Bayesian Multilevel Approach Research Articles

Why does the number of antenatal care visits in Ethiopia remain low?: A Bayesian multilevel approach.

Antenatal care (ANC) visit is a proxy for maternal and neonatal health. The ANC is a key indicator of access and utilization of health care for pregnant women. Recently, eight times ANC visits have been recommended during the pregnancy period. However, nearly 57% of women received less than four ANC visits in Ethiopia. Therefore, the objective of this study is to identify factors associated withthe number of ANC visits in Ethiopia. A community-based cross-sectional study design was conducted from March 21 to June 28/2019. Data were collected using interviewer-administered questionnaires from reproductive age groups. A stratified cluster sampling was used to select enumeration areas, households, and women from selected households. A Bayesian multilevel negative binomial model was applied for the analysis of this study. There is an intra-class correlation (ICC) = 23.42% and 25.51% for the null and final model, respectively. Data were analyzed using the STATA version 17.0. The adjusted incidence risk ratio (IRR) with 95% credible intervals (CrI) was used to declare the association. A total of 3915 pregnant women were included in this study. The mean(SD) age of the participants was 28.7 (.11) years. Nearly one-fourth (26.5%) of pregnant women did not have ANC visits, and 3% had eight-time ANC visits in Ethiopia. In the adjusted model, the age of the women 25-28 years (IRR:1.13; 95% CrI: 1.11, 1.16), 29-33 years (IRR: 1.15; 95% CrI: 1.15, 1.16), ≥34 years (IRR:1.14; 95% CrI: 1.12, 1.17), being a primary school (IRR: 1.22, 95% CrI: 1.21, 1.22), secondary school and above (IRR: 1.26, 95% CrI: 1.26, 1.26), delivered in health facility (IRR: 1.93; 95% CrI: 1.92, 1.93), delivered with cesarian section (IRR: 1.18; 95% CrI: 1.18, 1.19), multiple (twin) pregnancy (IRR: 1.11; 95% CrI: 1.10, 1.12), richest (IRR:1.23; 95% CrI: 1.23, 1.24), rich family (IRR: 1.34, 95% CrI: 1.30, 1.37), middle income (IRR: 1.29, 95% CrI: 1.28, 1.31), and poor family (IRR = 1.28, 95% CrI:1.28, 1.29) were shown to have significant association with higher number of ANC vists, while, households with total family size of ≥ 5 (IRR: 0.92; 95% CrI: 0.91, 0.92), and being a rural resident (IRR: 0.92, 95% CrI: 0.92, 0.94) were shown to have a significant association with the lower number of ANC visits. Overall, 26.5% of pregnant women do not have ANC visits during their pregnancy, and 3% of women have eight-time ANC visits. This result is much lower as compared to WHO's recommendation, which states that all pregnant women should have at least eight ANC visits. In this study, the ages of the women 25-28, 29-33, and ≥34 years, being a primary school, secondary school, and above, delivered in a health facility, delivered with caesarian section, multiple pregnancies, rich, middle and poor wealth index, were significantly associated with the higher number of ANC visits, while households with large family size and rural residence were significantly associated with a lower number of ANC visits in Ethiopia.

Māori businesses in Aotearoa New Zealand: Modelling Indigenous enterprise using self-identification and ownership

Identity and ownership are two conceptual pillars used to define Indigenous enterprise. Approaches that use administrative data offer the opportunity to identify Indigenous-owned enterprises without the burden of a survey. It remains unclear, however, if Indigenous-owned enterprises are also likely to self-identify as Indigenous. Thus, in this paper we examine if self-identification as an Indigenous business in Aotearoa New Zealand is driven by Māori ownership. We link information from businesses that had the opportunity to self-identify as Māori in an annual survey with administrative data from Stats NZ’s Integrated Data Infrastructure to calculate their proportion of Māori ownership. Then, we fit models of varying complexity using a Bayesian multilevel approach to predict the probability of self-identification as a Māori business as a function of businesses’ demographic variables and proportion of Indigenous ownership. Using model comparison and out-of-sample predictions we show that Māori ownership is a weak predictor of self-identification as a Māori business. We also show how the probability of self-identification as an Indigenous enterprise changes between regions, sectors, and industries to illustrate the benefits of a quantitative approach to target businesses likely to self-identify as Māori. Predicting the extent to which enterprise owners might choose to self-identify as a Māori business is critical to identifying a robust population of Indigenous businesses and to have better estimates of the Indigenous economy.

A full Bayesian multilevel approach for modeling interaction effects in single-vehicle crashes

Interaction effects constitute crucial crash attributes that can be classified into two distinct categories: spatiotemporal interactions and factor interactions. These interactions are rarely addressed systematically in modeling the severity of single-vehicle (SV) crashes. This study focuses on uncovering these crash attributes by designing a full Bayesian spatiotemporal interaction multilevel logit (STIML-logit) approach with heterogeneity in means and variances (HMV). Meanwhile, a nested Gaussian conditional autoregressive (CAR) structure is proposed to fit the spatiotemporal interaction component and its effectiveness is verified by calibrating four different interaction patterns. A standard multilevel logit (with and without HMV), a multilevel logit with HMV, and a spatiotemporal multilevel logit with HMV are constructed for comparison. Risk factors are decomposed into traffic environment factors (group level) and individual crash factors (case level) to construct a multilevel structure and to capture possible interactions between risk factors from different levels (cross-level factor interactions). We perform regression modeling utilizing SV crash cases covering 96 major urban roads in Shandong, China. The modeling results underscore several significant findings: (1) the STIML-logit with HMV demonstrates the best regression performance, suggesting that systematically dealing with the interaction effects and the HMV is a trustworthy modeling perspective; (2) crash models with the nested CAR outperform those with the traditional CAR and the result is supported by all the spatiotemporal statistical functions, highlighting the potential advantages of the nested structure; (3) all the environment factors maintain significant interactions with the case factors, highlighting that the contribution of the environment factors to crash injuries is not constant but is rather influenced by the specific case-related crash factors. The study introduces a promising regression architecture for modeling crash injuries and revealing subtle crash attributes.

The impact of directly observed therapy on the efficacy of Tuberculosis treatment: a Bayesian multilevel approach

Abstract We propose and discuss a Bayesian procedure to estimate causal effects for multilevel observations in the presence of confounding. This work is motivated by an interest in determining the causal impact of directly observed therapy on the successful treatment of Tuberculosis. We focus on propensity score regression and covariate adjustment to balance the treatment allocation. We discuss the need to include latent local-level random effects in the propensity score model to reduce bias in the estimation of causal effects. A simulation study suggests that accounting for the multilevel nature of the data with latent structures in both the outcome and propensity score models has the potential to reduce bias in the estimation of causal effects.

A novel Bayesian multilevel regression approach to the reconstruction of an eastern Mediterranean temperature record for the last 10,000 years

Climate reconstructions derived from proxy records for individual sites often fail to incorporate existing regional information, which may help to determine uncertainties and express variability within specific reconstructions or commonalities between datasets. Such an understanding is crucial when examining past human-environment interactions. Taking the eastern Mediterranean as our case study, we present here a new air temperature model for the last 10,000 years that utilises data from 33 previously published proxy-based independent reconstructions, using a novel fully Bayesian approach that applies multilevel regression models of individual temperature datasets grouped together into 300 year long consecutive sub-intervals. A Bayesian multilevel approach allows the model to share information between each regression model from the individual datasets and the 300 year grouped regression models. The results demonstrate commonalities between individual datasets derived from different sources, and embed the uncertainties within the model. Our results establish that the eastern Mediterranean region was consistently warmer than the 20th century, except for two short intervals at the end of the Early Holocene (between 8400 and 8250 yrs BP) and the start of the mid-Holocene (between 7800 and 7650 yrs BP). We also identify changes within temperature associated with both the 8.2 ka and 4.2 ka BP events, however our findings identify regional warming in the eastern Mediterranean, rather than cooling often associated with the 4.2 ka BP event. Our results are comparable with previous large scale hemispheric reconstructions, demonstrating that our model is a robust candidate for temperature reconstructions within a confined region, which can range from mesoscale up to macroscale.

Bayesian multi-level calibration of a process-based maize phenology model

Plant phenology models are important components in process-based crop models, which are used to assess the impact of climate change on food production. For reliable model predictions, parameters in phenology models have to be accurately known. They are usually estimated by calibrating the model to observations. However, at regional scales in which different cultivars of a crop species may be grown, not accounting for inherent differences in phenological development between cultivars in the model and the presence of model deficits lead to inaccurate parameter estimates. To account for inherent differences between cultivars and to identify model deficits, we used a Bayesian multi-level approach to calibrate a phenology model (SPASS) to observations of silage maize grown across Germany between 2009 and 2017. We evaluated four multi-level models of increasing complexity, where we accounted for different combinations of ecological, weather, and year effects, as well as the hierarchical classification of cultivars nested within ripening groups of the maize species. We compared the calibration quality from this approach to the commonly used pooled approach in which none of these factors are considered. The pooled model led to over-confident process model parameter estimates and comparatively poor calibration quality. The mean value of the unexplained residual error standard deviation reduced from 5.5 BBCH (phenological development units) in the pooled model case (BM-0) to 5.3 BBCH when eco-region and year effects (BMM-1) were considered. Additionally accounting for weather effects (BMM-2a) resulted in a mean value of 5.2 BBCH. Calibration quality especially improved when the hierarchical classification of cultivars within ripening groups of maize was incorporated. Including the hierarchical classification with eco-region and year effects (BMM-2b) led to a mean residual error of 4.4 BBCH while additionally considering weather effects in the full model case (BMM-3) resulted in a value of 4.3 BBCH. Our findings have implications for regional model calibration and data-gathering studies, since it emphasizes that ripening group and cultivar information is essential. Furthermore, we found that if this information is not available, at least weather, eco-region and year effects should be taken into account. Accounting for only the eco-region and year effects led to parameter-compensation of the missing weather effects. Our results can facilitate model improvement studies since we identified possible model limitations related to temperature effects in the reproductive (post-flowering) phase and to soil-moisture. We demonstrate that Bayesian multi-level calibration of a phenology model facilitates the incorporation of hierarchical dependencies and the identification of model limitations. Our approach can be extended to full crop models at different spatial scales.

Pooled prevalence of stunting and associated factors among children aged 6-59 months in Sub-Saharan Africa countries: A Bayesian multilevel approach.

Over 155 million children under five suffer from stunting, and it is responsible for over one million deaths and 54.9 million Disability Adjusted Life Years (DALYS) of under-five children worldwide. These predominantly occurred in low-and middle-income countries like sub-Saharan Africa. Stunted children begin their lives at a marked disadvantage. Some of these are; poor cognition and educational performance, low adult wages, lost productivity and, when accompanied by excessive weight gain later in childhood, an increased risk of nutrition-related chronic diseases in adult life and the devastating effects of stunting can last a lifetime and even affect the next generation. Despite this, its magnitude rises in the past 25 years in sub-Saharan Africa. Studies that capture the pooled prevalence and associated factors of stunting among children aged 6-59 months in sub-Saharan Africa are limited. Therefore, this study was carried out on the basis of the Bayesian approach to determine the pooled prevalence and predictors of stunting among children aged 6-59 months in Sub-Saharan Africa. To assess the pooled prevalence of stunting and associated factors among children aged 6-59 months in Sub-Saharan Africa. For this study a total of 173,483 weighted samples from the demography and health survey data set of 35 sub-Saharan African countries from 2008 to 2020 were used. After checking Variation between cluster by computing Intraclass Correlation Coefficient, binary logistic regression model was conducted based on hierarchical Bayesian statistical approach to account the hierarchical nature of demography and health survey data and to get reliable estimates by using additional information from the prior distribution. Adjusted odds ratio with 95% credible interval of the best fitted model was used to ascertain the predictors. The pooled prevalence of stunting in Sub-Saharan Africa was about 35% (95%CI: 34.87, 35.31). Of the sub-regions, the highest prevalence of stunting was in East Africa, 37% (95%, CI: 36.96, 37.63) followed by Central Africa, 35% (95%CI: (34.93, 35.94). Being male (AOR = 1.27, 95% CrI 1.25, 1.30), small birth size (AOR = 1.29, CrI 1.25, 1.32), home delivery (AOR = 1.17, CrI 1.14, 1.20), and no education of mothers (AOR = 3.07, CrI 2.79, 3.39) were some of the significant predictors of stunting of children. The prevalence of stunting of children in sub-Saharan Africa is among the highest in the world. Predictors such as being male, being small at birth, a child delivered at home, and, low level of maternal education were some of the predictors of childhood stunting. Stakeholders and non-governmental organizations should consider those contributing factors of stunting when they plan and design nutritional improvement programs.

AI, Demand and the Impact of Productivity-enhancing Technology on Jobs: Evidence from Portugal

ABSTRACT This study examines whether AI, as revealed in productivity improvements, may have the ability to threaten sectoral employment in Portugal. We first present a theoretical framework based on a supply and demand model for sectoral output. This model predicts that the impact of AI will depend on the response of labor demand to two opposing forces: as productivity improves less labor is required to produce the same output, while more output is demanded because of lower production costs brought about by higher productivity, which creates more jobs. Our estimates of the industry-level elasticities of employment with respect to productivity for a sample of 32 industries over 1995–2017 using a Bayesian multilevel approach are all negative and surprisingly similar across sectors.

Bayesian multilevel analysis of determinants of acute respiratory infection in children under the age of five years in Ethiopia

BackgroundAcute respiratory tract infection (ARI) is one of the leading causes of illness and mortality in children under the age of five worldwide. Pneumonia, which is caused by a respiratory tract infection, kills about 1.9 million children under the age of 5 years around the world. The majority of these deaths occur in underdeveloped countries. According to the 2016 Ethiopia Demographic and Health Survey (EDHS), the prevalence rate of ARI in Ethiopia was 7%. Prevalence is defined as the number of infectious diseases present at a given period in relation to the total number of children under the age of five who have been exposed to ARI. The goal of this study was to determine the risk factors for acute respiratory infection among children under the age of five in Ethiopia.MethodsTo provide representative samples of the population, a community-based cross-sectional sampling scheme was designed. Bayesian multilevel approach was employed to assess factors associated with the prevalence of ARI among children under age five in Ethiopia. The data was collected from 10,641 children under the age of 5 years out of which 9918 children were considered in this study.ResultsThe ARI prevalence rate in children under the age of 5 years was assessed to be 8.4%, somewhat higher than the country’s anticipated prevalence rate. Children whose mothers did not have a high level of education had the highest prevalence of ARI. The key health, environmental, and nutritional factors influencing the proportion of children with ARI differed by area. Tigray (15.3%) and Oromia (14.4%) had the highest prevalence of ARI, while Benishangul Gumuz had the lowest prevalence (2.6%). The use of vitamin A was investigated, and the results revealed that roughly 43.1% of those who received vitamin A had the lowest prevalence of ARI (7.7%) as compared to those who did not receive vitamin A. Diarrhea affected 11.1% of children under the age of five, with the highest frequency of ARI (24.6%) and the highest prevalence of ARI reported in children whose drinking water source was unprotected/unimproved (9.4%).ConclusionsThe prevalence of ARI among children under the age of 5 years was found to be strongly affected by the child’s age, household wealth index, mother’s educational level, vitamin A supplement, history of diarrhea, maternal work, stunting, and drinking water source. The study also found that the incidence of ARI varies significantly between and within Ethiopian areas. When intending to improve the health status of Ethiopian children, those predictive variables should be taken into consideration.

Validity and usefulness of the student-athletes' motivation toward sport and academics questionnaire: a Bayesian multilevel approach.

BackgroundReliable assessment and understanding of student-athletes’ motivation for dual careers are crucial to support their career development and transitions. The purpose of this research was to examine the validity and usefulness of the student-athletes’ motivation toward sport and academics questionnaire (SAMSAQ-PT) in the Brazilian higher education context. Four studies were performed.MethodsIn study one, conceptually and semantic translation of the questionnaire and Bayesian exploratory factor analysis were conducted. In study two, a Bayesian confirmatory factor analysis with an independent sample was performed. In study three, Bayesian multilevel modeling was applied to examine the construct validity of the questionnaire in a cross-sectional sample. In study four, the SAMSAQ-PT sensitiveness was examined in a longitudinal sample, and the results were interpreted based on multilevel regression and poststratification.ResultsAltogether the results provided evidence validity and usefulness of the SAMSAQ-PT in Brazilian student-athletes. The Brazilian student-athletes’ motivation scores were sensitive to the influence of sex, sport level, and type of university on career and sport motivation. SAMSAQ-PT estimate scores across an academic year showed a trend of stability in the scores, adjusting for sex, sport level, type of university, and student-athlete status.ConclusionThe SAMSAQ-PT proved to be a robust and valuable questionnaire, which could be used in Portuguese-speaking countries. The findings of the cross-sectional and longitudinal surveys urge to consider individual and contextual characteristics when investigating motivation of dual-career of athletes, also concerning the sex-related opportunities in university sports. Furthermore, there is a need for a call for action to promote and nurture the student-athletes motivation to remain engaged in both sports and educational commitments.

Volitional modification of brain activity in adolescents with Autism Spectrum Disorder: A Bayesian analysis of Slow Cortical Potential neurofeedback

Autism spectrum disorder is (ASD) characterized by a persisting triad of impairments of social interaction, language as well as inflexible, stereotyped and ritualistic behaviors. Increasingly, scientific evidence suggests a neurobiological basis of these emotional, social and cognitive deficits in individuals with ASD.The aim of this randomized controlled brain self-regulation intervention study was to investigate whether the core symptomatology of ASD could be reduced via an electroencephalography (EEG) based brain self-regulation training of Slow Cortical Potentials (SCP). 41 male adolescents with ASD were recruited and allocated to a) an experimental group undergoing 24 sessions of EEG-based brain training (n1 = 21), or to b) an active control group undergoing conventional treatment (n2 = 20), that is, clinical counseling during a 3-months intervention period. We employed real-time neurofeedback training recorded from a fronto-central electrode intended to enable participants to volitionally regulate their brain activity.Core autistic symptomatology was measured at six time points during the intervention and analyzed with Bayesian multilevel approach to characterize changes in core symptomatology. Additional Bayesian models were formulated to describe the neural dynamics of the training process as indexed by SCP (time-domain) and power density (PSD, frequency-domain) measures.The analysis revealed a substantial improvement in the core symptomatology of ASD in the experimental group (reduction of 21.38 points on the Social Responsiveness Scale, SD = 5.29), which was slightly superior to that observed in the control group (evidence Ratio = 5.79). Changes in SCP manifested themselves as different trajectories depending on the different feedback conditions and tasks. Further, the model of PSD revealed a continuous decrease in delta power, parallel to an increase in alpha power. Most notably, a non-linear (quadratic) model turned out to be better at predicting the data than a linear model across all analyses. Taken together, our analyses suggest that behavioral and neural processes of change related to neurofeedback training are complex and non-linear. Moreover, they have implications for the design of future trials and training protocols.

Bayesian Generalized Linear Mixed Modeling of Breast Cancer

Breast cancer is one of the most common cancers among women. Breast cancer treatment strategies in Nigeria need urgent strengthening to reduce mortality rate because of the disease. This study aimed to determine the relationship between the ages at diagnosis and established the prognostic factors of modality of treatment given to breast cancer patient in Nigeria. The data was collected for 247 women between years 2011-2015 who had breast cancer in two different hospitals in Ekiti State, Nigeria. Model estimation is based on Bayesian approach via Markov Chain Monte Carlo. A multilevel model based on generalized linear mixed model is used to estimate the random effect. The mean age of the patients (at the time of diagnosis) was 42.2 yr with 52% of the women aged between 35-49 yr. The results of the two approaches are almost similar but preference is given to Bayesian because the approach is more robust than the frequentist. Significant factors of treatment modality are age, educational level and breast cancer type. Differences in socio-demographic factors such as educational level and age at diagnosis significantly influence the modality of breast cancer treatment in western Nigeria. The study suggests the use of Bayesian multilevel approach in analyzing breast cancer data for the practicality, flexibility and strength of the method.

Longitudinal Changes of Functional Capacities Among Adolescent Female Basketball Players.

Background: The interpretation of young athletes' performance during pubertal years is important to support coaches' decisions, as performance may be erroneously interpreted due to the misalignment between chronological age (CA), biological age (BA) and sport age (SA).Aim: Using a Bayesian multilevel approach, the variation in longitudinal changes in performance was examined considering the influence of CA, BA (age at menarche), SA, body size, and exposure to training among female basketball players.Method: The study had a mixed-longitudinal design. Thirty eight female basketball players (aged 13.38 ± 1.25 years at baseline) were measured three times per season. CA, BA and SA were obtained. Anthropometric and functional measures: countermovement jump, Line drill (LD), Yo-Yo (Yo-Yo IR1). Based on the sum of the z-scores, an index of overall performance was estimated. The effects of training on longitudinal changes in performance were modeled.Results: A decrease in the rate of improvements was apparent at about 14 years of age. When aligned for BA, the slowing of the rate of improvements is apparent about 2 years after menarche for LD. For countermovement jump longitudinal changes, when performance was aligned for BA improvements became linear. For Yo-Yo IR1 and performance index, both indicators showed a linear trend of improvement when aligned for CA and BA, separately. Older players showed higher rates of improvement for Yo-Yo IR1 and performance index from pre-season to end-season. When considering performance changes aligned for BA it was apparent an improvement of performance as players became biologically mature.Conclusions and Implications: The alignment of CA with BA and SA provides important information for coaches. Human growth follows a genetically determined pattern, despite variation in both tempo and timing. When the effects of maturation reach their end, all the girls went through the same process. Hence, there is no need to artificially manipulate youth competitions in order to accelerate gains that sooner or later reach their peak and tend to flat their improvement curve.

Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling.

Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.

Modeling of Academic Achievement of Primary School Students in Ethiopia Using Bayesian Multilevel Approach

This study aims to explore Bayesian multilevel modeling to investigate variations of average academic achievement of grade eight school students. A sample of 636 students is randomly selected from 26 private and government schools by a two-stage stratified sampling design. Bayesian method is used to estimate the fixed and random effects. Input and process quality indicators of education such as student to class ratio, student to teacher ratio, availability of teaching learning resources at school, teaching methods, and standard of course curriculum are found to be significantly affecting the academic achievement of the students. The effects of student level covariates: absence from class, academic motivation, academic self concept, study time, family income, mother’s education, parents’ employment status, work demand at home, and parent’s follow-up of child are significantly varying from school to school. The results show that a large proportion of academic achievement variation is accounted to between schools. It is interesting to found out that the within school variation is very high for government schools while the between school variation is very high for private schools. There is uniformity across the government schools with high individual differences among students. However, there is lesser uniformity across the private schools with lesser individual differences of students. The findings in this study indicate that private schools are in a better position in maintaining quality of education at grade eight. Efficient academic management is needed at the government schools that can improve quality of education at the level.

Eco-social determinants of Schistosoma japonicum infection supported by multi-level modelling in Eryuan county, People's Republic of China

Schistosomiasis remains of considerable public health concern in many tropical and subtropical regions of the world, including the People's Republic of China (P.R. China). The effectiveness of schistosomiasis control interventions are, among other factors, governed by the social-ecological context. However, eco-social determinants of schistosomiasis are poorly understood, particularly at the household or village levels. In the current study, residents in 26 villages of Eryuan county, Yunnan province, P.R. China, were screened for Schistosoma japonicum infection with a serological assay that was followed by stool examination for sero-positive individuals. Bayesian multilevel models with spatial random effects were employed to profile the S. japonicum infection risk based on known transmission sites of S. japonicum that are scattered across individual land parcels in this part of the country. The key risk factors identified with this approach were the absence of a sanitary stall house for livestock and presence of living and infected intermediate host snails in close proximity. We conclude that a spatially explicit Bayesian multilevel approach can deepen our understanding of eco-social determinants that govern schistosomiasis transmission at a small geographical scale.

Bayesian autoregressive multilevel modeling of burden of diseases, injuries and risk factors in Iran 1990 - 2013.

Statistical modeling and developing new methods for estimating burden of diseases, injuries and risk factors is a fundamental concern in studying the country health situation for better health management and policy making. Bayesian autoregressive multilevel model is a strong method for this kind of study though in complex situations it has its own challenges. Our study aims to describe the way of modeling space and time data through an autoregressive multilevel model and address challenges in complex situation. We will obtain data from different published and unpublished secondary data sources including population-based health surveys (e. g. NHS, DHS, STEP) at national and provincial levels and we also assess epidemiological studies via systematic review for each disease, injuries and risk factor over the period of 1990-2013. These data generally have a multilevel hierarchy and also time correlation. However, statistical analysis of diseases, injuries and risk factors data is primarily facing the problem of information scarcity. Data are generally too scarce to ensure reliable estimates in many practical problems. Also, there may be nonlinear changes over time, different kind of uncertainties in data and incompatible geographical data. We describe Bayesian autoregressive multilevel modeling approach that provides a natural solution to these problems through its ability to sensibly combine information from several sources of data and available prior information. In this hierarchy model levels of each hierarchy borrow information from each other and also lower levels borrow information from higher levels. We will fit the model using Markov Chain Monte Carlo (MCMC) methods because of its capabilities and benefits in complex cases. Our analyses will include different existing sources of data in Iran for 24 years through a rational and reasonable model to estimate burden of diseases, injuries and risk factors for Iran at national, regional and provincial levels while considering several kinds of uncertainties. Comprehensive and realistic estimates are always an issue of request that will be obtained through a suitable statistical modeling considering all dimensions and then can be used for making better decision in real situations.

Do Integration Policies Affect Immigrants' Voluntary Engagement? An Exploration at Switzerland's Subnational Level

This paper investigates whether integration policies influence immigrants' propensity to volunteer, the latter being an important element of immigrants' integration into the host society. By distinguishing different categories of integration policies at Switzerland's subnational level and applying a Bayesian multilevel approach, our results suggest varying policy effects: while policies fostering socio-structural rights enhance immigrants' propensity to volunteer, we observe a negative curvilinear relationship between cultural rights and obligations and immigrants' volunteerism implying that a combination of cultural entitlements and obligations is most conducive to immigrants' civic engagement.