Bayesian Geostatistical Models Research Articles

Spatial co-distribution of tuberculosis prevalence and low BCG vaccination coverage in Ethiopia

While bacille-calmette–guerin (BCG) vaccination is one of the recommended strategies for preventing tuberculosis (TB), its coverage is low in several countries, including Ethiopia. This study investigated the spatial co-distribution and drivers of TB prevalence and low BCG coverage in Ethiopia. This ecological study was conducted using data from a national TB prevalence survey and the Ethiopian demographic and health survey (EDHS) to map the spatial co-distribution of BCG vaccination coverage and TB prevalence. A Bayesian geostatistical model was built to identify the drivers for the spatial distribution of TB prevalence and low BCG vaccination coverage. BCG vaccination coverage was defined as the number of children who received the vaccine divided by the total number of children born within five years preceding the EDHS surveys. Parameter estimation was done using binary logistic regression. Prediction maps for the co-distribution of high TB prevalence and low BCG vaccination coverage were created by overlying spatial prediction surfaces of the two outcomes. Posterior means and a 95% Bayesian credible interval (CrI) were used to summarize the parameters of the model. The national prevalence was 0.40% (95% confidence interval (CI) 0.34%, 0.47%) for TB and 47% (95% CI 46%, 48%) for vaccination coverage. Substantial spatial variation in TB prevalence and low BCG coverage was observed at a regional and local level, particularly in border areas of the country, including the Somali, Afar, and Oromia regions. Approximately 58% of the pixels (i.e., geographical area or spatial units) with high TB prevalence exhibited low BCG coverage in the same location. While travel time to cities (Mean = 0.28, 95% BCI: 0.15, 0.41) and distance to health facilities (Mean = 0.43, 95% CI 0.22, 0.63), were positively associated, population density (Mean = −0.04, 95% BCI −0.05, −0.02) was negatively associated, with the proportion of unvaccinated children for BCG indicating areas near health facilities and cities have better BCG coverage. However, there were no significant predictors for TB prevalence. Substantial spatial co-distribution between high TB prevalence and low BCG coverage was observed in some parts of the country, indicating that there are areas where the TB burden is not being adequately managed through the provision of vaccines in Ethiopia. Scaling up BCG vaccination coverage and TB diagnosis and treatment through improving access to health services in border regions such as Somalia and Afar would be important to reduce the prevalence of TB in Ethiopia.

Geospatial distribution of Hepatitis E seroprevalence in Nepal, 2021.

Hepatitis E virus (HEV) causes acute jaundice and poses an important public health problem in low- and middle-income countries. Limited surveillance capacity and suboptimal access to diagnostics leads to under-reporting of HEV infections in affected countries, including Nepal. Serum antibodies against HEV are indicative of past infection. We analyzed existing samples from a nationally representative serosurvey to describe the geospatial distribution and factors associated with HEV seroprevalence in Nepal, as a proxy for infection. A nationally representative cross-sectional serosurvey of 3,922 individuals ≥2 years old from 975 households spread across 65 wards throughout Nepal was conducted between November 2021 and January 2022. Bio-banked samples were tested for anti-HEV IgG. Seroprevalence and its 95% confidence interval were estimated by age, sex, ecological region, municipality type, and other waterborne-disease related risk factors. Bayesian geostatistical models were fitted to observed seroprevalence data and used to generate high-resolution maps of seroprevalence across Nepal. Available samples from 3,707 participants were tested for anti-HEV IgG, and 3,703 were used for final analysis. We found 20.8% (95% CI: 19.5-22.2) of participants had evidence of prior HEV infection. HEV seroprevalence increased with age, and was higher in males (23.5%, 95% CI: 21.5-25.5) than in females (18.6%, 95% CI: 16.9-20.3). Seroprevalence in hilly (28.9%, 95% CI: 26.6-31.2) and mountain (24.6%, 95% CI: 18.8-30.5) regions were significantly higher than in terai (14.2%, 95% CI: 12.7-15.8). While there was no significant difference between urban and rural populations, the predicted seroprevalence was highest in Kathmandu, the capital of Nepal, reaching seroprevalence of 50% in some selected area. No statistically significant differences were found for wealth quintile, water source, and toilet facility. This study provides population-based serologic evidence that HEV is endemic in Nepal, with the greatest risk of infection in Kathmandu.

Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests

BackgroundUnderstanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass.ResultsWe estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data.ConclusionsBy applying a relatively new Bayesian geostatistical modeling method to a novel forest type, our study produced the most accurate and precise aboveground biomass estimates to date for Sierra Nevada subalpine forests at 30 m pixel and management unit scales. Our estimates of total aboveground biomass within the management units had low uncertainty and can be used effectively in carbon accounting and carbon trading markets.

Risk profiling of tobacco epidemic and estimated number of smokers living in China: a cross-sectional study based on PBICR

BackgroundEvidence on the prevalence of smoking in China remains insufficient, with most previous studies focusing on a single region. However, smoking prevalence exhibits significant inequalities across the entire country. This study aimed to evaluate the risk of tobacco prevalence across the country, taking into account spatial inequalities.MethodsThe data used in this study were collected in 23 provinces, 5 autonomous regions, and 4 municipalities directly under the central government in 2022. Large population survey data were used, and a Bayesian geostatistical model was employed to investigate smoking prevalence rates across multiple spatial domains.FindingsSignificant spatial variations were observed in smokers and exposure to secondhand smoke across China. Higher levels of smokers and secondhand smoke exposure were observed in western and northeastern regions. Additionally, the autonomous region of Tibet, Shanghai municipality, and Yunnan province had the highest prevalence of smokers, while Tibet, Qinghai province, and Yunnan province had the highest prevalence of exposure to secondhand smoke.ConclusionWe have developed a model-based, high-resolution nationwide assessment of smoking risks and employed rigorous Bayesian geostatistical models to help visualize smoking prevalence predictions. These prediction maps provide estimates of the geographical distribution of smoking, which will serve as strong evidence for the formulation and implementation of smoking cessation policies.HighlightsOur study investigated the prevalence of smokers and exposure to secondhand smoke in different spatial areas of China and explored various factors influencing the smoking prevalence. For the first time, our study applied Bayesian geostatistical modeling to generate a risk prediction map of smoking prevalence, which provides a more intuitive and clear understanding of the spatial disparities in smoking prevalence across different geographical regions, economic levels, and development status. We found significant spatial variations in smokers and secondhand smoke exposure in China, with higher rates in the western and northeastern regions.

Geospatial joint modeling of vector and parasite serology to microstratify malaria transmission

The World Health Organization identifies a strong surveillance system for malaria and its mosquito vector as an essential pillar of the malaria elimination agenda. Anopheles salivary antibodies are emerging biomarkers of exposure to mosquito bites that potentially overcome sensitivity and logistical constraints of traditional entomological surveys. Using samples collected by a village health volunteer network in 104 villages in Southeast Myanmar during routine surveillance, the present study employs a Bayesian geostatistical modeling framework, incorporating climatic and environmental variables together with Anopheles salivary antigen serology, to generate spatially continuous predictive maps of Anopheles biting exposure. Our maps quantify fine-scale spatial and temporal heterogeneity in Anopheles salivary antibody seroprevalence (ranging from 9 to 99%) that serves as a proxy of exposure to Anopheles bites and advances current static maps of only Anopheles occurrence. We also developed an innovative framework to perform surveillance of malaria transmission. By incorporating antibodies against the vector and the transmissible form of malaria (sporozoite) in a joint Bayesian geostatistical model, we predict several foci of ongoing transmission. In our study, we demonstrate that antibodies specific for Anopheles salivary and sporozoite antigens are a logistically feasible metric with which to quantify and characterize heterogeneity in exposure to vector bites and malaria transmission. These approaches could readily be scaled up into existing village health volunteer surveillance networks to identify foci of residual malaria transmission, which could be targeted with supplementary interventions to accelerate progress toward elimination.

Leveraging climate and remote sensing metrics for predicting forest carbon stock using Bayesian geostatistical modelling under a projected climate warming in Zimbabwe

Climate change, driven by escalating carbon dioxide (CO2) emissions, poses a significant threat to forest ecosystems and the livelihoods of communities reliant on them, especially for the global south countries and regions like the eastern highlands of Zimbabwe. The 2000 land redistribution programme reduced buffer zones between ecologically sensitive forests and land reform beneficiaries near major carbon reservoirs. In light of these challenges, this study aimed to assess the potential effects of climate change on a strategically important plantation forest ecosystem in Zimbabwe's eastern highlands. Using data from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) of the Intergovernmental Panel on Climate Change (IPCC), we modelled and predicted changes in forest carbon (C) stock density under different climate scenarios: current (1970–2000), SSP5–4.5, and SSP5–8.5. Employing a hierarchical Bayesian geostatistical approach, we compared the baseline scenario (1970–2000) with projected scenarios (RCP4.5 and RCP8.5) for 2075 to estimate changes in forest carbon stock distribution. Our results indicated a decline in carbon stock concentration under future climate scenarios, reflecting the adverse impact of greenhouse gas emissions on forest growth. We found that the projected range of forest carbon stock under the RCP8.5 scenario for 2075 is notably lower (2≤MgCha−1≤44.9) than that of the baseline period (1970–2000) (1≤MgCha−1≤97), suggesting a substantial reduction in carbon storage. As the difference in posterior mean C stock (μ̅1−μ̅2), 52.1 MgCha-1 is well above zero, we deduce that the posterior mean C stock distribution of the projected future RCP8.5 2075 climate projection is indeed credibly different from the current (1970–2000) climate scenario. Additionally, there is a high probability (>90%) that forest plantations will be adversely affected by the business-as-usual climate warming projection. Overall, our findings highlight the urgent need for climate change mitigation strategies, such as reforestation programs and careful selection of tree species for plantations, to safeguard forest ecosystems and the communities dependent on them. These insights are crucial for informing effective adaptation measures in the face of future climate uncertainties.

Spatio-temporal spread of artemisinin resistance in Southeast Asia.

Current malaria elimination targets must withstand a colossal challenge-resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria-related deaths are set to increase substantially. Spatial information on the changing levels of artemisinin resistance in Southeast Asia is therefore critical for health organisations to prioritise malaria control measures, but available data on artemisinin resistance are sparse. We use a comprehensive database from the WorldWide Antimalarial Resistance Network on the prevalence of non-synonymous mutations in the Kelch 13 (K13) gene, which are known to be associated with artemisinin resistance, and a Bayesian geostatistical model to produce spatio-temporal predictions of artemisinin resistance. Our maps of estimated prevalence show an expansion of the K13 mutation across the Greater Mekong Subregion from 2000 to 2022. Moreover, the period between 2010 and 2015 demonstrated the most spatial change across the region. Our model and maps provide important insights into the spatial and temporal trends of artemisinin resistance in a way that is not possible using data alone, thereby enabling improved spatial decision support systems on an unprecedented fine-scale spatial resolution. By predicting for the first time spatio-temporal patterns and extents of artemisinin resistance at the subcontinent level, this study provides critical information for supporting malaria elimination goals in Southeast Asia.

Spatial analysis of modern contraceptive use among women who need it in Ethiopia: Using geo-referenced data from performance monitoring for action.

The challenge of achieving maternal and neonatal health-related goals in developing countries is significantly impacted by high fertility rates, which are partly attributed to limited access to family planning and access to the healthcare systems. The most widely used indicator to monitor family planning coverage is the proportion of women in reproductive age using contraception (CPR). However, this metric does not accurately reflect the true family planning coverage, as it fails to account for the diverse needs of women in reproductive age. Not all women in this category require contraception, including those who are pregnant, wish to become pregnant, sexually inactive, or infertile. To effectively address the contraceptive needs of those who require it, this study aims to estimate family planning coverage among this specific group. Further, we aimed to explore the geographical variation and factors influencing contraceptive uptake of contraceptive use among those who need. We used data from the Performance Monitoring for Action Ethiopia (PMA Ethiopia) survey of women of reproductive age and the service delivery point (SDP) survey conducted in 2019. A total of 4,390 women who need contraception were considered as the analytical sample. To account for the study design, sampling weights were considered to compute the coverage of modern contraceptive use disaggregated by socio-demographic factors. Bayesian geostatistical modeling was employed to identify potential factors associated with the uptake of modern contraception and produce spatial prediction to unsampled locations. The overall weighted prevalence of modern contraception use among women who need it was 44.2% (with 95% CI: 42.4%-45.9%). Across regions of Ethiopia, contraceptive use coverage varies from nearly 0% in Somali region to 52.3% in Addis Ababa. The average nearest distance from a woman's home to the nearest SDP was high in the Afar and Somali regions. The spatial mapping shows that contraceptive coverage was lower in the eastern part of the country. At zonal administrative level, relatively high (above 55%) proportion of modern contraception use coverage were observed in Adama Liyu Zone, Ilu Ababor, Misrak Shewa, and Kefa zone and the coverage were null in majority of Afar and Somali region zones. Among modern contraceptive users, use of the injectable dominated the method-mix. The modeling result reveals that, living closer to a SDP, having discussions about family planning with the partner, following a Christian religion, no pregnancy intention, being ever pregnant and being young increases the likelihood of using modern contraceptive methods. Areas with low contraceptive coverage and lower access to contraception because of distance should be prioritized by the government and other supporting agencies. Women who discussed family planning with their partner were more likely to use modern contraceptives unlike those without such discussion. Thus, to improve the coverage of contraceptive use, it is very important to encourage/advocate women to have discussions with their partner and establish movable health systems for the nomadic community.

High-resolution mapping of age- and gender-specific risk of Clonorchis sinensis infection risk in Guangdong, China: a geostatistical modeling study

BackgroundThe latest national survey on the distribution of human parasites in China demonstrated that Guangdong was among the endemic provinces with the highest Clonorchis sinensis infection rates. High-resolution, age- and gender-specific risk maps of the temporal and spatial distributions are essential for the targeted control work.MethodsDisease data on the prevalence of C.sinensis infection from 1990 onwards, either age- and gender-specific or aggregated across age and gender, were collected through systematic review and four large-scale surveys in Guangdong Province. Environmental and socioeconomic variables were obtained from open-access databases and employed as potential predictors. A Bayesian geostatistical model was developed to estimate the C.sinensis infection risk at high spatial resolution.ResultsThe final dataset included 606 surveys at 463 unique locations for C.sinensis infection. Our findings suggested that following an initial increase and stabilization, the overall population-adjusted prevalence had declined to 2.2% (95% Bayesian credible interval: 1.7–3.0%) in the period of 2015 onwards. From 2015 onwards, moderate and high infection risks were found in the northern regions (e.g. Heyuan and Shaoguan cities) and the southern Pearl River Delta (e.g. Foshan, Zhongshan, Zhuhai and Jiangmen cities), respectively. Age- and gender-specific risk maps revealed that males had a higher infection risk than females, and the infection risk was higher in adults compared to children.ConclusionsOur high-resolution risk maps of C. sinensis infection in Guangdong Province identified the spatial, temporal, age and gender heterogeneities, which can provide useful information assisting tailored control strategies.Graphical

A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems

ABSTRACT Modeling of environmental phenomena is usually confounded by the influence of multiple factors existing at different time and spatial scales. Bayesian modeling is presumed to be the best approach for modeling such complex systems. Using a Bayesian hierarchical inferential framework, we employed a multi-source data approach (i.e. remote sensing derived anthropogenic, climatic and topographic set of variables) to model Carbon (C) stock in a managed plantation forest ecosystem in Zimbabwe’s Eastern Highlands. We therefore investigated how two related multi-data sources of new generation remote sensing derived ancillary information influence C stock prediction required for building sustainable capacity in C monitoring and reporting. Two mainstream models constructed from Landsat-8 and Sentinel-2 derived vegetation indices coupled with climatic and topographic covariates were used to predict C stocks using forest inventory data collected using spatial coverage sampling. A multi-source data driven approach to C stock prediction yielded slightly lower predictions for both the Landsat-8 ( 1 ≤ M g C h a − 1 ≤ 135 ) and the Sentinel-2 ( 1 ≤ M g C h a − 1 ≤ 130 ) -based C stock models than C stock predictions published in related studies. Distance to settlements ( D I S T ) and N D V I are significant predictors of C stock with the Sentinel-2-based C stock model outperforming its Landsat-8 model variant in terms of prediction accuracy. The Sentinel-2-based C stock model resulted in a 1.17 MgCha−1 Root Mean Square Error (RMSE) with a ( 1 ≤ M g C h a − 1 ≤ 4 ) 95% credible interval whilst the Landsat-8-based C stock counterpart gave a 2.16 MgCha−1 RMSE with a ( 2 ≤ M g C h a − 1 ≤ 4 ) associated 95% credible interval. Despite a multi-source data prediction approach to the modeling of C stock in a managed plantation forest ecosystem set-up, the issues of scale still play a major role in modeling spatial variability of natural resource variables. Both climatic and topographic derived ancillary data are not significant predictors of C stock under the present modeling conditions. Accurate and precise accounting of C stock for climate change mitigation and action can best be done at landscape scales rather than local scale as the scale of variation for climate-change-related variables vary at larger spatial scales than the ones utilized in the present study.

Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria.

The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.

Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model

Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC), and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched Ocean and Land Color Imager (OLCI) sensor onboard Sentinel-3 (S3) provides images with comparable spatial and spectral resolution with MODIS data. However, the use of S3 OLCI imageries for vegetation variable estimation is rarely evaluated. This study evaluated the potential of S3 OLCI and MODIS data for estimating grassland LAI, FVC, and AGB in the eastern Eurasian steppe. A Bayesian spatial model (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) was used to address spatial autocorrelation of in-situ observation data and to enhance our predictions. Our results showed that the models based on S3 OLCI data presented higher accuracy than models with MODIS data. The RMSEs decreased by 3.7–10.8 %, 3.7–7.5 %, and 1.6–14.2 % for LAI, FVC, and AGB predictions, respectively. Through combinations of multiple predictors, we confirmed the robustness of red edge bands for grassland variable estimation, the models employing red edge variables yielded 3.5 %, 3.2 %, and 0.4 % lower RMSEs than models with conventional visible and NIR bands for LAI, FVC, and AGB prediction, respectively. INLA-SPDE spatial model produced lower bias and higher prediction accuracy than random forest and random forests kriging method in most of the models; the INLA-SPDE predicted LAI and FVC maps also showed a better agreement with ground observations than MODIS and PROBA-V land products.

Distribution and treatment needs of soil-transmitted helminthiasis in Bangladesh: A Bayesian geostatistical analysis of 2017-2020 national survey data.

In Bangladesh, preventive chemotherapy targeting soil-transmitted helminth (STH) infections in school-age children has been implemented since 2008. To evaluate the success of this strategy, surveys were conducted between 2017 and 2020 in 10 out of 64 districts. We estimate the geographic distribution of STH infections by species at high spatial resolution, identify risk factors, and estimate treatment needs at different population subgroups. Bayesian geostatistical models were fitted to prevalence data of each STH species. Climatic, environmental, and socioeconomic predictors were extracted from satellite images, open-access, model-based databases, and demographic household surveys, and used to predict the prevalence of infection over a gridded surface at 1 x 1 km spatial resolution across the country, via Bayesian kriging. These estimates were combined with gridded population data to estimate the number of required treatments for different risk groups. The population-adjusted prevalence of Ascaris lumbricoides, Trichuris trichiura, and hookworm across all ages is estimated at 9.9% (95% Bayesian credible interval: 8.0-13.0%), 4.3% (3.0-7.3%), and 0.6% (0.4-0.9%), respectively. There were 24 out of 64 districts with an estimated population-adjusted STH infection prevalence above 20%. The proportion of households with improved sanitation showed a statistically important, protective association for both, A. lumbricoides and T. trichiura prevalence. Precipitation in the driest month of the year was negatively associated with A. lumbricoides prevalence. High organic carbon concentration in the soil's fine earth fraction was related to a high hookworm prevalence. Furthermore, we estimated that 30.5 (27.2; 36.0) million dosages of anthelmintic treatments for school-age children were required per year in Bangladesh. For each of the STH species, the prevalence was reduced by at least 80% since treatment was scaled up more than a decade ago. The current number of deworming dosages could be reduced by up to 61% if the treatment strategy was adapted to the local prevalence.

Revised EU and WHO air quality thresholds: Where does Europe stand?

One year after the new WHO Global Air Quality Guidelines were published, the European Commission proposed revisions to the Ambient Air Quality Directives, seeking to align European Union air quality standards more closely with WHO recommendations. Here, we employed Bayesian geostatistical regression models to estimate at 1km2 spatial resolution the number of Europeans that would be currently exposed to levels above the new suggested thresholds for the three most harmful pollutants, namely PM2.5, PM10, and NO2. The results have shown that in 2021, 97.6% (95% credible interval: 95.1, 99.2%) of the total European population was exposed to PM2.5 levels exceeding the new WHO annual limit values, while 47.5% (36.2, 59.5%) lived in areas where the revised EU thresholds were exceeded. For PM10, the proportion of people exposed to concentrations above the WHO annual thresholds was estimated at 62.5% (54.0, 70.9%) and at 29.6 (21.9, 37.0%) above the EU values, whereas for NO2, the estimates were at 67.4% (57.9, 75.8%) and 20.2% (13.3, 28.5%), respectively. Our high-resolution model-based predictions are readily available to support policy makers and scientists working toward achieving the ambitious European zero-pollution vision for 2050.

The risk of developing postmenopausal breast cancer from ambient exposures to selected volatile organic compounds

ObjectivesTo estimate associations between incident postmenopausal breast cancer and environmental concentrations of five ambient volatile organic compounds (VOCs): benzene, hexane; ethylbenzene; n-decane; and 1,2,4-trimethylbenzene. MethodsWe conducted in Montreal a hospital-based case-control study between 1996 and 1997 and a population-based study between 2008 and 2011 of incident postmenopausal breast cancer. We measured five VOCs at about 130 locations during three sampling periods in 2005–2006 and we then used Bayesian geostatistical models to estimate concentrations of the VOCs at residential locations of participants in the two studies at the time of recruitment. We developed a directed acyclic graph to account for confounding but because of concerns with mis-specified models we used a series of unconditional logistic regression models to adjust for different combinations of personal risk factors, occupational exposures attributed by experts, air pollution data, and contextual variables. We estimated odds ratios (OR) for increases in concentrations across the interquartile ranges of the VOCs and associated 95% confidence intervals (CI). ResultsIn each study, we found that the ORs for each VOC were similar across the statistical models. In the hospital-based study, the ORs for the five VOCs varied between 0.77 and 1.04. In the population-based study, higher ORs were found for three VOCs: benzene, ORModel-7 = 1.12 (95%CI: 0.95, 1.31); hexane, ORModel-7 = 1.11 (95%CI: 0.97, 1.27); and 1,2,4-trimethyl-benzene, ORModel-7 = 1.07 (95%CI: 0.88, 1.31). We did not find important differences in sub-analyses by hormonal phenotype. ConclusionsIn the hospital-based study we did not find evidence of associations with these VOCs but in the population-based study there were indications of positive associations for environmental exposures to benzene and hexane.

Mapping the timeliness of routine childhood vaccination in The Gambia: A spatial modelling study

IntroductionTimeliness of routine vaccination shapes childhood infection risk and thus is an important public health metric. Estimates of indicators of the timeliness of vaccination are usually produced at the national or regional level, which may conceal epidemiologically relevant local heterogeneities and makeitdifficultto identify pockets of vulnerabilities that could benefit from targeted interventions. Here, we demonstrate the utility of geospatial modelling techniques in generating high-resolution maps of the prevalence of delayed childhood vaccination in The Gambia. To guide local immunisation policy and prioritize key interventions, we also identified the districts with a combination of high estimated prevalence and a significant population of affected infants. MethodsWe used the birth dose of the hepatitis-B vaccine (HepB0), third-dose of the pentavalent vaccine (PENTA3), and the first dose of measles-containing vaccine (MCV1) as examples to map delayed vaccination nationally at a resolution of 1 × 1-km2 pixel. We utilized cluster-level childhood vaccination data from The Gambia 2019–20 Demographic and Health Survey. We adopted a fully Bayesian geostatistical model incorporating publicly available geospatial covariates to aid predictive accuracy. The model was implemented using the integrated nested Laplace approximation—stochastic partial differential equation (INLA-SPDE) approach. ResultsWe found significant subnational heterogeneity in delayed HepB0, PENTA3 and MCV1 vaccinations. Specificdistricts in the central and eastern regions of The Gambia consistentlyexhibited the highest prevalence of delayed vaccination, while the coastal districts showed alower prevalence forallthree vaccines. We also found that districts in the eastern, central, as well as in coastal parts of The Gambia had a combination of high estimated prevalence of delayed HepB0, PENTA3 and MCV1 and a significant population of affected infants. ConclusionsOur approach provides decision-makers with a valuable tool to better understand local patterns of untimely childhood vaccination and identify districts where strengthening vaccine delivery systems could have the greatest impact.

Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields

Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha−1) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling.

Spatial predictive risk mapping of lymphatic filariasis residual hotspots in American Samoa using demographic and environmental factors.

American Samoa successfully completed seven rounds of mass drug administration (MDA) for lymphatic filariasis (LF) from 2000-2006. The territory passed the school-based transmission assessment surveys in 2011 and 2015 but failed in 2016. One of the key challenges after the implementation of MDA is the identification of any residual hotspots of transmission. Based on data collected in a 2016 community survey in persons aged ≥8 years, Bayesian geostatistical models were developed for LF antigen (Ag), and Wb123, Bm14, Bm33 antibodies (Abs) to predict spatial variation in infection markers using demographic and environmental factors (including land cover, elevation, rainfall, distance to the coastline and distance to streams). In the Ag model, females had a 26.8% (95% CrI: 11.0-39.8%) lower risk of being Ag-positive than males. There was a 2.4% (95% CrI: 1.8-3.0%) increase in the odds of Ag positivity for every year of age. Also, the odds of Ag-positivity increased by 0.4% (95% CrI: 0.1-0.7%) for each 1% increase in tree cover. The models for Wb123, Bm14 and Bm33 Abs showed similar significant associations as the Ag model for sex, age and tree coverage. After accounting for the effect of covariates, the radii of the clusters were larger for Bm14 and Bm33 Abs compared to Ag and Wb123 Ab. The predictive maps showed that Ab-positivity was more widespread across the territory, while Ag-positivity was more confined to villages in the north-west of the main island. The findings may facilitate more specific targeting of post-MDA surveillance activities by prioritising those areas at higher risk of ongoing transmission.

Bayesian geostatistical modeling for discrete‐valued processes

AbstractWe introduce a flexible and scalable class of Bayesian geostatistical models for discrete data, based on nearest‐neighbor mixture processes (NNMP), referred to as discrete NNMP. To define the joint probability mass function (pmf) over a set of spatial locations, we build from local mixtures of conditional pmfs using a directed graphical model, with a directed acyclic graph that summarizes the nearest neighbor structure. The approach supports direct, flexible modeling for multivariate dependence through specification of general bivariate discrete distributions that define the conditional pmfs. In particular, we develop a modeling and inferential framework for copula‐based NNMPs that can attain flexible dependence structures, motivating the use of bivariate copula families for spatial processes. Moreover, the framework allows for construction of models given a pre‐specified family of marginal distributions that can vary in space, facilitating covariate inclusion. Compared to the traditional class of spatial generalized linear mixed models, where spatial dependence is introduced through a transformation of response means, our process‐based modeling approach provides both computational and inferential advantages. We illustrate the methodology with synthetic data examples and an analysis of North American Breeding Bird Survey data.

Spatiotemporal distributions of under-five mortality in Ethiopia between 2000 and 2019.

Under-five mortality declined in the last two decades in Ethiopia, but sub-national and local progress remains unclear. This study aimed to investigate the spatiotemporal distributions and ecological level factors of under-five mortality in Ethiopia. Data on under-five mortality were obtained from five different Ethiopian Demographic and Health Surveys (EDHS), conducted in 2000, 2005, 2011, 2016, and 2019. Environmental and healthcare access data were obtained from different publicly available sources. Bayesian geostatistical models were used to predict and visualize spatial risks for under-five mortality. The national under-five mortality rate in Ethiopia declined from 121 per 1000 live births in 2000 to 59 per 1000 live births in 2019. Spatial variation in under-five mortality was observed at regional and local levels with the highest rates reported in the Western, Eastern, and Central parts of Ethiopia. Spatial clustering of under-five mortality was significantly associated with population density, access to a water body, and climatic factors such as temperature. Under-five mortality rate declined over the past two decades and varied substantially at sub-national and local levels in Ethiopia. Increasing access to water and health care may help to reduce under-five mortality in high-risk areas. Therefore, interventions targeted to reduce under-five mortality should be strengthened in the areas that had a clustering of under-five mortality in Ethiopia by increasing access to quality health care access.