Regression Model In Order Research Articles

대학 재학생들의 중도탈락 패턴 분석 및 이탈 예측: H 대학교를 중심으로

This study was conducted to analyze dropout patterns of H University students and construct a prediction model for churning of students. Keeping a high enrollment rate of students in a local university is very important for its sustainability and survival, which forced us to develop a good explainable predictive model for dropout probabilities of each student. We applied a mixed effect logistic regression model in order to predict a dropout probability based on 11 explanatory variables such as admission type of processes, first semester grades, total admission scores, category of high schools, and departments of application, etc. The constructed model tells us that department of application is a significant variable as a random effect on which fixed effects for total admission scores and first semester grades should be interpreted differently. The main finding is that in each department the proposed model was able to discriminate a high-probabilistic dropout student although they have showed a good performance in total admission scores and first semester grades. It is highly expected that our model can be effectively applied to manage the dropout rates and enhance the enrollment rates of the students.

Analysing Spatio-temporal Dynamics of Urban Sprawl, Evolving Pattern of Urban Landscape and Driving Forces of Urban Growth: A Case of Varanasi Planning Region, India

Urban sprawl is at the centre of contemporary urban debates and is very often criticised for its negative social and environmental externalities. Building on geo-spatial methodology and field observations, this study sets out to develop a holistic approach to understanding urban sprawl through a particular focus on a rapidly growing metropolitan urban agglomeration of northern India, that is, Varanasi. Following an analysis of principal urban growth modes through landscape expansion index and changing patterns of urban landscape through landscape metrics, this research refers to a geo-spatially grounded logistic regression model in order to shed light on the dynamic impact of multiple growth factors on the trajectory of urban sprawl in the study area. Moreover, in order to address the evolving nature of urban sprawl in Varanasi and its immediate surroundings this study focuses on a temporal span of 20 years, encompassing two consecutive decades of the 21st century (2001–2011 and 2011–2021). The research concludes by paving a platform for an amalgamation of the findings from different geo-spatial metrics as well as by corroborating the principal findings with the propositions of a number of contemporary hypotheses concerning the spatial growth of urbanised territories.

Fault detection for district heating substations: Beyond three-sigma approaches

The topic of this paper is fault detection for district heating substations, which is an important enabler for the transition towards fourth-generation district heating systems. Classical fault detection approaches are often based on anomaly detection, commonly making the implicit assumption that the errors between the measurements and the predictions made by the baseline model are i.i.d. and following an underlying Gaussian distribution. Our analysis shows that this does not hold up in the field, showing clear seasonality in the error over time. We propose to replace the Gaussian error model by a quantile regression model in order to provide a more nuanced fault threshold, conditioned on time and other input variables. Additionally, we observed that properly training the baseline model comes with its own challenges due to this time dependency, which we propose to resolve by employing an ensemble of models, trained on different periods of time. We demonstrate our method on unlabelled operational data obtained from a Swedish district heating operator to illustrate its use in the field. In addition, we validate it on labelled data from our residential lab setup, testing a variety of common faults.

Oil jump tail risk as a driver of inflation dynamics

In this paper, we look at the role of various oil jump tail risk measures as drivers of both U.S. headline and core inflation. Those measures are first computed from high-frequency oil future prices and are then introduced into standard regression models in order to (i) assess in-sample determinants of inflation, (ii) assess overtime the evolution of inflation drivers, (iii) estimate impulse response functions and (iv) forecast inflation out-of-sample for various horizons. Empirical results suggest that oil jump tail risk measures contain useful information to describe inflation dynamics, generally leading to upward inflationary pressures. Even after controlling from standard variables involved in a Phillips curve, goodness-of-fit measures show evidence of a gain, in particular for headline inflation. Overall, we observe that oil jump tail risk measures are contributing more to inflation dynamics since the Covid-19 crisis.

Implementation of a patient reminder system in Haiti in a socio-political crisis context: evaluation of outcomes

BackgroundIn Haiti, patient’s adherence to treatment and compliance with medical appointments are very challenging due to different local factors. We aimed to assess the effectiveness of a reminder system implemented in health facilities in Haiti in a context of socio-political crisis.MethodsWe used appointment data from patients aged 15 years and older between January 2021 and November 2023 from four healthcare centers in the Port-au-Prince metropolitan area. We performed descriptive analysis, crossing covariates with appointment attendance. We performed Pearson’s Chi-squared test, and multivariate regression analysis using a mixed-effect logistic regression model in order to explore the association between sending reminders and appointment attendance, with and without adjustment for other patient-level covariates.ResultsA total of 14 108 appointments were registered on the reminder systems, with 2 479 (17.6%) attendances. Among those to whom reminders were sent, the number of attendances was 167 (17.4%) for email recipients only, 199 (36.7%) for SMS recipients only, and 19 (42.2%) for both SMS and email recipients – versus 2 094 (16.7%) for non-reminders. After adjusting on all other covariates, we found that patients to whom a reminder was sent via email (aOR: 1.45; CI: 1.08, 1.94), SMS (aOR: 2.95; CI: 2.41, 3.60), and both SMS and email (aOR: 2.86, CI: 1.37, 5.96) were more likely to show up on their appointment day compared to those who did not receive any reminder. Other socio-demographic factors such as being 50 years and older (aOR: 1.31; CI: 1.10, 1.56) compared to under 30 years, living as couple (aOR: 1.23; CI: 1.10, 1.37), and not having children (aOR: 1.21; CI: 1.07, 1.37) were significantly associated with appointment attendance.ConclusionsOur study suggests that patient reminder systems may be used to reduce non-attendance in Haiti, even in a context of socio-political crisis.

Digital financial inclusion and household financial vulnerability: An empirical analysis of rural and urban disparities in China

In recent times, a notable increase in the leverage ratios among numerous households across China has been witnessed, culminating in heightened household financial vulnerability. Concurrently, the sphere of digital inclusive finance has witnessed rapid advancement, establishing itself as a crucial mechanism for Chinese households to counteract financial risk shocks. This research article meticulously constructs an ordered regression model, anchored in micro-level data from household surveys, to delve into the influence and operative mechanisms of digital inclusive finance on the vulnerability of household finances. Empirical findings from this study robustly indicate that the evolution of digital inclusive finance significantly mitigates the household financial vulnerability. A thorough mechanism analysis reveals that digital inclusive finance primarily curtails household financial vulnerability through several avenues: it notably enhances financial literacy, augments the income derived from household financial assets, and elevates contributions to commercial insurance. Intriguingly, a heterogeneity analysis underscores that the impact of digital inclusive finance is more pronounced in reducing financial vulnerability amongst households registered in rural areas and those with lower income levels. This article contributes to the expansion of the theoretical framework concerning household financial vulnerability, offering insightful guidance and policy implications for addressing financial vulnerability concerns and forestalling macro-financial risks.

Safety evaluation via conflict classification during automated shuttle bus service operations

The widespread adoption of Connected and Automated Vehicles (CAVs) is being propelled, not only in the realm of private vehicles but also within transit systems. This development serves to enhance urban transport activities, rendering transportation more appealing to passengers. The present study aims to identify and examine the safety effects of testing different operational speed shuttle bus services in various future mobility conditions. To investigate impacts of autonomous shuttle bus services and to further examine their operational speed, the microscopic simulation method was performed. Specifically, four sets of simulation scenarios were comprised: a baseline scenario representing the current conditions and three operational speed scenarios (15 km/h, 30 km/h and 45 km/h) for an autonomous shuttle service. Each one of these sets included eleven CAV market penetration rates (MPRs) of CAVs of the general traffic (ranging from 0 to 100% in 10% increments). By analyzing the trajectory data extracted from microsimulation, traffic conflicts were identified and further analyzed by developing Mixed-Effects Multinomial Logit Regression models (ME-MLMs) in order to associate conflict type taking into account network characteristics as well as traffic conditions. Several aspects were determined as statistical significant parameters influencing type of conflict. The analysis yielded several significant findings that provide quantitative measurements and assessments of the effects observed, enabling a better understanding of the safety implications associated with the widespread adoption of automated services.

Dietary inflammatory index and lower glomerular filtration rate in Mexican adults

We hypothesized that higher scores on the dietary inflammatory index (DII) would be associated with a lower glomerular filtration rate (GFR). This cross-sectional study included 2098 participants from Mexican Teachers Cohort Study, the Health Workers Cohort Study, and the Comitán Study belonging to the RenMex consortium. Energy-adjusted DII scores were estimated using a semi-quantitative food frequency questionnaire (FFQ). eGFR was estimated by the CKD Epidemiology Collaboration equation. Quantile regression models and ordered regression models were estimated to assess the associations of interest. Median age of study participants was 47 years, median eGFR was 102.9 mL/min/1.73m2, and the median energy-adjusted DII was 0.89 (range, -2.25, +4.86). The median eGFR was lower in participants in the highest percentile of DII compared to those in the lowest percentile (103.8 vs 101.4). We found that continuous and categorical energy-adjusted DII scores were associated with lower eGFR, especially at the lower percentiles. In adjusted ordered logistic regression, we found that the highest DII category was associated with 1.80 times the odds of belonging to the mildly decreased eGFR category or moderately decreased eGFR category compared lowest DII category (OR: 1.80, 95%CI 1.35, 2.40). A high DII score was associated with a lower eGFR among the Mexican population. Additional studies are crucial to validate these findings and explore potential strategies to reduce the consumption of pro-inflammatory foods as a preventive approach for chronic kidney disease (CKD).

Government subsidies and corporate environmental, social and governance performance: Evidence from companies of China

AbstractEnvironmental, social, and governance (ESG) performance is crucial for companies to attain sustainable development, which is a key reference for assessing the value and growth potential of a company. Government subsidies can provide incentives for companies to prioritize environmental preservation, meet their social duties, and improve their governance performance. This paper empirically examines the effects and mechanisms of government subsidies on corporate ESG performance, using an Ologit multiple ordered regression model based on data from Chinese listed companies. We find that not only do total government subsidies significantly improve firms' ESG performance, but both environmental and non‐environmental subsidies also have the similar effect, albeit with different impact mechanisms. The analysis of the mechanism suggests that government subsidies can enhance corporate ESG performance by promoting green innovation, alleviating financing constraints, increasing charitable donations, and attracting social attention. This paper holds significant practical value as it presents empirical findings as a basis for reforming China's subsidy policies, showcasing the actual impact of diverse subsidy policies and the heterogeneity.

The features associated with mammography-occult MRI-detected newly diagnosed breast cancer analysed by comparing machine learning models with a logistic regression model.

To compare machine learning (ML) models with logistic regression model in order to identify the optimal factors associated with mammography-occult (i.e. false-negative mammographic findings) magnetic resonance imaging (MRI)-detected newly diagnosed breast cancer (BC). The present single-centre retrospective study included consecutive women with BC who underwent mammography and MRI (no more than 45days apart) for breast cancer between January 2018 and May 2023. Various ML algorithms and binary logistic regression analysis were utilized to extract features linked to mammography-occult BC. These features were subsequently employed to create different models. The predictive value of these models was assessed using receiver operating characteristic curve analysis. This study included 1957 malignant lesions from 1914 patients, with an average age of 51.64 ± 9.92years and a range of 20-86years. Among these lesions, there were 485 mammography-occult BCs. The optimal features of mammography-occult BC included calcification status, tumour size, mammographic density, age, lesion enhancement type on MRI, and histological type. Among the different ML models (ANN, L1-LR, RF, and SVM) and the LR-based combined model, the ANN model with RF features was found to be the optimal model. It demonstrated the best discriminative performance in predicting mammography false- negative findings, with an AUC of 0.912, an accuracy of 86.90%, a sensitivity of 85.85%, and a specificity of 84.18%. Mammography-occult MRI-detected breast cancers have features that should be considered when performing breast MRI to improve the detection rate for breast cancer and aid in clinician management.

Southern Horse Mackerel (Trachurus trachurus) Spatio-Temporal Distribution Patterns Based on Fine-Scale Resolution Data

In this study, the distribution patterns of southern horse mackerel are examined using commercial fine-scale resolution data. Using landings by size category and VMS data from the Portuguese commercial bottom-trawl fishery, which consistently targets horse mackerel, this study provides a comprehensive analysis of horse mackerel age distributions spanning a decade (2010–2020). Importantly, this study addresses potential biases in commercial effort data and establishes the usefulness of commercial bottom-trawl gear as a suitable method for sampling and evaluating southern horse mackerel stock dynamics. Ordered regression models were applied to allow for the modelling of the distribution of multiple age categories and investigate spatio-temporal migrations off the Portuguese coast. Southern horse mackerel show a widespread age distribution range and stable abundance with indications of seasonal and spatial patterns in the distribution of specific age groups. The insights derived from this research contribute valuable knowledge for understanding the dynamics and distribution patterns of fish populations.

Linguistic analysis of health anxiety during the COVID-19 pandemic.

Health anxiety, which is defined as fear of having or contracting serious physical illness, is particularly salient in light of the COVID-19 pandemic. We conducted a mixed methods study in which 578 narrative samples were analyzed using Linguistic Inquiry and Word Count (LIWC) software to determine linguistic markers from six LIWC categories relevant to cognitive-behavioral features of health anxiety. Broad linguistic predictors were analyzed through three backward elimination regression models in order to inform subcategory predictors of each area of health anxiety. Thus, both broad and specific linguistic predictors of general health anxiety, virus-relevant body vigilance, and fears of viral contamination were examined. Greater use of affective category words in written narratives predicted general health anxiety, as well as body vigilance and viral contamination fears. These findings represent the first direct demonstration of linguistic analysis of health anxiety and provide nuanced information about the nature and etiology of health anxiety.

The Impact of Commercial-Industry Development of Urban Vitality: A Study on the Central Urban Area of Guangzhou Using Multisource Data

Urban commercial centers play a critical role in the development of cities, and it is of significant relevance to research the influencing variables of the urban vitality of commercial centers to improve the quality of urban commercial centers. This study employs big data to construct a multiple linear regression model in order to uncover the spatial-distribution characteristics of urban vitality and commercial sectors in commercial centers within the primary urban region of Guangzhou. The findings indicate that the commercial sectors of life, business, finance, and leisure have a substantial influence on the fluctuation of pedestrian flow in commercial centers throughout the day. Conversely, public service commercial sectors do not have a significant impact on pedestrian flow. Furthermore, the effect of various commercial sectors on the vibrancy of urban commercial centers varies, and their performance differs on weekdays and holidays. Additionally, the level of integration among commercial sectors affects the vitality of the city’s commercial space. This research presents empirical facts that can be used to optimize the logical allocation of urban commercial resources in urban planning.

Oxidative balance score was negatively associated with the risk of metabolic syndrome, metabolic syndrome severity, and all-cause mortality of patients with metabolic syndrome.

The oxidative balance score (OBS), an encompassing scoring mechanism for assessing oxidative stress, is formulated based on nutritional and lifestyle components. The emergence of metabolic syndrome (MetS) is intricately linked to oxidative stress. Nonetheless, the correlation between OBS and MetS displays variability within distinct cohorts. We worked on the relationships between OBS and the risk of MetS, MetS severity, and all-cause mortality of MetS patients. A total of 11,171 adult participants were collected from the U.S. National Health Examination Survey (NHANES) 2007-2018. Employing survey-weighted logistic models, we evaluated the relationship between OBS and MetS risk. Furthermore, survey-weighted linear models were utilized to investigate the connection between OBS and MetS severity. Among the participants, 3,621 individuals had their survival status recorded, allowing us to employ Cox proportional hazards regression models in order to ascertain the association between OBS and the all-cause mortality within the subset of individuals with MetS. The OBS (where a higher OBS signified an increased prevalence of anti- or pro-oxidant exposures) weighed the 20 factors, while the MetS severity score weighed the five factors. After multivariable adjustment, individuals with elevated OBS were found to exhibit a decreased susceptibility to MetS [odds ratio (OR) 0.95; 95% CI 0.94-0.96]. The adjusted OR was 0.42 (95% CI 0.33-0.53) for MetS risk in the fourth OBS quartile compared with those in the first OBS quartile (P for trend < 0.001). A one-unit increase in OBS was linked to a 3% reduction in MetS severity score by 3% (mean difference, -0.03; 95% CI, -0.04 to -0.03). Moreover, increased OBS correlated with decreased hazard of all-cause mortality risk among MetS subjects (adjusted hazard ratio, 0.95; 95% CI, 0.93-0.98). These associations retained their strength even subsequent to the introduction of sensitivity analyses. There existed a statistically significant negative correlation between diet/lifestyle OBS and both MetS risk as well as MetS severity. An inverse correlation was observed between OBS and the susceptibility to MetS, MetS severity, and all-cause mortality of MetS patients. Health outcomes for MetS patients were positively related to antioxidant diets and lifestyles.

LGBTQ+ related curricular activities in Brazilian Dental Schools: Do institutional and/or deans' profiles matter?

The aim of this cross-sectional study was to investigate whether the characteristics of dental schools and the profiles of their deans were associated with curricular activities related to LGBTQ+ in Brazilian dental schools. A survey was mailed to the deans of all Brazilian dental schools for the purpose of assessing the development of pedagogical activities focused on the topic of oral health of the LGBTQ+ population, and the characteristics of the institutions and profiles of their deans. The data collected were analyzed with hierarchical multiple logistic regression models in order to analyze associations between variables of interest. Of the deans approached, 156 (response rate of approximately 32.4%) completed and returned the survey. The majority of dental schools (67.3%) of the sample were private institutions and 39% of them developed pedagogical activities directed toward the LGBTQ+. This percentage was much lower in public institutions (18%). The results of the hierarchical multiple logistic regression demonstrated that deans who attributed high relevance to the theme "oral health of the LGBTQ+ population" were more likely to develop pedagogical activities directed toward the LGBTQ+ population (odds ratio=4.74; 95% confidence interval: 1.65-13.48), than those who attributed low or little importance to this topic (p<0.05). A low percentage of activities aimed at the LGBTQ+ population was observed in the sample of Brazilian dental schools. Deans must be aware of the importance of the topic and be motivated to implement pedagogical activities for this population.

Design and two step process optimization of a reactive distillation column for improving production amount of ethyl acetate and water

In this study, two step optimization was performed for operating conditions of a reactive distillation (RD) column using simulation outputs. The effects of the operating parameters of the RD column, on the ethyl acetate (EtAc) amount as a result of the reaction between ethanol (EtOH) and acetic acid (HAc) were assessed. The response surface methodology (RSM) was applied to determine the optimum operating conditions for the RD column. The two step experimental design used in RSM was created with the Design Expert (DE) program. The Plackett-Burman (PB) design was used to determine the most important parameters affecting on the designed RD column for production of EtAc amount. The most important parameters were determined as HAc feed rate (kmol/h), EtOH feed rate (kmol/h), reflux ratio and temperature (°C). After PB design Central Composite Design (CCD) was applied to determine the optimum operating conditions of these most important parameters for the RD column. ANOVA was used to investigate the interaction between the response and independent variables with the obtained data and 3 dimensional (3D) plots created by the program were interpreted. A second order regression model according to the independent variables for the EtAc amount (response) was developed with DE program. The optimum operating conditions for the designed RD column were obtained as HAc feed rate 149.86 kmol/h, EtOH feed rate 99.99 kmol/h, feed temperature 70 °C and reflux ratio 2.50. The production of EtAc amount which determined with model coefficients and recommended by the program were compared and relative errors between the model results and the predicted values were less than 1%. The obtained results were showed that RSM may be a suitable method for optimization of operating parameters in RD columns.

Petrophysical Relationship between Soil Moisture Content and Dielectric Permittivity for Wastewater Landfill Contaminated in Cherang Hangus Soil Using GPR

This study focuses on the dielectric permittivity of Cherang Hangus soil, a laterite type known for its water retention and landfill liner applications. By leveraging GPR data alongside empirical models, researchers aim to understand how moisture content influences dielectric permittivity, which is critical for managing wastewater migration in various soil types. The methodology involves simulating landfill conditions using a concrete tank filled with Cherang Hangus soil and solid waste, allowing for detailed monitoring of moisture and GPR scans. The study employs various regression models to predict dielectric permittivity based on moisture content, including linear, logarithmic, and polynomial approaches. Results indicate that more complex models, particularly the third-order polynomial, provide better accuracy in capturing the relationship between moisture content and dielectric permittivity, as evidenced by high R² 0.9883 for 3rd order regression model for Cherang Hangus. The findings underscore the significance of soil composition in predicting dielectric permittivity, highlighting that the high gravel content in Cherang Hangus soil affects moisture retention and, consequently, the accuracy of empirical models.

Random State Initialized Logistic Regression for Improved Heart Attack Prediction

One of the primary causes of death in Indonesia is heart attacks. Therefore, an effective method of pre-diction is required to determine whether a patient is experiencing a heart attack. One efficient approach is to use machine learning models. However, it is still rare to find machine learning models that have good performance in predicting heart attacks. This study aims to develop a machine learning model on Logistic Regression algorithm in predicting heart attack. Logistic Regression is one of the machine learning meth-ods that can be used to study the relationship between a binary response variable [0,1] and a set of pre-dictor variables, and can be used directly to calculate probabilities. In this study, a random state is ini-tialized in the Logistic Regression model in order to stabilize the training of the machine learning model and increase the precision of the proposed method. The results of this study show that the proposed model can be a method that has good performance in predicting heart attack disease.

Farmers' adoption of agriculture green production technologies: perceived value or policy-driven?

Understanding the underlying reasons for farmers' AGPT adoption in the context of resource environment tightening and agricultural carbon reduction has become crucial to promote agriculture sustainable development. This paper uses multiple ordered regression model and probit model to test the impact of farmers' perceived value on their adoption of agricultural green production technologies (AGPT) by using the first-hand data from 613 rice farmers in Hunan, and explores the effect and mechanism of policy subsidies and market incentives on the adoption of AGPT, and investigates the differences in the impact of perceived value on the adoption of green technologies in different production stages. The results showed that, (1)farmers' AGPT adoption is significantly affected by their perceived value, in which perceived benefits significantly promotes farmers' AGPT adoption, while the perceived risks is the opposite. But farmers' AGPT adoption in different production stages is influenced by different perceived value. The adoption of soil testing and fertilization technology (STFT) in the pre-production stage is significantly influenced by the perceived benefits; adoption of green pest control technology (GPCT) in the mid-production stage is significantly influenced by both the perceived benefits and the perceived risks; and adoption of straw return technology (SRT) in the post-production stage is significantly influenced by the perceived risks. (2) Farmers' adoption of green technologies can be encouraged by policy subsidies, which also have the moderating effect of decreasing the negative influence of perceived risks on farmers' adoption behaviour. (3) Further analysis reveals that market incentives can compensate for the limitations of policy subsidies, greatly promote farmers' adoption of AGPT, and regulate farmers' perceived risks and perceived benefits to encourage them to use green technologies. Consequently, the Government should actively publicize and organize training on agriculture green technologies, and provide diversified subsidy programmes for different green technologies. And governments should also improve the quality certification system and the market price mechanism for agricultural products, so as to help farmers adopting green technologies to achieve an increase in their income.

Childhood immunization uptake determinants in Kinshasa, Democratic Republic of the Congo: ordered regressions to assess timely infant vaccines administered at birth and 6-weeks

BackgroundDespite global efforts to reduce preventable childhood illness by distributing infant vaccines, immunization coverage in sub-Saharan African settings remains low. Further, timely administration of vaccines at birth—tuberculosis (Bacille Calmette–Guérin [BCG]) and polio (OPV0)—remains inconsistent. As countries such as Democratic Republic of the Congo (DRC) prepare to add yet another birth-dose vaccine to their immunization schedule, this study aims to improve current and future birth-dose immunization coverage by understanding the determinants of infants receiving vaccinations within the national timeframe.MethodsThe study used two ordered regression models to assess barriers to timely BCG and first round of the hepatitis B (HepB3) immunization series across multiple time points using the Andersen Behavioral Model to conceptualize determinants at various levels. The assessment leveraged survey data collected during a continuous quality improvement study (NCT03048669) conducted in 105 maternity centers throughout Kinshasa Province, DRC. The final sample included 2398 (BCG analysis) and 2268 (HepB3 analysis) women-infant dyads living with HIV.ResultsBetween 2016 and 2020, 1981 infants (82.6%) received the BCG vaccine, and 1551 (68.4%) received the first dose of HepB3 vaccine. Of those who received the BCG vaccine, 26.3%, 43.5%, and 12.8% received BCG within 24 h, between one and seven days, and between one and 14 weeks, respectively. Of infants who received the HepB3 vaccine, 22.4% received it within six weeks, and 46% between six and 14 weeks of life. Many factors were positively associated with BCG uptake, including higher maternal education, household wealth, higher facility general readiness score, and religious-affiliated facility ownership. The factors influencing HepB3 uptake included older maternal age, higher education level, household wealth, transport by taxi to a facility, higher facility general and immunization readiness scores, and religious-affiliated facility ownership.ConclusionsThis study demonstrated that the study participants’ uptake of vaccines was consistent with the country average, but not in a timely manner. Various factors were associated with timely uptake of BCG and HepB3 vaccines. These findings suggest that investment to strengthen the vaccine delivery system might improve timely vaccine uptake and equity in vaccine coverage.