CONCLUSION: Health-related quality of life among hypertensive patients attending public health hospitals in Addis Ababa is unacceptably poor. Emphasis should be given to patients with higher stages of hypertension, increased use of antihypertensive medications, and an increased hospitalization rate giving due focus to older, rural residents, and low-income patients to promote their health-related quality of life.

Effects of taxane-induced peripheral neuropathy on hand dexterity impairment: evaluation of quantitative and subjective assessments.

PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) commonly involves hand dexterity impairment. However, the factors affecting hand dexterity impairment are unknown and there is currently no established treatment. The purpose of the current study was to clarify factors influencing hand dexterity impairment in taxane-induced peripheral neuropathy using subjective and objective assessments. METHODS: We assessed patient characteristics, treatment-related factors, subjective symptoms of CIPN (Patient Neurotoxicity Questionnaire [PNQ]), psychological symptoms, and upper limb dysfunction (Quick Disabilities of the Arm, Shoulder and Hand [Quick DASH]). Quantitative assessments were pinch strength, sensory threshold, hand dexterity impairment, and grip force control. Multiple regression analysis was performed using hand dexterity impairment as the dependent variable and age and PNQ, Quick DASH, and control of grip force as independent variables. RESULTS: Forty-three breast cancer patients were included in the analysis. Hand dexterity impairment in taxane-induced peripheral neuropathy patients was significantly correlated with age, grip force control, and PNQ sensory scores (p < 0.008). Multiple regression analysis demonstrated that PNQ sensory scores and grip force control were significantly associated with hand dexterity impairment (p < 0.01). CONCLUSION: Subjective symptoms (numbness and pain) and grip force control contributed to impaired hand dexterity in taxane-induced peripheral neuropathy.

3D visualization technology for Learning human anatomy among medical students and residents: a meta- and regression analysis.

BACKGROUND: 3D visualization technology applies computers and other devices to create a realistic virtual world for individuals with various sensory experiences such as 3D vision, touch, and smell to gain a more effective understanding of the relationships between real spatial structures and organizations. The purpose of this study was to comprehensively evaluate the effectiveness of 3D visualization technology in human anatomy teaching/training and explore the potential factors that affect the training effects to better guide the teaching of classroom/laboratory anatomy. METHODS: We conducted a meta-analysis of randomized controlled studies on teaching human anatomy using 3D visualization technology. We extensively searched three authoritative databases, PubMed, Web of Science, and Embase; the main outcomes were the participants' test scores and satisfaction, while the secondary outcomes were time consumption and enjoyment. Heterogeneity by I² was statistically determined because I²> 50%; therefore, a random-effects model was employed, using data processing software such as RevMan, Stata, and VOSviewer to process data, apply standardized mean difference and 95% confidence interval, and subgroup analysis to evaluate test results, and then conduct research through sensitivity analysis and meta-regression analysis. RESULTS: Thirty-nine randomized controlled trials (2,959 participants) were screened and included in this study. The system analysis of the main results showed that compared with other methods, including data from all regions 3D visualization technology moderately improved test scores as well as satisfaction and enjoyment; however, the time that students took to complete the test was not significantly reduced. Meta-regression analysis also showed that regional factorsaffected test scores, whereas other factors had no significant impact. When the literature from China was excluded, the satisfaction and happiness of the 3D virtual-reality group were statistically significant compared to those of the traditional group; however, the test results and time consumption were not statistically significant. CONCLUSION: 3D visualization technology is an effective way to improve learners' satisfaction with and enjoyment of human anatomical learning, but it cannot reduce the time required for testers to complete the test. 3D visualization technology may struggle to improve the testers' scores. The literature test results from China are more prone to positive results and affected by regional bias.

COVID-19 vaccine intercountry distribution inequality and its underlying factors: a combined concentration index analysis and multiple linear regression analysis.

Introduction: Inequitable access to COVID-19 vaccines among countries is a pressing global health issue. Factors such as economic power, political power, political stability, and health system strength contribute to disparities in vaccine distribution. This study aims to assess the inequality in vaccine distribution among countries based on these factors and identify their relationship with COVID-19 vaccine distribution. Methods: A Concentration Index (CI) analysis was conducted to evaluate inequalities in the distribution of COVID-19 vaccines among countries based on four separate variables: GDP per capita, political stability (PS), World Power Index (WPI), and Universal Health Coverage (UHC). Additionally, Multiple Linear Regression (MLR) analysis was employed to explore the relationship between vaccine distribution and these independent variables. Two vaccine distribution variables were utilized for result reliability. Results: The analysis revealed significant inequalities in COVID-19 vaccine distribution according to the countries' GDP/capita, PS, WPI, and UHC. However, the multiple linear regression analysis showed that there is no significant relationship between COVID-19 vaccine distribution and the countries' GDP/capita and that UHC is the most influential factor impacting COVID-19 vaccine distribution and accessibility. Discussion: The findings underscore the complex interplay between economic, political, and health system factors in shaping vaccine distribution patterns. To improve the accessibility to vaccines in future pandemics, Global Health Governance (GHG) and countries should consider working on three areas; enhance political stabilities in countries, separate the political power from decision-making at the global level and most importantly support countries to achieve UHC.

Mismanaged plastic waste and the environmental Kuznets curve: A quantile regression analysis.

The relationship between economic growth, governance, and environmental outcomes, particularly mismanaged plastic waste (MPW) leaking out to the ocean, has been a focal point of policy and academic debates. This study aims to understand the dynamics of income and control of corruption across different levels of MPW. Utilizing Quantile Regression models, we explore the generalized and quantile-specific relationships between the variables. The findings confirm the validity of the Environmental Kuznets Curve (EKC), revealing an initial increase in MPW with economic growth, followed by a decline after surpassing a specific economic threshold. However, the EKC is not validated for all quantiles and the shifting point may vary across the distribution. Moreover, control of corruption emerged as a significant factor in determining MPW levels, emphasizing its moderating role at the highest levels of mismanagement. This study underscores the need for synergizing economic strategies with robust environmental policies, guided by strong governance mechanisms.

Prediction of excess cement residues using a regression model to avoid peri-implant diseases: An in vitro study.

Dental implant restorations attached to cement can potentially result in peri-implant mucositis and peri-implantitis if cement residues are present. Effectively predicting and eliminating such dental cement residues is crucial for preventing complications. This study focuses on creating a regression model using the pixel values to predict the Excess Cement Residues (ECR) by employing an octagonal surface imaging approach. A model featuring gingival imitation, ten abutments, and ten crowns was created, and the cemented implants underwent thorough photographic and analytical assessment. The ECR was determined through two distinct approaches: the Computerized Planimetric Method (CPM) and the weighing method. Across ten implants in this in vitro study, ECR varied from 0.3 to 21 mg, with an average of 5.69 mg. The findings reveal a higher amount of ECR on the distal, mesiobuccal, and mesial sides. Utilizing Pearson's correlation, a coefficient value of r = 0.786 signifies a strong correlation between CPM and the weighing method. The regression model further aids in predicting ECR based on pixel values. The octagonal surface imaging approach not only vividly captures information about ECR in the implant cementation region but also emphasizes the feasibility of ImageJ as an effective tool for detecting ECR. The congruence between CPM and the weighing method results supports the application of the regression model for precise ECR prediction.

Determination of the maximum bioaccumulation capacity of various metals in leaves of two Tillandsia species.

Tillandsia species are plants from the Bromeliaceae family which display biomonitoring capacities in both active and passive modes. The bioaccumulation potential of Tillandsia aeranthos (Loisiel.) Desf. and Tillandsia bergeri Mez acclimated to Southern/Mediterranean Europe has never been studied. More generally, few studies have detailed the maximum accumulation potential of Tillandsia leaves through controlled experiments. The aim of this study is to evaluate the maximum accumulation values of seven metals (Co, Cu, Mn, Ni, Pb, Pt, and Zn) in T. aeranthos and T. bergeri leaves. Plants were immersed in different mono elemental metallic solutions of Co (II), Cu (II), Mn (II), Ni (II), Pb (II), Pt (IV), and Zn (II) ions at different concentrations. In addition, cocktail solutions of these seven metals at different concentrations were prepared to study the main differences and the potential selectivity between metals. After exposure, the content of these metals in the leaves were measured by inductively coupled plasma-optical emission spectrometry. Data sets were evaluated by a fitted regression hyperbola model and principal component analysis, maximum metal loading capacity, and thermodynamic affinity constant were determined. The results showed important differences between the two species, with T. bergeri demonstrating higher capacity and affinity for metals than T. aeranthos. Furthermore, between the seven metals, Pb and Ni showed higher enrichment factors (EF). T. bergeri might be a better bioaccumulator than T. aeranthos with marked selectivity for Pb and Ni, metals of concern in air quality biomonitoring.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

What can we learn about stress and sleep from COVID-19 pandemic-perspective from the theory of preventive stress management.

Background: The COVID-19 pandemic has presented unique challenges to individuals worldwide, with a significant focus on the impact on sleep. However, the precise mechanisms through which emotional and cognitive variables mediate this relationship remain unclear. To expand our comprehensive understanding of variables, the present study utilizes the Preventive Stress Management theory, to test the relationship between perceived social support and sleep quality, as well as the effect of perceived COVID-19 stress, hope, negative emotions and coping styles. Methods: Data were collected in March 2022 from 1,034 college students in two universities located in Liaoning Province, China, using an online survey platform regarding perceived social support, perceived COVID-19 stress, sleep quality, hope, negative emotions and coping styles. The moderated mediation model were conducted using Process macro program (Model 6) and the syntax in SPSS. Results: The results revealed perceived COVID-19 stress and negative emotions sequentially mediated the negative relationship between perceived social support and sleep quality. Furthermore, hope and coping styles were found to moderate the sequential mediating effect. Conclusion: The present study sheds light on the pathways that affect sleep quality among college students during the COVID-19 pandemic. Findings highlight the protective roles played by positive social and personal resources, such as perceived social support, hope, and effective coping styles, against sleep problems. These insights have important implications for the development of targeted interventions to improve sleep outcomes during this challenging time.

The effect of retirement on physical and mental health in China: a nonparametric fuzzy regression discontinuity study.

BACKGROUND: With the rapid aging of the domestic population, China has a strong incentive to increase the statutory retirement age. How retirement affects the health of the elderly is crucial to this policymaking. The health consequences of retirement have been debated greatly. This study aims to investigate the effects of retirement on physical and mental health among Chinese elderly people. METHODS: The data we use in this study comes from four waves (2011, 2013, 2015, and 2018) of the Harmonized China Health and Retirement Longitudinal Study (Harmonized CHARLS), a prospective cohort. We use the nonparametric fuzzy regression discontinuity design to estimate the effects of retirement on physical and mental health. We test the robustness of our results with respect to different bandwidths, kernel functions, and polynomial orders. We also explore the heterogeneity across gender and education. RESULTS: Results show that retirement has an insignificant effect on a series of physical and mental health outcomes, with and without adjusting several sociodemographic variables. Heterogeneity exists regarding gender and education. Although stratified analyses indicate that the transition from working to retirement leaves minimal effects on males and females, the effects go in the opposite direction. This finding holds for low-educated and high-educated groups for health outcomes including depression and cognitive function. Most of the results are stable with respect to different bandwidths, kernel functions, and polynomial orders. CONCLUSIONS: Our results suggest that it is possible to delay the statutory retirement age in China as retirement has insignificant effects on physical and mental health. However, further research is needed to assess the long-term effect of retirement on health.

HE4-based nomogram for predicting overall survival in patients with idiopathic pulmonary fibrosis: construction and validation.

Idiopathic pulmonary fibrosis (IPF) is a life-threatening interstitial lung disease. Identifying biomarkers for early diagnosis is of great clinical importance. The epididymis protein 4 (HE4) is important in the process of inflammation and fibrosis in the epididymis. Its prognostic value in IPF, however, has not been studied. The mRNA and protein levels of HE4 were used to determine the prognostic value in different patient cohorts. In this study, prognostic nomograms were generated based on the results of the cox regression analysis. We identified the HE4 protein level increased in IPF patients, but not the HE4 gene expression. The increased expression of HE4 correlated positively with a poor prognosis for patients with IPF. The HR and 95% CI were 2.62 (1.61-4.24) (p < 0.001) in the training set. We constructed a model based on the risk-score = 0.16222182 * HE4 + 0/0.37580659/1.05003609 (for GAP index 0-3/4-5/6-8) + (- 1.1183375). In both training and validation sets, high-risk patients had poor prognoses (HR: 3.49, 95%CI 2.10-5.80, p = 0.001) and higher likelihood of dying (HR: 6.00, 95%CI 2.04-17.67, p = 0.001). Analyses of calibration curves and decision curves suggest that the method is effective in predicting outcomes. Furthermore, a similar formulation was used in a protein-based model based on HE4 that also showed prognostic value when applied to IPF patients. Accordingly, HE4 is an independent poor prognosis factor, and it has the potential to predict IPF patient survival.

Stem moisture content prediction model for Larix olgensis based on beta regression.

To investigate the longitudinal variation patterns of sapwood, heartwood, bark and stem moisture content along the trunk of artificial Larix olgensis, we constructed mixed effect models of moisture content based on beta regression by combining the effects of sampling plot and sample trees. We used two sampling schemes to calibrate the model, without limiting the relative height (Scheme Ⅰ) and with a limiting height of less than 2 m (Scheme II). The results showed that sapwood and stem moisture content increased gradually along the trunk, heartwood moisture content decreased slightly and then increased along the trunk, and bark moisture content increased along the trunk and then levelled off before increasing. Relative height, height to crown base, stand area at breast height per hectare, age, and stand dominant height were main factors driving moisture content of L. olgensis. Scheme Ⅰ showed the stable prediction accuracy when randomly sampling moisture content measurements from 2-3 discs to calibrate the model, with the mean absolute percentage error (MAPE) of up to 7.2% for stem moisture content (randomly selected 2 discs), and the MAPE of up to 7.4%, 10.5% and 10.5% for sapwood, heartwood and bark moisture content (randomly selected 3 discs), respectively. Scheme Ⅱ was appropriate when sampling moisture content measurements from discs of 1.3 and 2 m height and the MAPE of sapwood, heartwood, bark and stem moisture content reached 7.8%, 11.0%, 10.4% and 7.1%, respectively. The prediction accuracies of all mixed effect beta regression models were better than the base model. The two-level mixed effect beta regression models, considering both plot effect and tree effect, would be suitable for predicting moisture content of each part of L. olgensis well.

Can predictive factors determine the time to treatment initiation for oral and oropharyngeal cancer? A classification and regression tree analysis.

This ecological study aimed to identify the factors with the greatest power to discriminate the proportion of oral and oropharyngeal cancer (OOC) records with time to treatment initiation (TTI) within 30 days of diagnosis in Brazilian municipalities. A descriptive analysis was performed on the variables grouped into five dimensions related to patient characteristics, access to health services, support for cancer diagnosis, human resources, and socioeconomic characteristics of 3,218 Brazilian municipalities that registered at least one case of OOC in 2019. The Classification and Regression Trees (CART) technique was adopted to identify the explanatory variables with greater discriminatory power for the TTI response variable. There was a higher median percentage of records in the age group of 60 years or older. The median percentage of records with stage III and IV of the disease was 46.97%, and of records with chemotherapy, radiation, or both as the first treatment was 50%. The median percentage of people with private dental and health insurance was low. Up to 75% had no cancer diagnostic support services, and up to 50% of the municipalities had no specialist dentists. Most municipalities (49.4%) started treatment after more than 30 days. In the CART analysis, treatment with chemotherapy, radiotherapy, or both explained the highest TTI in all municipalities, and it was the most relevant for predicting TTI. The final model also included anatomical sites in the oral cavity and oropharynx and the number of computed tomography services per 100,000. There is a need to expand the availability of oncology services and human resources specialized in diagnosing and treating OOC in Brazilian municipalities for a timely TTI of OOC.

Out-of-Pocket Costs and Payments in Autologous and Implant-Based Breast Reconstruction: A Nationwide Analysis.

BACKGROUND: Many factors influence a patient's decision to undergo autologous versus implant-based breast reconstruction, including medical, social, and financial considerations. This study aims to investigate differences in out-of-pocket and total spending for patients undergoing autologous and implant-based breast reconstruction. METHODS: The IBM MarketScan Commercial Databases were queried to extract all patients who underwent inpatient autologous or implant-based breast reconstruction from 2017 to 2021. Financial variables included gross payments to the provider (facility and/or physician) and out-of-pocket costs (total of coinsurance, deductible, and copayments). Univariate regressions assessed differences between autologous and implant-based reconstruction procedures. Mixed-effects linear regression was used to analyze parametric contributions to total gross and out-of-pocket costs. RESULTS: The sample identified 2079 autologous breast reconstruction and 1475 implant-based breast reconstruction episodes. Median out-of-pocket costs were significantly higher for autologous reconstruction than implant-based reconstruction ($597 vs $250, P < 0.001) as were total payments ($63,667 vs $31,472, P < 0.001). Type of insurance plan and region contributed to variable out-of-pocket costs (P < 0.001). Regression analysis revealed that autologous reconstruction contributes significantly to increasing out-of-pocket costs (B = $597, P = 0.025) and increasing total costs (B = $74,507, P = 0.006). CONCLUSION: The US national data demonstrate that autologous breast reconstruction has higher out-of-pocket costs and higher gross payments than implant-based reconstruction. More study is needed to determine the extent to which these financial differences affect patient decision-making.

Improving the robustness of the Sequentially Optimized Reconstruction Strategy (SORS) for visual field testing.

Perimetry, or visual field test, estimates differential light sensitivity thresholds across many locations in the visual field (e.g., 54 locations in the 24-2 grid). Recent developments have shown that an entire visual field may be relatively accurately reconstructed from measurements of a subset of these locations using a linear regression model. Here, we show that incorporating a dimensionality reduction layer can improve the robustness of this reconstruction. Specifically, we propose to use principal component analysis to transform the training dataset to a lower dimensional representation and then use this representation to reconstruct the visual field. We named our new reconstruction method the transformed-target principal component regression (TTPCR). When trained on a large dataset, our new method yielded results comparable with the original linear regression method, demonstrating that there is no underfitting associated with parameter reduction. However, when trained on a small dataset, our new method used on average 22% fewer trials to reach the same error. Our results suggest that dimensionality reduction techniques can improve the robustness of visual field testing reconstruction algorithms.

Predictors of maternal health services uptake in West African region: a multilevel multinomial regression analysis of demographic health survey reports.

BACKGROUND: Pursuant to studies, receiving the three key maternal health services (Antenatal Care, Skilled Delivery Service, and Postnatal Care) in a continuum could prevent 71% of global maternal deaths. Despite the Western African region being known for its high maternal death and poor access to maternal health services, there is a dearth of studies that delve into the spectrum of maternal health services uptake. Hence, this study aimed to assess the level and predictors of partial and adequate utilization of health services in a single analytical model using the most recent Demographic and Health Survey (DHS) data (2013-2021). METHODS: This study was based on the appended women's (IR) file of twelve West African countries. STATA software version 16 was used to analyze a weighted sample of 89,504 women aged 15-49 years. A composite index of maternal health service utilization has been created by combining three key health services and categorizing them into 'no', 'partial', or 'adequate' use. A multilevel multivariable multinomial logistic regression analysis was carried out to examine the effects of each predictor on the level of service utilization. The degree of association was reported using the adjusted relative risk ratio (aRRR) with a corresponding 95% confidence interval, and statistical significance was declared at p < 0.05. RESULTS: 66.4% (95% CI: 64.9, 67.7) and 23.8% (95% CI: 23.3, 24.2) of women used maternal health services partially and adequately, respectively. Togo has the highest proportion of women getting adequate health care in the region, at 56.7%, while Nigeria has the lowest proportion, at 11%. Maternal education, residence, wealth index, parity, media exposure (to radio and television), enrolment in health insurance schemes, attitude towards wife beating, and autonomy in decision-making were identified as significant predictors of partial and adequate maternal health service uptake. CONCLUSION: The uptake of adequate maternal health services in the region was found to be low. Stakeholders should plan for and implement interventions that increase women's autonomy. Program planners and healthcare providers should give due emphasis to those women with no formal education and from low-income families. The government and the private sectors need to collaborate to improve media access and increase public enrolment in health insurance schemes.