Hierarchical Modeling Techniques Research Articles

Good modelling practice in ecology, the hierarchical Bayesian perspective

Ecological modelling often involves addressing challenges such as dependence in responses, e.g., spatial and/or temporal correlation, heterogeneity of variance, and hierarchical structures inherent in ecological processes and data. A constant challenge is the inadequacy of the data to well address the questions of interest. What is observable may not be sufficiently informative. What has been observed may not have been well designed. Carefully conceived modelling can take us to improved inference compared with adopting standard inference tools like basic regression and analysis of variance. Hierarchical modelling techniques provide a powerful framework for capturing these complexities by explicitly modelling the multi-level structure of ecological systems.In this paper we focus on good modelling practice in the hierarchical Bayesian framework. We discuss good modelling practice in the form of 10 steps, elaborated with a running example, to aid quantitative ecologists interested in employing Bayesian modelling. Two of the authors are statisticians who view themselves primarily as stochastic modellers. The other two are quantitative ecologists who view such stochastic tools as essential for advancing ecological knowledge. Hence, implicitly, we pay careful attention to good modelling practice. Further, we argue the benefits that accrue to working in a Bayesian framework as the paper is developed.It is worth noting that the steps we propose, though presented in the context of ecological modelling, are appropriate for effective model building across most fields of application.

Intimate partner violence against adolescent girls: regional and national prevalence estimates and associated country-level factors

Intimate partner violence is a serious public health problem and negatively affects short-term and long-term health, development, and wellbeing of adolescent girls. Global estimates from WHO have shown that adolescent girls aged 15-19 years experience high rates of intimate partner violence. We aimed to estimate the lifetime and past-year prevalence and patterns of physical or sexual intimate partner violence against adolescent girls by male partners across 161 countries and areas, and to examine the country-level factors, including the prevalence of child marriage, associated with the lifetime and past-year prevalence of intimate partner violence in this age group. These analyses used the 2018 global, regional, and country estimates on intimate partner violence published by WHO and economic, social, and political metadata from subject-specific databases. Drawing on data from the WHO Global Database on Prevalence of Violence Against Women, we used hierarchical Bayesian modelling techniques to estimate lifetime and past-year prevalence of physical or sexual (or both) intimate partner violence against adolescent girls aged 15-19 years by country. Linear regression methods were used to examine contextual social, economic, and political factors associated with intimate partner violence against adolescent girls in the 101 countries (lifetime prevalence) and 105 countries (past-year prevalence) for which these metadata were available. The estimated global prevalence of physical or sexual intimate partner violence against ever-partnered adolescent girls aged 15-19 years was 24% (95% uncertainty interval 21-28) in their lifetime and 16% (14-19) in the past year. Prevalence varied greatly across countries and regions, with lifetime prevalence ranging from 6% (3-11) in Georgia to 49% (35-64) in Papua New Guinea. Overall, the prevalence of both lifetime (154 countries) and past-year (157 countries) intimate partner violence against adolescent girls was higher in low-income and lower-middle-income countries and regions than in high-income countries and regions. Countries with higher rates of female secondary school enrolment and those with inheritance laws that are more gender-equal had lower prevalence of intimate partner violence against adolescent girls. Lower-income countries and societies with a high prevalence of child marriage had higher prevalence of physical or sexual intimate partner violence against adolescent girls. Our findings highlight the widespread prevalence of intimate partner violence against adolescent girls across the globe and its relationship with country-level contextual factors. They emphasise the need for promoting and ensuring policies and programmes that increase and ensure gender equality. Countries should strive to provide secondary education for all girls, ensure equal property rights for women, eliminate discriminatory gender norms, and address harmful practices such as child marriage. UN Women-WHO Joint Programme on Strengthening Violence Against Women Data funded by the UK Foreign and Commonwealth Development Office and the UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development, and Research Training in Human Reproduction.

Using group level factor models to resolve high dimensionality in model-based sampling.

Joint modeling of decisions and neural activation poses the potential to provide significant advances in linking brain and behavior. However, methods of joint modeling have been limited by difficulties in estimation, often due to high dimensionality and simultaneous estimation challenges. In the current article, we propose a method of model estimation that draws on state-of-the-art Bayesian hierarchical modeling techniques and uses factor analysis as a means of dimensionality reduction and inference at the group level. This hierarchical factor approach can adopt any model for the individual and distill the relationships of its parameters across individuals through a factor structure. We demonstrate the significant dimensionality reduction gained by factor analysis and good parameter recovery, and illustrate a variety of factor loading constraints that can be used for different purposes and research questions, as well as three applications of the method to previously analyzed data. We conclude that this method provides a flexible and usable approach with interpretable outcomes that are primarily data-driven, in contrast to the largely hypothesis-driven methods often used in joint modeling. Although we focus on joint modeling methods, this model-based estimation approach could be used for any high dimensional modeling problem. We provide open-source code and accompanying tutorial documentation to make the method accessible to any researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bayesian Multisource Hierarchical Models with Applications to the Monthly Retail Trade Survey

Abstract The integration of multiple survey, administrative, and third-party data offers the opportunity to innovate and improve survey estimation via statistical modeling. With decreasing response rates and increasing interest for more timely and geographically detailed estimates, imputation methodology that combines multiple data sources to adjust for low unit response and allow for more detailed publication levels, including geographic estimates, is both timely and necessary. Motivated by the Advance Monthly Retail Trade Survey (MARTS) and Monthly Retail Trade Survey (MRTS), we propose Bayesian hierarchical multiple imputation-dependent data models with the goals of automating imputation for the MARTS by using historic MRTS data and providing geographically granular (state-level) estimates for the MRTS via mass imputation using third-party data and spatial dependence. As a natural byproduct of this approach, measures of uncertainty are provided. This article illustrates the advantages of applying established Bayesian hierarchical modeling techniques with multiple source data to address practical problems in official statistics and is, therefore, of independent interest. The motivating empirical studies are unified by their hierarchical modeling framework, which ultimately results in a more principled approach for estimation for the MARTS and a more geographically granular data product for the MRTS.

A Bayesian hierarchical model for the analysis of visual analogue scaling tasks.

In psychophysics and psychometrics, an integral method to the discipline involves charting how a person's response pattern changes according to a continuum of stimuli. For instance, in hearing science, Visual Analog Scaling tasks are experiments in which listeners hear sounds across a speech continuum and give a numeric rating between 0 and 100 conveying whether the sound they heard was more like word "a" or more like word "b" (i.e. each participant is giving a continuous categorization response). By taking all the continuous categorization responses across the speech continuum, a parametric curve model can be fit to the data and used to analyze any individual's response pattern by speech continuum. Standard statistical modeling techniques are not able to accommodate all of the specific requirements needed to analyze these data. Thus, Bayesian hierarchical modeling techniques are employed to accommodate group-level non-linear curves, individual-specific non-linear curves, continuum-level random effects, and a subject-specific variance that is predicted by other model parameters. In this paper, a Bayesian hierarchical model is constructed to model the data from a Visual Analog Scaling task study of mono-lingual and bi-lingual participants. Any nonlinear curve function could be used and we demonstrate the technique using the 4-parameter logistic function. Overall, the model was found to fit particularly well to the data from the study and results suggested that the magnitude of the slope was what most defined the differences in response patterns between continua.

Relationship between executive remuneration and performance of South African mining companies

Orientation: Managers are supposed to manage companies to maximise shareholders’ wealth. Instead, there are long-standing perceptions that managers are rent extractors who maximise their own wealth, implying a misalignment between executive remuneration and company performance.Research purpose: The purpose of this study was to determine the existence of a relationship between executive remuneration and the financial performance of South African-listed mining companies.Motivation for the study: Executive remuneration significantly increased over the past five decades relative to company financial performance. The mining sector was selected due to its susceptibility to external factors and shocks leading to volatility in the financial performance of mining companies.Research approach/design and method: This study was a quantitative archival study, using data from 2015 to 2021, by applying the hierarchical linear modelling technique at a 95% confidence level and a 5% significance level.Main findings: The study found a weak to strong relationship between executive remuneration and company financial performance. Furthermore, an analysis of executive remuneration revealed an increase in short-term incentive payments and a decrease in the fixed salary as a proportion of chief executive officer remuneration.Practical/managerial implications: Because the study found a strong link between executive remuneration and earnings-based financial performance metrics, governing bodies should ensure that financial performance metrics include cash flow-based financial metrics as company earnings are highly susceptible to management manipulation.Contribution/value-add: This study contributes to the existing literature on executive remuneration and will be useful to researchers, shareholders, boards of directors, remuneration committees and policymakers.

Embracing fairness within a cross-efficiency hierarchical network DEA system

Several scholars have utilized hierarchical network Data Envelopment Analysis modeling techniques to assess the performance of complex structures. However, there has been limited consideration given to the integration of a peer-appraisal setting within a self-evaluation hierarchical context. This aims to enhance discriminatory power and mitigate the issue of unrealistic weighting scheme. To this end, our study extends the single-stage hierarchical additive self-evaluation model of Kao (Omega 51:121–127, 2015. https://doi.org/10.1016/j.omega.2014.09.008), by integrating the well-established cross-efficiency method. An original combination of a maxmin secondary goal model and the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is proposed, to expand the basic hierarchical self-evaluation model. The maxmin model addresses the issue of the non-unique optimal multipliers obtained from the self-evaluation model, ensuring a more realistic weight scheme. The CRITIC method, that tackles the aggregation problem by objectively determining weights of criteria, rewards the minority and is conducive to a fairer evaluation. Results indicate that the proposed approach is more likely to obtain a unique efficiency and ranking score for the units under consideration. This study entails a numerical experimentation aimed at evaluating the efficiency of a set of 20 universities while validating the applicability of our proposed approach. To conclude, the practical applications of this methodological framework could encompass assessing services within the higher education sector or fostering sustainable development across various operations within a hierarchical structure.

Mapping the prevalence of cancer risk factors at the small area level in Australia

BackgroundCancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies.MethodsUsing Australia as a case study, the 2017–2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated.ResultsWe illustrated that in 2017–2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work.ConclusionsOur study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

On the integration of Physics-Based Machine Learning with hierarchical Bayesian modeling techniques

Machine Learning (ML) has widely been used for modeling and predicting physical systems. These techniques offer high expressive power and good generalizability for interpolation within observed data sets. However, the disadvantage of black-box models is that they underperform under blind conditions since no physical knowledge is incorporated. Physics-based ML aims to address this problem by retaining the mathematical flexibility of ML techniques while incorporating physics. In accord, this paper proposes to embed mechanics-based models into the mean function of a Gaussian Process (GP) model and characterize potential discrepancies through kernel machines. A specific class of kernel function is promoted, which has a connection with the gradient of the physics-based model with respect to the input and parameters and shares similarity with the exact Auto-covariance function of linear dynamical systems. The spectral properties of the kernel function enable considering dominant periodic processes originating from physics misspecification. Nevertheless, the stationarity of the kernel function is a difficult hurdle in the sequential processing of long data sets, resolved through hierarchical Bayesian techniques. This implementation is also advantageous to mitigate computational costs, alleviating the scalability of GPs when dealing with sequential data. Using numerical and experimental examples, potential applications of the proposed method to structural dynamics inverse problems are demonstrated.

Modeling host–microbiome interactions to improve mechanistic understanding of aphid vectored plant pathogens

Insect transmission of plant pathogens involves multi-layered interactions between vectors, viruses, host plants and environmental factors. Adding to the complexity of vector–virus relationships are diverse microbial communities, which are hypothesized to influence pathogen transmission. Although vector–virus interaction research has flourished, the role played by microbes in vector competence and disease epidemiology remains unclear in many pathosystems. We therefore aimed to develop a novel ecological modeling approach to identify environmental drivers of complex vector–virus–microbiome interactions, particularly differences in the abundance of microbial symbionts within vector microbiomes and relationships between symbionts and the probability of virus acquisition. Our approach combines established molecular tools for profiling microbial communities with underutilized Bayesian hierarchical modeling and data integration techniques. We used a globally relevant aphid–virus pathosystem to develop custom vector–microbiome models that incorporate environmental covariates (e.g., temperature, landcover) and applied them to individual vector symbionts to identify the extent to which environmental factors drive changes in microbial communities that then influence virus acquisition by the host aphid. Specifically, we focus on the aphid obligate symbiont (Buchnera) and a wide-spread facultative symbiont (Serratia) as proof of concept to develop models for two major vector species that include a single environmental covariate (i.e., temperature). Overall, we demonstrate how community-level modeling and microbiome profiling can identify candidate microbes and environmental variables associated with vector competence. Our modeling framework can accommodate a range of microbial symbionts with different abundances, overcome spatial misalignment of data streams, and is robust to varying levels of disease incidence. Results show Buchnera relative abundance is strongly negatively associated with virus acquisition by the vector S.avenae, but not R. padi. Although Serratia was not associated with R. padi vector competence, relative abundance was influenced by differences in spring temperatures. This work lays the foundation for developing a broader modeling framework for predicting disease dynamics in agroecosystems and deploying microbiome-targeted pest management tactics.

Interregional migration of construction workers in China: roles of employment opportunities and environment amenities

PurposeWithin the labor-intensive construction industry characterized by distinctly structural shortages in the labor force worldwide, efficient and effective migration of construction workers across regions is critical for the smooth operation of construction activities. This study aims to investigate how the interregional migration patterns of construction workers are impacted by the disparities in both employment opportunities and environment amenities between the origin and destination provinces.Design/methodology/approachDrawing on the push and pull theory and the archival data on 13,728 migrant construction workers in China, descriptive analyses are first performed to characterize the interregional migration patterns of the investigated construction workers. Combining regional data in the National Bureau of Statistics of China, this study uses hierarchical regression modeling techniques to empirically test the relative importance of the employment-related and environment-related factors in driving the interregional migration of construction workers after controlling for the effects of related economic and geographic factors.FindingsThe results provide evidence that the interregional migration of construction workers is principally driven by the disparities in employment opportunities while disparities in environment amenities (including climate comfort disparity, medical service disparity and educational service disparity) generally play much fewer substantive roles. With regard to the impacts of employment opportunities, the results provide evidence that compared with the disparity in job market size, the disparities in job income and industry development level are more significantly relevant factors, which positively pull and adversely push the interregional migration flows, respectively.Research limitations/implicationsThis study contributes to a deepened understanding of how workers specifically balance their employment and amenity needs to make temporary migration decisions in the “laggard” labor-intensive construction industry. This study also adds to the literature on population migration by characterizing the specific characteristics of construction workers and the temporary nature of the workers' migration activities. The findings hold important practical implications for construction organizations and policymakers for effectively managing the mobility of migrant construction workers.Originality/valueThe extant literature on migrant construction workers has primarily focused on the consequences of international migration and the generalization of empirical findings on population migration mechanisms in other domains to the construction industry is substantially limited by the specific characteristics of construction workers and the temporary nature of their migration activities. In addressing this gap, this study represents an exploratory effort to quantitatively characterize the interregional migration patterns of construction workers in the labor-intensive construction industry and examines the roles of employment opportunity and environmental amenity in driving interregional migration.

Long-term forecasting of a motor outcome following rehabilitation in chronic stroke via a hierarchical bayesian dynamic model

BackgroundGiven the heterogeneity of stroke, it is important to determine the best course of motor therapy for each patient, i.e., to personalize rehabilitation based on predictions of long-term outcomes. Here, we propose a hierarchical Bayesian dynamic (i.e., state-space) model (HBDM) to forecast long-term changes in a motor outcome due to rehabilitation in the chronic phase post-stroke.MethodsThe model incorporates the effects of clinician-supervised training, self-training, and forgetting. In addition, to improve forecasting early in rehabilitation, when data are sparse or unavailable, we use the Bayesian hierarchical modeling technique to incorporate prior information from similar patients. We use HBDM to re-analyze the Motor Activity Log (MAL) data of participants with chronic stroke included in two clinical trials: (1) the DOSE trial, in which participants were assigned to a 0, 15, 30, or 60-h dose condition (data of 40 participants analyzed), and (2) the EXCITE trial, in which participants were assigned a 60-h dose, in either an immediate or a delayed condition (95 participants analyzed).ResultsFor both datasets, HBDM accounts well for individual dynamics in the MAL during and outside of training: mean RMSE = 0.28 for all 40 DOSE participants (participant-level RMSE 0.26 ± 0.19—95% CI) and mean RMSE = 0.325 for all 95 EXCITE participants (participant-level RMSE 0.32 ± 0.31), which are small compared to the 0-5 range of the MAL. Bayesian leave-one-out cross-validation shows that the model has better predictive accuracy than static regression models and simpler dynamic models that do not account for the effect of supervised training, self-training, or forgetting. We then showcase model’s ability to forecast the MAL of “new” participants up to 8 months ahead. The mean RMSE at 6 months post-training was 1.36 using only the baseline MAL and then decreased to 0.91, 0.79, and 0.69 (respectively) with the MAL following the 1st, 2nd, and 3rd bouts of training. In addition, hierarchical modeling improves prediction for a patient early in training. Finally, we verify that this model, despite its simplicity, can reproduce previous findings of the DOSE trial on the efficiency, efficacy, and retention of motor therapy.ConclusionsIn future work, such forecasting models can be used to simulate different stages of recovery, dosages, and training schedules to optimize rehabilitation for each person.Trial registration This study contains a re-analysis of data from the DOSE clinical trial ID NCT01749358 and the EXCITE clinical trial ID NCT00057018

Uncovering the cognitive mechanisms underlying the gaze cueing effect.

The gaze cueing effect is the tendency for people to respond faster to targets appearing at locations gazed at by others, compared with locations gazed away from by others. The effect is robust, widely studied, and is an influential finding within social cognition. Formal evidence accumulation models provide the dominant theoretical account of the cognitive processes underlying speeded decision-making, but they have rarely been applied to social cognition research. In this study, using a combination of individual-level and hierarchical computational modelling techniques, we applied evidence accumulation models to gaze cueing data (three data sets total, N = 171, 139,001 trials) for the first time to assess the relative capacity that an attentional orienting mechanism and information processing mechanisms have for explaining the gaze cueing effect. We found that most participants were best described by the attentional orienting mechanism, such that response times were slower at gazed away from locations because they had to reorient to the target before they could process the cue. However, we found evidence for individual differences, whereby the models suggested that some gaze cueing effects were driven by a short allocation of information processing resources to the gazed at location, allowing for a brief period where orienting and processing could occur in parallel. There was exceptionally little evidence to suggest any sustained reallocation of information processing resources neither at the group nor individual level. We discuss how this individual variability might represent credible individual differences in the cognitive mechanisms that subserve behaviourally observed gaze cueing effects.

Immigrant and native's export benefiting from business collaborations: a global study

The authors hypothesised that export develops in the network of business collaborations that are embedded in migration status. In that, collaborative networking positively affects export performance and immigrant entrepreneurs enjoy higher collaborative networking than native entrepreneurs due to their advantage of being embedded in the home and the host country. Moreover, the advantage of being an immigrant promotes the benefits of collaborative networking for export compared to those of native entrepreneurs. A total of 47,200 entrepreneurs starting, running and owning firms in 71 countries were surveyed by Global Entrepreneurship Monitor and analysed through hierarchical linear modelling technique. Collaborative networking facilitated export and migration status influenced entrepreneurs' networking, in that, immigrant entrepreneurs had a higher level of collaborative networking than native entrepreneurs. Consequently, immigrant entrepreneurs seemed to have benefited from their network collaborations more than their native counterparts did. This study sheds light on how immigrant entrepreneurs' network collaborations can be effective for their exporting.

THE RELATIONSHIP BETWEEN RELIGIOSITY AND NURSING HOME SERVICE UTILIZATION: A LONGITUDINAL ANALYSIS

Abstract On any given day 3.9% to 4.2% of the US population receives nursing home (NH) care. Given the critical role NHs play in caring for older adults, research has identified various demographic, biological, psychological, and social factors associated with NH utilization. Few studies, however, have explored religiosity as a predictor; none, longitudinally. This study thus aims to examine how one’s religiosity (organizational, non-organizational, intrinsic) affects NH use. A total of n=7,564 respondents (>51 years) from the 2006 wave of the Health and Retirement Study, with no prior NH use, were assessed every two years, until 2014. Hierarchical modeling techniques (i.e., Generalized Estimating Equations Multinomial Logistic Regression) were used to model the relationship between religiosity and subsequent NH use, while controlling for other factors. Results indicate that high levels of organizational religiosity (i.e., service attendance) is associated with higher rates of NH utilization (B= 0.13, p < 0.001). Conversely, they indicate that higher non-organizational religiosity (i.e., private prayer) is associated with a lower likelihood of NH care use (B= -0.03, p < 0.05). Sex and race served as moderators in these relationships. The association between higher levels of service attendance and higher rates of NH utilization may reflect the sharing of information about available resources such as long-term care through one’s social support networks, including congregations. In contrast, individuals who were more likely to pray on their own, may lack the social support provided by congregations, and as a result, experienced more limited knowledge transfer about available resources such as NHs.

Robust optimization with uncertainty using a stochastic multivariate Gaussian process model

In robust parameter design (RPD), estimating the response surface model from the noisy experimental data is critical for parameter optimization. The multivariate Gaussian process (MGP) model has become a popular tool for response surface modelling. However, most existing MGP models may not effectively evaluate the uncertainty of noisy data. This article proposes a stochastic MGP for efficient emulation and robust optimization by considering uncertainty. Firstly, the hierarchical modelling technique is adopted to estimate the relationship model with noisy data. Secondly, the hyperparameters are estimated by the Gibbs technique, and the Bayesian averaging method is used to measure the parameter uncertainty. Finally, a novel optimization model integrated with the Bayesian averaging and quality loss function is developed to find the optimal solution. Two case studies are used to illustrate the effectiveness of the proposed method. The results show that the proposed approach obtains smaller quality loss than the existing ones.

Prevalence and factors associated with delayed antiretroviral therapy initiation among adults with HIV in Alebtong district, Northern Uganda: A facility-based study.

Globally, an estimated 36.7 million people were living with HIV (PLWH) and of these, 2.1 million were newly infected and 1.1 million died of AIDS in 2015. By 2016, only 67% of adults eligible for ART were enrolled in ART in Uganda. Delayed ART initiation has been shown to contribute to the continued transmission of HIV as well as to higher morbidity and mortality among persons living with HIV. Our study examined the prevalence and factors associated with delayed ART initiation among adults with HIV in Alebtong district, Northern Uganda. A cross-sectional study involving 432 adults living with HIV was conducted between March and June 2018 in Alebtong district. Quantitative data were collected using interviewer-administered questionnaires and desk reviews using a data extraction tool. A binary logistic regression using a hierarchical modelling technique was used at the multivariable level to determine associations at a 95% confidence interval and p<0.05 using SPSS Statistics software version 23.0. Overall, 432 participants were enrolled in the study, of whom 18.1% (78/432) had delayed ART initiation. After final adjustment, our key findings showed a significantly lower odds of delayed ART initiation among older respondents (aOR = 0.35, 95% CI: 0.16-0.76); adherence to HIV clinic appointments, (aOR = 0.06, 95% CI: 0.02-0.15); and linkage to the HIV clinic the same day HIV test was conducted (aOR = 0.21, 95% CI: 0.08-0.55). However, a significantly higher odds of delayed ART initiation was observed among those whose cultures do not support the use of ART (aOR = 10.62, 95% CI: 3.04-32.08). Reducing delayed ART initiation in the district requires strengthening the involvement of adolescents and young people in the HIVAIDS programming, scaling up the implementation of the same-day ART initiation policy, and addressing negative cultural beliefs affecting early ART initiation in the district.

Religion as a Micro and Macro Property: Investigating the Multilevel Relationship between Religion and Abortion Attitudes across the Globe

AbstractIn many countries, abortion is the most contested issue being publicly discussed, in part, because it has important implications for gender equality, health, family formation, politics, and views about morality. Personal religious beliefs are often seen as having an important role in shaping disapproving attitudes. However, little attention has been given to the country religious context for understanding public opinion about abortion. Drawing on ideas from cultural sociology, the anti-ascetic hypothesis, and the moral communities hypothesis, this study investigates the individual and country religious associations with abortion attitudes. Using hierarchical modelling techniques and survey data from over 70 nations, representing the majority of the world’s population, the multilevel roles of religion are investigated. Both personal and country levels of religious importance are associated with the disapproval of abortion, but there are few differences across religions. Laws allowing for abortion are marginally associated with attitudes and there is no moral communities effect, whereby overall levels of religious importance have a moderating influence on the relationship between personal religiosity and attitudes. However, individual religious importance appears to have a greater association with disapproval in richer, rather than poorer, countries, providing novel insight into why abortion has remained controversial in many countries, especially those with higher levels of economic development.

Estimation of the Covariance Matrix in Hierarchical Bayesian Spatio-Temporal Modeling via Dimension Expansion.

Ozone concentrations are key indicators of air quality. Modeling ozone concentrations is challenging because they change both spatially and temporally with complicated structures. Missing data bring even more difficulties. One of our interests in this paper is to model ozone concentrations in a region in the presence of missing data. We propose a method without any assumptions on the correlation structure to estimate the covariance matrix through a dimension expansion method for modeling the semivariograms in nonstationary fields based on the estimations from the hierarchical Bayesian spatio-temporal modeling technique (Le and Zidek). Further, we apply an entropy criterion (Jin et al.) based on a predictive model to decide if new stations need to be added. This entropy criterion helps to solve the environmental network design problem. For demonstration, we apply the method to the ozone concentrations at 25 stations in the Pittsburgh region studied. The comparison of the proposed method and the one is provided through leave-one-out cross-validation, which shows that the proposed method is more general and applicable.

Illustrating a Species Sensitivity Distribution for Nano- and Microplastic Particles Using Bayesian Hierarchical Modeling.

Environmental contamination with nano‐ and microplastic (NMP) particles is an emerging global concern. The derivation of species sensitivity distributions (SSDs) is an essential step in estimating a hazardous concentration for 5% of the species (HC5), and this HC5 value is often used as a “safe” concentration in ecological risk assessment, that is, predicted‐no‐effect concentration. Although properties of plastics such as particle size can affect toxic effect concentrations, such influences have not yet been quantitatively considered in estimating SSDs for NMP particles. We illustrate a log‐normal SSD using chronic lowest‐observed‐effect concentrations (LOECs) of NMP particles from readily available toxicity data sets, considering the influence of particle size, polymer type, and freshwater or marine test media by adopting Bayesian hierarchical modeling techniques. Results of the hierarchical SSD modeling suggest that the SSD mean was negatively associated with particle size and was lower in marine media than in freshwater media. The posterior medians of the HC5 estimated from the LOEC‐based SSD varied by a factor of 10 depending on these factors (e.g., 1.8–20 μg/L for the particle size range of 0.1–5000 μm in the marine environment). Hierarchical SSD modeling allows us to clarify the influences of important factors such as NMP properties on effect concentrations, thereby helping to guide more relevant ecological risk assessments for NMP. Environ Toxicol Chem 2022;41:954–960. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.