Investigating temporal variations in pedestrian crossing behavior at semi-controlled crosswalks: A Bayesian multilevel modeling approach

In sub-Saharan Africa, 72% of pregnant women received an antenatal care visit at least once in their pregnancy period. Ethiopia has one of the highest rates of maternal mortality in sub-Saharan African countries. So, this high maternal mortality levels remain a major public health problem. According to EDHS, 2016, the antenatal care (ANC), delivery care (DC), and postnatal care (PNC) were 62%, 73%, and 13%, respectively, indicating that ANC is in a low level. The main objective of this study was to examine the factors that affect the utilization of antenatal care services in Ethiopia using Bayesian multilevel logistic regression models. The data used for this study comes from the 2016 Ethiopian Demographic and Health Survey which was conducted by the Central Statistical Agency (CSA). The statistical method of data analysis used for this study is the Bayesian multilevel binary logistic regression model in general and the Bayesian multilevel logistic regression for the random coefficient model in particular. The convergences of parameters are estimated by using Markov chain Monte-Carlo (MCMC) using SPSS and MLwiN software. The descriptive result revealed that out of the 7171 women who are supposed to use ANC services, 2479 (34.6%) women were not receiving ANC services, while 4692 (65.4%) women were receiving ANC services. Moreover, women in the Somali and Afar regions are the least users of ANC. Using the Bayesian multilevel binary logistic regression of random coefficient model factors, place of residence, religion, educational attainment of women, husband educational level, employment status of husband, beat, household wealth index, and birth order were found to be the significant factors for usage of ANC. Regional variation in the usage of ANC was significant.

The prospective cohort study PROTECT is the largest study in pediatric ulcerative colitis (UC) with standardized treatments, providing valuable data for predicting clinical outcomes. PROTECT and previous studies have identified characteristics associated with clinical outcomes. In this study, we aimed to compare predictive modeling between Bayesian analysis including machine learning and frequentist analysis. The key outcomes for this analysis were week 4, 12 and 52 corticosteroid (CS)-free remission following standardized treatment from diagnosis. We developed predictive modeling with multivariable Bayesian logistic regression (BLR), Bayesian additive regression trees (BART) and frequentist logistic regression (FLR). The effect estimate of each risk factor was estimated and compared between the BLR and FLR models. The predictive performance of the models was assessed including area under curve (AUC) of the receiver operating characteristic (ROC) curve. Ten-fold cross-validation was performed for internal validation of the models. The estimation contained 95% credible (or confidence) interval (CI). The statistically significant associations between the risk factors and early or late outcomes were consistent between all BLR and FLR models. The model performance was similar while BLR and BART models had narrower credible intervals of AUCs. To predict week 4 CS-free remission, the BLR model had AUC of 0.69 (95% CI 0.67-0.70), the BART model had AUC of 0.70 (0.67-0.72), and the FLR had AUC of 0.70 (0.65-0.76). To predict week 12 CS-free remission, the BLR model had AUC of 0.78 (0.77-0.79), the BART model had AUC of 0.78 (0.77-0.79), and the FLR model had AUC of 0.79 (0.74-0.83). To predict week 52 CS-free remission, the BLR model had AUC of 0.69 (0.68-0.70), the BART model had AUC of 0.69 (0.67-0.70), and the FLR model had AUC of 0.69 (0.64-0.74). The BART model identified nonlinear associations. BLR and BART models had intuitive interpretation on interval estimation, better precision in estimating the AUC and can be alternatives for predicting clinical outcomes in pediatric patients with UC. BART model can estimate nonlinear nonparametric association.

BackgroundAs a global public health problem, anemia affects more than 400 million women of reproductive age worldwide, mostly in Africa and India. In the DRC, the prevalence of anemia has decreased slightly from 52.9% in 2007, to 46.4% in 2012 and 42.4% in 2019. However, there is considerable regional variation in its distribution. The aim of this study is to determine the factors contributing to anemia in women of reproductive age and to explore its spatial distribution in the DRC.MethodsBased on the Bayesian Multilevel Spatial Ordinal Logistic Regression Model, we used the 2013 Democratic Republic of Congo Demographic and Health Survey (DHS-DRC II) data to investigate individual and environmental characteristics contributing to the development of anemia in women of reproductive age and the mapping of anemia in terms of residual spatial effects.ResultsAge, pregnancy status, body mass index, education level, current breastfeeding, current marital status, contraceptive and insecticide-treated net use, source of drinking water supply and toilet/latrine use including the province of residence were the factors contributing to anemia in women of reproductive age in DRC. With Global Moran's I = -0.00279, p-value ≥ 0.05, the spatial distribution of anemia in women of reproductive age in DRC results from random spatial processes. Thus, the observed spatial pattern is completely random.ConclusionThe Bayesian Multilevel Spatial Ordinal Logistic Regression statistical model is able to adjust for risk and spatial factors of anemia in women of reproductive age in DRC highlighting the combined role of individual and environmental factors in the development of anemia in DRC.

BackgroundConsiderable progress towards controlling malaria has been made in Papua New Guinea through the national malaria control programme’s free distribution of long-lasting insecticidal nets, improved diagnosis with rapid diagnostic tests and improved access to artemisinin combination therapy. Predictive prevalence maps can help to inform targeted interventions and monitor changes in malaria epidemiology over time as control efforts continue. This study aims to compare the predictive performance of prevalence maps generated using Bayesian decision network (BDN) models and multilevel logistic regression models (a type of generalized linear model, GLM) in terms of malaria spatial risk prediction accuracy.MethodsMultilevel logistic regression models and BDN models were developed using 2010/2011 malaria prevalence survey data collected from 77 randomly selected villages to determine associations of Plasmodium falciparum and Plasmodium vivax prevalence with precipitation, temperature, elevation, slope (terrain aspect), enhanced vegetation index and distance to the coast. Predictive performance of multilevel logistic regression and BDN models were compared by cross-validation methods.ResultsPrevalence of P. falciparum, based on results obtained from GLMs was significantly associated with precipitation during the 3 driest months of the year, June to August (β = 0.015; 95% CI = 0.01–0.03), whereas P. vivax infection was associated with elevation (β = − 0.26; 95% CI = − 0.38 to − 3.04), precipitation during the 3 driest months of the year (β = 0.01; 95% CI = − 0.01–0.02) and slope (β = 0.12; 95% CI = 0.05–0.19). Compared with GLM model performance, BDNs showed improved accuracy in prediction of the prevalence of P. falciparum (AUC = 0.49 versus 0.75, respectively) and P. vivax (AUC = 0.56 versus 0.74, respectively) on cross-validation.ConclusionsBDNs provide a more flexible modelling framework than GLMs and may have a better predictive performance when developing malaria prevalence maps due to the multiple interacting factors that drive malaria prevalence in different geographical areas. When developing malaria prevalence maps, BDNs may be particularly useful in predicting prevalence where spatial variation in climate and environmental drivers of malaria transmission exists, as is the case in Papua New Guinea.

BackgroundIn medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected from a study population that distinguishes several subpopulations with different (i.e., heterogeneous) effects of an intervention. Despite the frequent occurrence of such data, methods to analyze them are less common and researchers often resort to either ignoring the multilevel and/or heterogeneous structure, analyzing only a single dependent variable, or a combination of these. These analysis strategies are suboptimal: Ignoring multilevel structures inflates Type I error rates, while neglecting the multivariate or heterogeneous structure masks detailed insights.MethodsTo analyze such data comprehensively, the current paper presents a novel Bayesian multilevel multivariate logistic regression model. The clustered structure of multilevel data is taken into account, such that posterior inferences can be made with accurate error rates. Further, the model shares information between different subpopulations in the estimation of average and conditional average multivariate treatment effects. To facilitate interpretation, multivariate logistic regression parameters are transformed to posterior success probabilities and differences between them.ResultsA numerical evaluation compared our framework to less comprehensive alternatives and highlighted the need to model the multilevel structure: Treatment comparisons based on the multilevel model had targeted Type I error rates, while single-level alternatives resulted in inflated Type I errors. Further, the multilevel model was more powerful than a single-level model when the number of clusters was higher. A re-analysis of the Third International Stroke Trial data illustrated how incorporating a multilevel structure, assessing treatment heterogeneity, and combining dependent variables contributed to an in-depth understanding of treatment effects. Further, we demonstrated how Bayes factors can aid in the selection of a suitable model.ConclusionThe method is useful in prediction of treatment effects and decision-making within subpopulations from multiple clusters, while taking advantage of the size of the entire study sample and while properly incorporating the uncertainty in a principled probabilistic manner using the full posterior distribution.

Background: For cohort and cross-sectional studies, the risk ratio (RR) is the preferred measure of effect rather than an odds ratio (OR), especially when the outcome is common (>10%). The log-binomial (LB) and Poisson models are commonly used to estimate the RR; the OR estimated using logistic regression is often used to approximate the RR when the outcome is rare. However, regardless of the prevalence of the outcome, logistic regression predicted exposed and unexposed risks may be used to estimate the RR. Because maximum likelihood estimation is used to fit the logistic model, estimation of the SE of the RR is difficult. Methods: To overcome difficulty in estimation of the SE of the RR and provide a flexible framework for modeling, we developed a Bayesian logistic regression (BLR) model to estimate the RR, with associated credible interval (CIB). We applied the BLR model to a large hypothetical cross-sectional study with categorical variables and to a small hypothetical clinical trial with a continuous variable for which the LB method did not converge. Results of the BLR model were compared to those from several commonly used RR modeling methods. Results: Our examples illustrate the Bayesian logistic regression model estimates adjusted RRs and 95% CIBs comparable to results from other methods. Adjusted risks and risk differences were easily obtained from the posterior distribution. Conclusions: The Bayesian logistic regression modeling approach compares favorably with existing RR modeling methods and provides a flexible framework for investigating confounding and effect modification on the risk scale.

The concern of tourism viability in Thailand is the main issue in the post-pandemic period. A dramatic reduction in tourist arrivals has damaged the tourism sector. This chapter investigated a hierarchal binary model for determining the best option for stimulating tourism activities, such as demand promotion or supply development, particularly green tourism. The hierarchal scenarios are divided into three parts, such as (1) tourist numbers are a driving factor (525 observations), (2) shops and restaurants are the keys (127 samples), and (3) both demands and supplies simultaneously influence the tourism sector (652 observations). Bayesian inference and multi-level logistic regression model. Green tourism (MCMC) simulations are key in addressing insufficient and ineffective survey samples to fix the problem of unbalanced sample sizes. The main econometric model is the multi-level logistic regression model. In conclusion, the perception and satisfaction of tourists (tourism demand sides) still play an essential role in promoting green tourism to drive the Thai tourism sector, especially during the period of the great depression or the post-COVID-19 era.

Bayesian multilevel models—also known as hierarchical or mixed models—are used in situations in which the aim is to model the random effect of groups or levels. In this paper, we conduct a simulation study to compare the predictive ability of 1-level Bayesian multilevel logistic regression models with that of 2-level Bayesian multilevel logistic regression models by using the prior Scaled Beta2 and inverse-gamma distributions to model the standard deviation in the 2-level. Then, these models are employed to estimate the correct answers in two questionnaires administered to university students throughout the first academic semester of 2018. The results show that 2-level models have a better predictive ability and provide more precise probability intervals than 1-level models, particularly when the prior Scaled Beta2 distribution is used to model the standard deviation in the second level. Moreover, the probability intervals of 1-level Bayesian multilevel logistic regression models proved to be more precise when Scaled Beta2 distributions, rather than an inverse-gamma distribution, are employed to model the standard deviation or when 1-level Bayesian multilevel logistic regression models, are used.

The postnatal period is a critical period for both mothers and their newborns for their health. Lack of early postnatal care (PNC) services during a 2-day period is a life-threatening situation for both the mother and the babies. However, no data have been examined for PNCs in East Africa. Hence, using the more flexible Bayesian multilevel modeling approach, this study aims to investigate the pooled prevalence and potential factors for PNC utilization among women after delivery in East African countries. We retrieved secondary data from the Kids Record (KR) demographic and health surveys (DHS) data from 2015 to 2022 from 10 East African countries. A total of 77,052 weighted women were included in the study. We used R 4.3.2 software for analysis. We fitted Bayesian multilevel logistic regression models. Techniques such as Rhat, effective sample size, density, time series, autocorrelation plots, widely applicable information criterion (WAIC), deviance information criterion (DIC), and Markov Chain Monte-Carlo (MCMC) simulation were used to estimate the model parameters using Hamiltonian Monte-Carlo (HMC) and its extensions, No-U-Turn Sampler (NUTS) techniques. An adjusted odds ratio (AOR) with a 95% credible interval (CrI) in the multivariable model to select variables that have a significant association with PNC was used. The overall pooled prevalence of PNC within 48 hrs. of delivery was about 52% (95% CrI: 39, 66). A higher rate of PNC usage was observed among women aged 25-34 years (AOR = 1.21; 95% CrI: 1.15, 1.27) and 35-49-years (AOR = 1.61; 95% CrI: 1.5, 1.72) as compared to women aged 15-24 years; similarly, women who had achieved primary education (AOR = 1.96; 95% CrI: 1.88, 2.05) and secondary/higher education (AOR = 3.19; 95% CrI: 3.03, 3.36) as compared to uneducated women; divorced or widowed women (AOR = 0.83; 95% CrI: 0.77, 0.89); women who had currently working status (AOR = 0.9; 95% CrI: 0.87, 0.93); poorer women (AOR = 0.88; 95% CrI: 0.84, 0.92), middle-class women (AOR = 0.83; 95% CrI: 0.79, 0.87), richer women (AOR = 0.77; 95% CrI: 0.73, 0.81), and richest women (AOR = 0.59; 95% CrI: 0.55, 0.63) as compared to the poorest women; women who had media exposure (AOR = 1.32; 95% CrI: 1.27, 1.36), were having 3-5 children (AOR = 0.89; 95% CrI: 0.84, 0.94), had >5 children (AOR = 0.69; 95% CrI: 0.64, 0.75), had first birth at age < 20 years (AOR = 0.82; 95% CrI: 0.79, 0.84), had at least one ANC visit (AOR = 1.93; 95% CrI: 1.8, 2.08), delivered at health facilities (AOR = 2.57; 95% CrI: 2.46, 2.68), had average birth size (AOR = 0.94; 95% CrI: 0.91, 0.98) and small birth size child (AOR = 0.88; 95% CrI: 0.84, 0.92), had twin newborns (AOR = 1.15; 95% CrI: 1.02, 1.3), and fourth and above birth order (AOR = 0.88; 95% CrI: 0.82, 0.95) were individual-driven women who have been independently associated with PNC, respectively. Regarding community-level variables, rural women (AOR = 0.76; 95% CrI: 0.72, 0.79), high media exposure communities (AOR = 1.1; 95% CrI: 1.04, 1.18), communities with high wealth levels (AOR = 0.88 95% CrI: 0.83, 0.94), communities with high antenatal care (ANC) utilization (AOR = 1.13, 95% CrI: 1.07, 1.19), and long distance to health facilities (AOR = 1.5; 95% CrI: 1.38, 1.63) were among the community factors associated with PNC, respectively. One of the significant public health priorities in East Africa continues to be the underutilization of immediate PNC. The government ought to prioritize improving maternity and child health services, collaborating with interested parties in the area, reducing health disparities, educating mothers about child health, and other connected issues that are very beneficial.

Achieving equity in vaccination coverage has been a critical priority within the global health community. Despite increased efforts recently, certain populations still have a high proportion of un- and under-vaccinated children in many low- and middle-income countries (LMICs). These populations are often assumed to reside in remote-rural areas, urban slums and conflict-affected areas. Here, we investigate the effects of these key community-level factors, alongside a wide range of other individual, household and community level factors, on vaccination coverage. Using geospatial datasets, including cross-sectional data from the most recent Demographic and Health Surveys conducted between 2008 and 2018 in nine LMICs, we fitted Bayesian multi-level binary logistic regression models to determine key community-level and other factors significantly associated with non- and under-vaccination. We analyzed the odds of receipt of the first doses of diphtheria-tetanus-pertussis (DTP1) vaccine and measles-containing vaccine (MCV1), and receipt of all three recommended DTP doses (DTP3) independently, in children aged 12–23 months. In bivariate analyses, we found that remoteness increased the odds of non- and under-vaccination in nearly all the study countries. We also found evidence that living in conflict and urban slum areas reduced the odds of vaccination, but not in most cases as expected. However, the odds of vaccination were more likely to be lower in urban slums than formal urban areas. Our multivariate analyses revealed that the key community variables–remoteness, conflict and urban slum–were sometimes associated with non- and under-vaccination, but they were not frequently predictors of these outcomes after controlling for other factors. Individual and household factors such as maternal utilization of health services, maternal education and ethnicity, were more common predictors of vaccination. Reaching the Immunisation Agenda 2030 target of reducing the number of zero-dose children by 50% by 2030 will require country tailored analyses and strategies to identify and reach missed communities with reliable immunisation services.

Achieving equity in vaccination coverage has been a critical priority within the global health community. Despite increased efforts recently, certain populations still have a high proportion of un- and under-vaccinated children in many low- and middle-income countries (LMICs). These populations are often assumed to reside in remote-rural areas, urban slums and conflict-affected areas. Here, we investigate the effects of these key community-level factors, alongside a wide range of other individual, household and community level factors, on vaccination coverage. Using geospatial datasets, including cross-sectional data from the most recent Demographic and Health Surveys conducted between 2008 and 2018 in nine LMICs, we fitted Bayesian multi-level binary logistic regression models to determine key community-level and other factors significantly associated with non- and under-vaccination. We analyzed the odds of receipt of the first doses of diphtheria-tetanus-pertussis (DTP1) vaccine and measles-containing vaccine (MCV1), and receipt of all three recommended DTP doses (DTP3) independently, in children aged 12-23 months. In bivariate analyses, we found that remoteness increased the odds of non- and under-vaccination in nearly all the study countries. We also found evidence that living in conflict and urban slum areas reduced the odds of vaccination, but not in most cases as expected. However, the odds of vaccination were more likely to be lower in urban slums than formal urban areas. Our multivariate analyses revealed that the key community variables-remoteness, conflict and urban slum-were sometimes associated with non- and under-vaccination, but they were not frequently predictors of these outcomes after controlling for other factors. Individual and household factors such as maternal utilization of health services, maternal education and ethnicity, were more common predictors of vaccination. Reaching the Immunisation Agenda 2030 target of reducing the number of zero-dose children by 50% by 2030 will require country tailored analyses and strategies to identify and reach missed communities with reliable immunisation services.

Using a stages of change approach to explore opportunities for increasing bicycle commuting

Achieving the ambitious goals of the Immunisation Agenda 2030 (IA2030) requires a deeper understanding of factors influencing under-vaccination, including timely vaccination. This study investigates the demand- and supply-side determinants influencing the timely uptake of key childhood vaccines scheduled throughout the first year of life in The Gambia. We used two nationally-representative datasets: the 2019-20 Gambian Demographic and Health Survey and the 2019 national immunisation facility mapping. Using Bayesian multi-level binary logistic regression models, we identified key factors significantly associated with timely vaccination for five key vaccines: birth dose of hepatitis-B (HepB0), first, second, and third doses of the pentavalent vaccine (Penta1, Penta2, Penta3), and first-dose of measles-containing vaccine (MCV1) in children aged 12-35months. We report the adjusted Odds Ratios (aORs) and 95% Credible Intervals (95% CIs) in each case. We found that demand-side factors, such as ethnicity, household wealth status, maternal education, maternal parity, and the duration of the household's residency in its current location, were the most common drivers of timely childhood vaccination. However, supply-side factors such as travel time to the nearest immunisation clinic, availability of cold-storage and staffing numbers in the nearest immunisation clinic were also significant determinants. Furthermore, the determinants varied across specific vaccines and the timing of doses. For example, delivery in a health facility (aOR=1.58, 95%CI: 1.02-2.53), living less than 30min (aOR=2.11, 95%CI: 1.2-8.84) and living between 30 and 60min (aOR=3.68, 95%CI: 1.1-14.99) from a fixed-immunisation clinic was associated with timely HepB0, a time-sensitive vaccine that must be administered within 24h of birth. On the other hand, children who received Penta1 and Penta2 on time were three- to five-fold more likely to receive subsequent doses on time (Penta2 and Penta3, respectively). Finally, proximity to an immunisation facility with functional vaccine cold-storage was a significant supply-side determinant of timely MCV1 (aOR=1.4, 95%CI: 1.09-1.99). These findings provide valuable insights for programme managers and policymakers. By prioritising interventions and allocating scarce resources based on these identified determinants, they can maximize their impact and ensure children in The Gambia receive timely vaccinations throughout their first year of life, contributing to IA2030 goals.

Excellence networks in science: A Web-based application based on Bayesian multilevel logistic regression (BMLR) for the identification of institutions collaborating successfully

In this paper, we investigate the effectiveness of a Bayesian logistic regression model to compute the weights of a pseudo-metric, in order to improve its discriminatory capacity and thereby increase image retrieval accuracy. In the proposed Bayesian model, the prior knowledge of the observations is incorporated and the posterior distribution is approximated by a tractable Gaussian form using variational transformation and Jensen's inequality, which allow a fast and straightforward computation of the weights. The pseudo-metric makes use of the compressed and quantized versions of wavelet decomposed feature vectors, and in our previous work, the weights were adjusted by classical logistic regression model. A comparative evaluation of the Bayesian and classical logistic regression models is performed for content-based image retrieval as well as for other classification tasks, in a decontextualized evaluation framework. In this same framework, we compare the Bayesian logistic regression model to some relevant state-of-the-art classification algorithms. Experimental results show that the Bayesian logistic regression model outperforms these linear classification algorithms, and is a significantly better tool than the classical logistic regression model to compute the pseudo-metric weights and improve retrieval and classification performance. Finally, we perform a comparison with results obtained by other retrieval methods.