Effects of health intervention programs and arsenic exposure on child mortality from acute lower respiratory infections in rural Bangladesh

BackgroundRespiratory infections continue to be a public health threat, particularly to young children in developing countries. Understanding the geographic patterns of diseases and the role of potential risk factors can help improve future mitigation efforts. Toward this goal, this paper applies a spatial scan statistic combined with a zero-inflated negative-binomial regression to re-examine the impacts of a community-based treatment program on the geographic patterns of acute lower respiratory infection (ALRI) mortality in an area of rural Bangladesh. Exposure to arsenic-contaminated drinking water is also a serious threat to the health of children in this area, and the variation in exposure to arsenic must be considered when evaluating the health interventions.MethodsALRI mortality data were obtained for children under 2 years old from 1989 to 1996 in the Matlab Health and Demographic Surveillance System. This study period covers the years immediately following the implementation of an ALRI control program. A zero-inflated negative binomial (ZINB) regression model was first used to simultaneously estimate mortality rates and the likelihood of no deaths in groups of related households while controlling for socioeconomic status, potential arsenic exposure, and access to care. Next a spatial scan statistic was used to assess the location and magnitude of clusters of ALRI mortality. The ZINB model was used to adjust the scan statistic for multiple social and environmental risk factors.ResultsThe results of the ZINB models and spatial scan statistic suggest that the ALRI control program was successful in reducing child mortality in the study area. Exposure to arsenic-contaminated drinking water was not associated with increased mortality. Higher socioeconomic status also significantly reduced mortality rates, even among households who were in the treatment program area.ConclusionCommunity-based ALRI interventions can be effective at reducing child mortality, though socioeconomic factors may continue to influence mortality patterns. The combination of spatial and non-spatial methods used in this paper has not been applied previously in the literature, and this study demonstrates the importance of such approaches for evaluating and improving public health intervention program

Child Mortality from Lower Respiratory Infections and the Effect of Arsenic-contaminated Drinking Water in Rural Bangladesh

Children in Bangladesh continue to experience high mortality rates from acute lower respiratory infections (ALRI) despite ongoing treatment programs. Exposure to inorganic arsenic from contaminated drinking water is also a serious health threat in Bangladesh. While lung diseases are commonly associated with inhalation exposures, studies have suggested that exposure to arsenic from drinking water may also negatively affect the lungs. Employing a disease ecology framework, this thesis examines the patterns of mortality from ALRI and the risk factor of arsenic exposure in children under 2 years old between 1989 and 1996 in Matlab, Bangladesh. During this period a community-based treatment program for ALRI was initiated, while arsenic exposure remained high due to widespread well use and the unknown contamination problem. Using a zero-inflated negative binomial regression model, I first examine the association between arsenic exposure from contaminated drinking water and increased mortality rates. Second, I use a spatial scan statistic to test for local clusters of respiratory infection mortality while using the results of the zero-inflated model to adjust the cluster tests. The results suggest that the ALRI treatment program was successful in reducing mortality and that its placement in the region strongly influences the mortality patterns. Arsenic exposure was not significantly associated with ALRI mortality after controlling for the treatment program, socioeconomic status, and access to care. This study focuses on ALRI mortality and cannot rule out a possible association between arsenic and morbidity from respiratory infections. Future work will address this concern as well as the challenges of estimating retrospective exposure to arsenic from drinking water. The continued burden of ALRI makes it important to continue to evaluate intervention programs alongside potential environmental risk factors, such as arsenic exposure, in order to improve child survival

Multi-scale gridded urban morphometrics for settlement classification and population mapping

Urban areas are expanding rapidly around the world, and much of this growth is expected in low- and middle-income countries. Policy makers, researchers, and those implementing development projects need up-to-date and consistent information on cities in order to plan and track progress towards Sustainable Development Goals. Yet in many places experiencing rapid growth, information on urban areas and their population is lacking, outdated or incomplete. In recent years, increasing availability of very high spatial resolution imagery (<1 m resolution) and computing power is enabling sets of building footprint polygons to be automatically extracted from the imagery and mapped for whole countries. These building footprint datasets provide a unique resource to study urban morphometrics in places which may lack other local data. This paper demonstrates the use of a spatial grid to classify urban fabric into settlement types. This unit of analysis is in contrast to plots or parcels which are more commonly used in urban morphology studies, and a case study in Southampton, UK is used to explore the sensitivity of the results to varying the parameters used to define the size of the grid. These initial results suggest that multiple scales of observation windows can be combined to identify key patterns across space and that multiple grid resolutions can give relatively consistent classification results. Future work is needed to explore the use of grids to study urban form in other settings

Bayesian hierarchical modelling approaches for combining information from multiple data sources to produce annual estimates of national immunization coverage

Estimates of national immunization coverage are crucial for guiding policy and decision-making in national immunization programs and setting the global immunization agenda. WHO and UNICEF estimates of national immunization coverage (WUENIC) are produced annually for various vaccine-dose combinations and all WHO Member States using information from multiple data sources and a deterministic computational logic approach. This approach, however, is incapable of characterizing the uncertainties inherent in coverage measurement and estimation. It also provides no statistically principled way of exploiting and accounting for the interdependence in immunization coverage data collected for multiple vaccines, countries and time points. Here, we develop Bayesian hierarchical modeling approaches for producing accurate estimates of national immunization coverage and their associated uncertainties. We propose and explore two candidate models: a balanced data single likelihood (BDSL) model and an irregular data multiple likelihood (IDML) model, both of which differ in their handling of missing data and characterization of the uncertainties associated with the multiple input data sources. We provide a simulation study that demonstrates a high degree of accuracy of the estimates produced by the proposed models, and which also shows that the IDML model is the better model. We apply the methodology to produce coverage estimates for select vaccine-dose combinations for the period 2000-2019. A contributed R package {\tt imcover} implementing the No-U-Turn Sampler (NUTS) in the Stan programming language enhances the utility and reproducibility of the methodology.Comment: 31 pages (main), 4 figure

popRF: Random Forest-informed Disaggregative Population Modelling and Mapping

Here we introduce the popRF package in R that largely addresses these issues. This is done by functionalising the RF-informed dasymetric population modelling procedure (F. R. Stevens et al. 2015) in a single language that is completely free, open source, and environment agnostic. Further, the package has been parallelised where possible to achieve efficient prediction and geoprocessing over large extents, providing functions that have applied utility outside of simply performing disaggregative population modelling. This package was utilised already to predict population and inform the mapping of modelled human settlement (Nieves, Sorichetta, et al. 2020; Nieves, Bondarenko, et al. 2020; Nieves et al. 2021) at 100m resolution across 249 countries from 2000-2020, ingesting over 10TB of covariates (Lloyd et al. 2019) and producing another 70 TB of population and population related dataset

Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources

Oak Ridge National Laboratory (ORNL) initiated an internal National Electric Generation Siting Study, which is an ongoing multiphase study addressing several key questions related to our national electrical energy supply. This effort has led to the development of a tool, OR-SAGE (Oak Ridge Siting Analysis for power Generation Expansion), to support siting evaluations. The objective in developing OR-SAGE was to use industry-accepted approaches and/or develop appropriate criteria for screening sites and employ an array of Geographic Information Systems (GIS) data sources at ORNL to identify candidate areas for a power generation technology application. The initial phase of the study examined nuclear power generation. These early nuclear phase results were shared with staff from the Electric Power Research Institute (EPRI), which formed the genesis and support for an expansion of the work to several other power generation forms, including advanced coal with carbon capture and storage (CCS), solar, and compressed air energy storage (CAES). Wind generation was not included in this scope of work for EPRI. The OR-SAGE tool is essentially a dynamic visualization database. The results shown in this report represent a single static set of results using a specific set of input parameters. In this case, the GIS input parameters were optimized to support an economic study conducted by EPRI. A single set of individual results should not be construed as an ultimate energy solution, since US energy policy is very complex. However, the strength of the OR-SAGE tool is that numerous alternative scenarios can be quickly generated to provide additional insight into electrical generation or other GIS-based applications. The screening process divides the contiguous United States into 100 x 100 m (1-hectare) squares (cells), applying successive power generation-appropriate site selection and evaluation criteria (SSEC) to each cell. There are just under 700 million cells representing the contiguous United States. If a cell meets the requirements of each criterion, the cell is deemed a candidate area for siting a specific power generation form relative to a reference plant for that power type. Some SSEC parameters preclude siting a power plant because of an environmental, regulatory, or land-use constraint. Other SSEC assist in identifying less favorable areas, such as proximity to hazardous operations. All of the selected SSEC tend to recommend against sites. The focus of the ORNL electrical generation source siting study is on identifying candidate areas from which potential sites might be selected, stopping short of performing any detailed site evaluations or comparisons. This approach is designed to quickly screen for and characterize candidate areas. Critical assumptions supporting this work include the supply of cooling water to thermoelectric power generation; a methodology to provide an adequate siting footprint for typical power plant applications; a methodology to estimate thermoelectric plant capacity while accounting for available cooling water; and a methodology to account for future ({approx}2035) siting limitations as population increases and demands on freshwater sources change. OR-SAGE algorithms were built to account for these critical assumptions. Stream flow is the primary thermoelectric plant cooling source evaluated in this study. All cooling was assumed to be provided by a closed-cycle cooling (CCC) system requiring makeup water to account for evaporation and blowdown. Limited evaluations of shoreline cooling and the use of municipal processed water (gray) cooling were performed. Using a representative set of SSEC as input to the OR-SAGE tool and employing the accompanying critical assumptions, independent results for the various power generation sources studied were calculated

Dataset for Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot

This dataset supports the publication: Warren C. Jochem and Andrew J. Tatem. &quot;Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot.&quot; PLOS ONE.</span

Explaining the immigrant health advantage: self-selection and protection in health-related factors among five major national-origin immigrant groups in the United States

Despite being newcomers, immigrants often exhibit better health relative to native-born populations in industrialized societies. We extend prior efforts to identify whether self-selection and/or protection explain this advantage. We examine migrant height and smoking levels just prior to immigration to test for self-selection; and we analyze smoking behavior since immigration, controlling for self-selection, to assess protection. We study individuals aged 20�49 from five major national origins: India, China, the Philippines, Mexico, and the Dominican Republic. To assess self-selection, we compare migrants, interviewed in the National Health and Interview Surveys (NHIS), with nonmigrant peers in sending nations, interviewed in the World Health Surveys. To test for protection, we contrast migrants� changes in smoking since immigration with two counterfactuals: (1) rates that immigrants would have exhibited had they adopted the behavior of U.S.-born non-Hispanic whites in the NHIS (full �assimilation�); and (2) rates that migrants would have had if they had adopted the rates of nonmigrants in sending countries (no-migration scenario). We find statistically significant and substantial self-selection, particularly among men from both higher-skilled (Indians and Filipinos in height, Chinese in smoking) and lower-skilled (Mexican) undocumented pools. We also find significant and substantial protection in smoking among immigrant groups with stronger relative social capital (Mexicans and Dominicans)

Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot.

Spatial datasets of building footprint polygons are becoming more widely available and accessible for many areas in the world. These datasets are important inputs for a range of different analyses, such as understanding the development of cities, identifying areas at risk of disasters, and mapping the distribution of populations. The growth of high spatial resolution imagery and computing power is enabling automated procedures to extract and map building footprints for whole countries. These advances are enabling coverage of building footprint datasets for low and middle income countries which might lack other data on urban land uses. While spatially detailed, many building footprints lack information on structure type, local zoning, or land use, limiting their application. However, morphology metrics can be used to describe characteristics of size, shape, spacing, orientation and patterns of the structures and extract additional information which can be correlated with different structure and settlement types or neighbourhoods. We introduce the foot package, a new set of open-source tools in a flexible R package for calculating morphology metrics for building footprints and summarising them in different spatial scales and spatial representations. In particular our tools can create gridded (or raster) representations of morphology summary metrics which have not been widely supported previously. We demonstrate the tools by creating gridded morphology metrics from all building footprints in England, Scotland and Wales, and then use those layers in an unsupervised cluster analysis to derive a pattern-based settlement typology. We compare our mapped settlement types with two existing settlement classifications. The results suggest that building patterns can help distinguish different urban and rural types. However, intra-urban differences were not well-predicted by building morphology alone. More broadly, though, this case study demonstrates the potential of mapping settlement patterns in the absence of a housing census or other urban planning data