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Background: One in six Mexican adults' lives with type 2 diabetes mellitus (T2DM), which is the third leading cause of death in the country. Analyzing the geographic distribution of T2DM mortality helps identify regions with higher mortality rates. This study aimed to examine the spatial patterns of mortality from type 2 diabetes mellitus (T2DM) across municipalities in Mexico and to analyze the main contextual factors linked to this cause of death in 2020. Methods: We employed a spatial Bayesian hierarchical regression model to estimate the risk and probability of death from type 2 diabetes mellitus (T2DM) across Mexico's municipalities. Results: The SMR results revealed geographic and age-specific patterns. Central Mexico and the Yucatán Peninsula exhibited the highest excess mortality rates. For the population under 50 years of age, municipalities in Oaxaca had the highest T2DM mortality rates, whereas those aged 50 years old and older had the highest rates in Tlaxcala and Puebla. Socioeconomic factors such as low levels of educational attainment, lack of health services, dietary deficiency, and marginalization were positively associated with increased T2DM mortality risk. By contrast, GDP per capita showed a negative association. High-risk areas for T2DM mortality were prominent along the south of the Pacific Coast, the Bajío, Central Mexico, and southern Yucatán for those under 50, and along a central strip extending to the Yucatán Peninsula for the older population. Significant uncertainties in mortality risk were identified, with Central Mexico, Oaxaca, Chiapas, and Tabasco showing high probabilities of excess risk for those under 50 years of age and extended risk areas along the Gulf of Mexico for those 50 years old and older. Conclusions: The assessment and identification of spatial distribution patterns associated with T2DM mortality, and its main contextual factors, are crucial for informing effective public health policies aimed at reducing the impact of this chronic disease in Mexico. [ABSTRACT FROM AUTHOR]

Background: Smoking is one of the leading causes of preventable mortality and morbidity worldwide, with the European Region having the highest prevalence of tobacco smoking among adults compared to other WHO regions. The Belgian Health Interview Survey (BHIS) provides a reliable source of national and regional estimates of smoking prevalence; however, currently there are no estimates at a smaller geographical resolution such as the municipality scale in Belgium. This hinders the estimation of the spatial distribution of smoking attributable mortality at small geographical scale (i.e., number of deaths that can be attributed to tobacco). The objective of this study was to obtain estimates of smoking prevalence in each Belgian municipality using BHIS and calculate smoking attributable mortality at municipality level.Methods: Data of participants aged 15 + on smoking behavior, age, gender, educational level and municipality of residence were obtained from the BHIS 2018. A Bayesian hierarchical Besag-York-Mollie (BYM) model was used to model the logit transformation of the design-based Horvitz-Thompson direct prevalence estimates. Municipality-level variables obtained from Statbel, the Belgian statistical office, were used as auxiliary variables in the model. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA). Deviance Information Criterion (DIC) and Conditional Predictive Ordinate (CPO) were computed to assess model fit. Population attributable fractions (PAF) were computed using the estimated prevalence of smoking in each of the 589 Belgian municipalities and relative risks obtained from published meta-analyses. Smoking attributable mortality was calculated by multiplying PAF with age-gender standardized and stratified number of deaths in each municipality.Results: BHIS 2018 data included 7,829 respondents from 154 municipalities. Smoothed estimates for current smoking ranged between 11% [Credible Interval 3;23] and 27% [21;34] per municipality, and for former smoking between 4% [0;14] and 34% [21;47]. Estimates of smoking attributable mortality constituted between 10% [7;15] and 47% [34;59] of total number of deaths per municipality.Conclusions: Within-country variation in smoking and smoking attributable mortality was observed. Computed estimates should inform local public health prevention campaigns as well as contribute to explaining the regional differences in mortality. [ABSTRACT FROM AUTHOR]

Objective: The outbreak of the novel coronavirus disease 2019 (COVID-19) is still affecting African countries. The pandemic presents challenges on how to measure governmental, and community responses to the crisis. Beyond health risks, the socio-economic implications of the pandemic motivated us to examine the transmission dynamics of COVID-19 and the impact of non-pharmaceutical interventions (NPIs). The main objective of this study was to assess the impact of BCG vaccination and NPIs enforced on COVID-19 case-death-recovery counts weighted by age-structured population in Ethiopia, Kenya, and Rwanda. We applied a semi-mechanistic Bayesian hierarchical model (BHM) combined with Markov Chain Monte Carlo (MCMC) simulation to the age-structured pandemic data obtained from the target countries. Results: The estimated mean effective reproductive number (Rt) for COVID-19 was 2.50 (C1: 1.99–5.95), 3.51 (CI: 2.28–7.28) and 3.53 (CI: 2.97–5.60) in Ethiopia, Kenya and Rwanda respectively. Our results indicate that NPIs such as lockdowns, and curfews had a large effect on reducing Rt. Current interventions have been effective in reducing Rt and thereby achieve control of the epidemic. Beyond age-structure and NPIs, we found no significant association between COVID-19 and BCG vaccine-induced protection. Continued interventions should be strengthened to control transmission of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Background: The sex ratio at birth (SRB; ratio of male to female births) in Nepal has been reported around the normal level on the national level. However, the national SRB could mask the disparity within the country. Given the demographic and cultural heterogeneities in Nepal, it is crucial to model Nepal SRB on the subnational level. Prior studies on subnational SRB in Nepal are mostly based on reporting observed values from surveys and census, and no study has provided probabilistic projections. We aim to estimate and project SRB for the seven provinces of Nepal from 1980 to 2050 using a Bayesian modeling approach.Methods: We compiled an extensive database on provincial SRB of Nepal, consisting 2001, 2006, 2011, and 2016 Nepal Demographic and Health Surveys and 2011 Census. We adopted a Bayesian hierarchical time series model to estimate and project the provincial SRB, with a focus on modelling the potential SRB imbalance.Results: In 2016, the highest SRB is estimated in Province 5 (Lumbini Pradesh) at 1.102, corresponding to 110.2 male births per 100 female births, with a 95% credible interval (1.044, 1.127) and the lowest SRB is in Province 2 at 1.053 (1.035, 1.109). The SRB imbalance probabilities in all provinces are generally low and vary from 16% in Province 2 to 81% in Province 5 (Lumbini Pradesh). SRB imbalances are estimated to have begun at the earliest in 2001 in Province 5 (Lumbini Pradesh) with a 95% credible interval (1992, 2022) and the latest in 2017 (1998, 2040) in Province 2. We project SRB in all provinces to begin converging back to the national baseline in the mid-2030s. By 2050, the SRBs in all provinces are projected to be around the SRB baseline level.Conclusions: Our findings imply that the majority of provinces in Nepal have low risks of SRB imbalance for the period 1980-2016. However, we identify a few provinces with higher probabilities of having SRB inflation. The projected SRB is an important illustration of potential future prenatal sex discrimination and shows the need to monitor SRB in provinces with higher possibilities of SRB imbalance. [ABSTRACT FROM AUTHOR]

Background: Invasive reptiles pose a serious threat to global biodiversity, but early detection of individuals in an incipient population is often hindered by their cryptic nature, sporadic movements, and variation among individuals. Little is known about the mechanisms that affect the movement of these species, which limits our understanding of their dispersal. Our aim was to determine whether translocation or small-scale landscape features affect movement patterns of brown treesnakes (Boiga irregularis), a destructive invasive predator on the island of Guam. Methods: We conducted a field experiment to compare the movements of resident (control) snakes to those of snakes translocated from forests and urban areas into new urban habitats. We developed a Bayesian hierarchical model to analyze snake movement mechanisms and account for attributes unique to invasive reptiles by incorporating multiple behavioral states and individual heterogeneity in movement parameters. Results: We did not observe strong differences in mechanistic movement parameters (turning angle or step length) among experimental treatment groups. We found some evidence that translocated snakes from both forests and urban areas made longer movements than resident snakes, but variation among individuals within treatment groups weakened this effect. Snakes translocated from forests moved more frequently from pavement than those translocated from urban areas. Snakes translocated from urban areas moved less frequently from buildings than resident snakes. Resident snakes had high individual heterogeneity in movement probability. Conclusions: Our approach to modeling movement improved our understanding of invasive reptile dispersal by allowing us to examine the mechanisms that influence their movement. We also demonstrated the importance of accounting for individual heterogeneity in population-level analyses, especially when management goals involve eradication of an invasive species. [ABSTRACT FROM AUTHOR]

Background: Reproductive containment provides an opportunity to implement a staged-release strategy for genetic control of malaria vectors, in particular allowing predictions about the spread and persistence of (self-limiting) sterile and male-biased strains to be compared to outcomes before moving to (self-sustaining) gene-drive strains. Methods: In this study, we: (i) describe a diffusion–advection–reaction model of the spread and persistence of a single cohort of male mosquitoes; (ii) elicit informative prior distributions for model parameters, for wild-type (WT) and genetically modified dominant sterile strains (DSM); (iii) estimate posterior distributions for WT strains using data from published mark-recapture-release (MRR) experiments, with inference performed through the Delayed-Rejection Adaptive Metropolis algorithm; and (iv) weight prior distributions, in order to make predictions about genetically modified strains using Bayes factors calculated for the WT strains. Results: If a single cohort of 5000 genetically modified dominant sterile male mosquitoes are released at the same location as previous MRR experiments with their WT counterparts, there is a 90% probability that the expected number of released mosquitoes will fall to < 1 in 10 days, and that by 12 days there will be a 99% probability that no mosquitoes will be found more than 150 m from the release location. Conclusions: Spread and persistence models should form a key component of risk assessments of novel genetic control strategies for malaria vectors. Our predictions, used in an independent risk assessment, suggest that genetically modified sterile male mosquitoes will remain within the locality of the release site, and that they will persist for a very limited amount of time. Data gathered following the release of these mosquitoes will enable us to test the accuracy of these predictions and also provide a means to update parameter distributions for genetic strains in a coherent (Bayesian) framework. We anticipate this will provide additional insights about how to conduct probabilistic risk assessments of stage-released genetically modified mosquitoes. [ABSTRACT FROM AUTHOR]

Background: Like many developing countries, Thailand has experienced a rapid rise in obesity, accompanied by a rapid change in occupational structure. It is plausible that these two trends are related, with movement into sedentary occupations leading to increases in obesity. National health examination survey data contains information on obesity and socioeconomic conditions that can help untangle the relationship, but analysis is challenging because of small sample sizes.Methods: This paper explores the relationship between occupation and obesity using data on 10,127 respondents aged 20-59 from the 2009 National Health Examination Survey. Obesity is measured using waist circumference. Modelling is carried out using an approach known as Multiple Regression with Post-Stratification (MRP). We use Bayesian hierarchical models to construct prevalence estimates disaggregated by age, sex, education, urban-rural residence, region, and occupation, and use census population weights to aggregate up. The Bayesian hierarchical model is designed to protect against overfitting and false discovery, which is particularly important in an exploratory study such as this one.Results: There is no clear relationship between the overall sedentary nature of occupations and obesity. Instead, obesity appears to vary occupation by occupation. For instance, women in professional occupations, and men who are agricultural or fishery workers, have relatively low rates of obesity.Conclusion: Bayesian hierarchical models plus post-stratification offers new possibilities for using surveys to learn about complex health issues. [ABSTRACT FROM AUTHOR]

Background: Ambitious global goals have been established to provide universal access to affordable modern contraceptive methods. To measure progress toward such goals in populous countries like Nigeria, it's essential to characterize the current levels and trends of family planning (FP) indicators such as unmet need and modern contraceptive prevalence rates (mCPR). Moreover, the substantial heterogeneity across Nigeria and scale of programmatic implementation requires a sub-national resolution of these FP indicators. The aim of this study is to estimate the levels and trends of FP indicators at a subnational scale in Nigeria utilizing all available data and accounting for survey design and uncertainty.Methods: We utilized all available cross-sectional survey data from Nigeria including the Demographic and Health Surveys, Multiple Indicator Cluster Surveys, National Nutrition and Health Surveys, and Performance, Monitoring, and Accountability 2020. We developed a hierarchical Bayesian model that incorporates all of the individual level data from each survey instrument, accounts for survey uncertainty, leverages spatio-temporal smoothing, and produces probabilistic estimates with uncertainty intervals.Results: We estimate that overall rates and trends of mCPR and unmet need have remained low in Nigeria: the average annual rate of change for mCPR by state is 0.5% (0.4%,0.6%) from 2012-2017. Unmet need by age-parity demographic groups varied significantly across Nigeria; parous women express much higher rates of unmet need than nulliparous women.Conclusions: Understanding the estimates and trends of FP indicators at a subnational resolution in Nigeria is integral to inform programmatic decision-making. We identify age-parity-state subgroups with large rates of unmet need. We also find conflicting trends by survey instrument across a number of states. Our model-based estimates highlight these inconsistencies, attempt to reconcile the direct survey estimates, and provide uncertainty intervals to enable interpretation of model and survey estimates for decision-making. [ABSTRACT FROM AUTHOR]

Background: Although human leukocyte antigen (HLA) genotyping based on amplicon, whole exome sequence (WES), and RNA sequence data has been achieved in recent years, accurate genotyping from whole genome sequence (WGS) data remains a challenge due to the low depth. Furthermore, there is no method to identify the sequences of unknown HLA types not registered in HLA databases. Results: We developed a Bayesian model, called ALPHLARD, that collects reads potentially generated from HLA genes and accurately determines a pair of HLA types for each of HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, and -DRB1 genes at 3rd field resolution. Furthermore, ALPHLARD can detect rare germline variants not stored in HLA databases and call somatic mutations from paired normal and tumor sequence data. We illustrate the capability of ALPHLARD using 253 WES data and 25 WGS data from Illumina platforms. By comparing the results of HLA genotyping from SBT and amplicon sequencing methods, ALPHLARD achieved 98.8% for WES data and 98.5% for WGS data at 2nd field resolution. We also detected three somatic point mutations and one case of loss of heterozygosity in the HLA genes from the WGS data. Conclusions: ALPHLARD showed good performance for HLA genotyping even from low-coverage data. It also has a potential to detect rare germline variants and somatic mutations in HLA genes. It would help to fill in the current gaps in HLA reference databases and unveil the immunological significance of somatic mutations identified in HLA genes. [ABSTRACT FROM AUTHOR]

Background: Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).Methods: Using nationally representative household survey data from the 2013 Demographic and Health Survey (DHS) in Namibia, individual-level responses (n = 1138) were linked to theoretical estimates of travel time to the nearest public or private health facility. Bayesian Item Response Theory (IRT) models were fitted via Markov Chain Monte Carlo (MCMC) simulation to estimate parameters related to fever treatment and estimate probability of treatment for children under five years. Different models were implemented to evaluate computational needs and the effect of including predictor variables such as rurality. The mean treatment rates were then estimated at regional level.Results: Modelling results suggested probability of fever treatment was highest in regions with relatively high incidence of malaria historically. The minimum predicted threshold probability of seeking treatment was 0.3 (model 1: 0.340; 95% CI 0.155-0.597), suggesting that even in populations at large distances from facilities, there was still a 30% chance of an individual seeking treatment for fever. The agreement between correctly predicted probability of treatment at individual level based on a subset of data (n = 247) was high (AUC = 0.978), with a sensitivity of 96.7% and a specificity of 75.3%.Conclusion: We have shown how individual responses in national surveys can be transformed to probabilistic measures comparable at population level. Our analysis of household survey data on fever suggested a 30% baseline threshold for fever treatment in Namibia. However, this threshold level is likely to vary by country or endemicity. Although our focus was on fever treatment, the methodology outlined can be extended to multiple health seeking behaviours captured in routine national survey data and to other infectious diseases. [ABSTRACT FROM AUTHOR]

Background: The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. Results: In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. Conclusions: Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature. [ABSTRACT FROM AUTHOR]

Background: Epidemiological studies suggest that air pollution is adversely associated with pregnancy outcomes. Such associations may be modified by spatially-varying factors including socio-demographic characteristics, land-use patterns and unaccounted exposures. Yet, few studies have systematically investigated the impact of these factors on spatial variability of the air pollution's effects. This study aimed to examine spatial variability of the effects of air pollution on term birth weight across Census tracts and the influence of tract-level factors on such variability.Methods: We obtained over 900,000 birth records from 2001 to 2008 in Los Angeles County, California, USA. Air pollution exposure was modeled at individual level for nitrogen dioxide (NO2) and nitrogen oxides (NOx) using spatiotemporal models. Two-stage Bayesian hierarchical non-linear models were developed to (1) quantify the associations between air pollution exposure and term birth weight within each tract; and (2) examine the socio-demographic, land-use, and exposure-related factors contributing to the between-tract variability of the associations between air pollution and term birth weight.Results: Higher air pollution exposure was associated with lower term birth weight (average posterior effects: -14.7 (95 % CI: -19.8, -9.7) g per 10 ppb increment in NO2 and -6.9 (95 % CI: -12.9, -0.9) g per 10 ppb increment in NOx). The variation of the association across Census tracts was significantly influenced by the tract-level socio-demographic, exposure-related and land-use factors. Our models captured the complex non-linear relationship between these factors and the associations between air pollution and term birth weight: we observed the thresholds from which the influence of the tract-level factors was markedly exacerbated or attenuated. Exacerbating factors might reflect additional exposure to environmental insults or lower socio-economic status with higher vulnerability, whereas attenuating factors might indicate reduced exposure or higher socioeconomic status with lower vulnerability.Conclusions: Our Bayesian models effectively combined a priori knowledge with training data to infer the posterior association of air pollution with term birth weight and to evaluate the influence of the tract-level factors on spatial variability of such association. This study contributes new findings about non-linear influences of socio-demographic factors, land-use patterns, and unaccounted exposures on spatial variability of the effects of air pollution. [ABSTRACT FROM AUTHOR]