Assessing Hospital Readmission Risk Factors in Heart Failure Patients Enrolled in a Telemonitoring Program

The purpose of this study was to validate a previously developed heart failure readmission predictive algorithm based on psychosocial factors, develop a new model based on patient-reported symptoms from a telemonitoring program, and assess the impact of weight fluctuations and other factors on hospital readmission. Clinical, demographic, and telemonitoring data was collected from 100 patients enrolled in the Partners Connected Cardiac Care Program between July 2008 and November 2011. 38% of study participants were readmitted to the hospital within 30 days. Ten different heart-failure-related symptoms were reported 17,389 times, with the top three contributing approximately 50% of the volume. The psychosocial readmission model yielded an AUC of 0.67, along with sensitivity 0.87, specificity 0.32, positive predictive value 0.44, and negative predictive value 0.8 at a cutoff value of 0.30. In summary, hospital readmission models based on psychosocial characteristics, standardized changes in weight, or patient-reported symptoms can be developed and validated in heart failure patients participating in an institutional telemonitoring program. However, more robust models will need to be developed that use a comprehensive set of factors in order to have a significant impact on population health

Spatiotemporal modelling of flood-related impacts on daily population movement

This paper presents research combining spatiotemporal population flow data, flood modelling and network analysis to examine the effect of time of flood onset and flood magnitude on travel across a city for commuters and primary school children. Findings quantify that flood onset time has an effect on the disruption to travel comparable to flood event magnitud

A simulated ‘sandbox’ for exploring the modifiable areal unit problem in aggregation and disaggregation

We present a spatial testbed of simulated boundary data based on a set of very high-resolution census-based areal units surrounding Guadalajara, Mexico. From these input areal units, we simulated 10 levels of spatial resolutions, ranging from levels with 5,515–52,388 units and 100 simulated zonal configurations for each level – totalling 1,000 simulated sets of areal units. These data facilitate interrogating various realizations of the data and the effects of the spatial coarseness and zonal configurations, the Modifiable Areal Unit Problem (MAUP), on applications such as model training, model prediction, disaggregation, and aggregation processes. Further, these data can facilitate the production of spatially explicit, non-parametric estimates of confidence intervals via bootstrapping. We provide a pre-processed version of these 1,000 simulated sets of areal units, meta- and summary data to assist in their use, and a code notebook with the means to alter and/or reproduce these data

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

Place-level urban-rural indices for the United States from 1930 to 2018

Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant problems when the process of interest is highly localized, as with the incorporation of rural towns and villages into encroaching metropolitan areas. Moreover, existing rural-urban classifications are often inconsistent over time, or require complex, multi-source input data (e.g., remote sensing observations or road network data), thus, prohibiting the longitudinal analysis of rural-urban dynamics. Here, we develop a set of distance- and spatial-network-based methods for consistently estimating the remoteness and rurality of places at fine spatial resolution, over long periods of time. We demonstrate the utility of our approach by constructing indices of urbanness for 30,000 places in the United States from 1930 to 2018 and further test the plausibility of our results against a variety of evaluation datasets. We call these indices the place-level urban-rural index (PLURAL) and make the resulting datasets publicly available (https://doi.org/10.3886/E162941) so that other researchers can conduct long-term, fine-grained analyses of urban and rural change. In addition, due to the simplistic nature of the input data, these methods can be generalized to other time periods or regions of the world, particularly to data-scarce environments.</jats:p

Examining the correlates and drivers of human population distributions across low-and middle-income countries

Geographical factors have influenced the distributions and densities of global human population distributions for centuries. Climatic regimes have made some regions more habitable than others, harsh topography has discouraged human settlement, and transport links have encouraged population growth. A better understanding of these types of relationships enables both improved mapping of population distributions today and modelling of future scenarios. However, few comprehensive studies of the relationships between population spatial distributions and the range of drivers and correlates that exist have been undertaken at all, much less at high spatial resolutions, and particularly across the low-and middle-income countries. Here, we quantify the relative importance of multiple types of drivers and covariates in explaining observed population densities across 32 low-and middle-income countries over four continents using machine-learning approaches. We find that, while relationships between population densities and geographical factors show some variation between regions, theyare generally remarkably consistent,pointing to universal drivers of human population distribution. Here,we find that a set of geographical features relating to the built environment, ecology and topography consistently explain the majority of variability in population distributions at fine spatial scales across the low-and middle-income regions of the world.</p

Global population distributions and the environment: discerning observed global and regional patterns

Between 1990 to 2015, numerous groups used ancillary data about the environment surrounding populations to more accurately map global populations from standard census data. No comprehensive study has been under taken to characterize the observed relationships between population density and ancillary data. Better understanding these relationships may produce more accurate population maps, focus resources on new datasets with a high probability of modelling importance, and lead to expanded end-user applications. This study examined these relationships by extracting variable importances from 36 independently run, country-specific population models from the WorldPop project’s population data. Covariate data describing urban/suburban extents were found to be the most significant predictors of population. Little difference was found in the resolution of urban/suburban data regarding their modelling importance. Further examination of the effect of different definitions of built-/urban-area, methods of quantifying input data quality, and the probability of specific variable classes as significant predictors of population is require

Modelling global human settlement to better inform annual population modelling

Since 1950, the world’s population has shifted from being largely rural to majority urbanised. This trend of increasing urbanisation of population and increasing land use transitions promoting the growth of settlements and the built-environment, are expected to continue in future decades, particularly in low- and middle-income countries. These trends are accompanied by rapidly shifting subnational demographics and spatial distributions of populations, even within urbanised areas. Accurate and timely data is required to develop adaptive strategies for these shifting trends and minimising potential negative impacts. While multi-temporal, high-resolution datasets of built-settlement extent have become globally available, there remain gaps in their coverage and globally consistent methods of predicting future built-settlement expansion at regular intervals have not kept pace with these new data.This thesis develops and validates a country-specific yet globally applicable means of annually interpolating built-settlement extents and projecting built- settlement extents into the near future using relative changes in subnational population and lights at night radiance. Additionally, I demonstrate the utility of this modelling framework within a global population modelling context across a period of 13 years. This thesis improves upon previous urban growth modelling approaches by demonstrating that relative changes in population can be sufficient, in and of themselves and as causal proxies for changes in economics, for accurately predicting areas undergoing built-settlement expansion across time and space. Additionally, this thesis validates its predictions at the pixel level, something not done by previous global urban and settlement modelling approaches. By addressing the limits that exist within current global urban modelling approaches, such as large or specific data requirements and subjective assumptions of growth factors/parameters, the modelling frameworks presented in this thesis allows for more consistent, frequent, and accurate built-settlement predictions. By extension, these accurate, time-specific built-settlement predictions allow for better, time-specific population mapping across the globe. Improved knowing of where and when built-settlement appeared allows for further investigations into arable land use consumption in relation to population dynamics, temporally fine-scale changes in population distributions across space in relation to climate change stresses, built-settlement expansion and greenhouse gas emissions, and trends in built-settlement expansion in relation to sea level rise, to name a few