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475 results for “income”
A consistent dataset for net income deciles for 190 countries, aggregated to 32 geographical regions from 1958-2015
<p>This is a data record which corresponds to the paper "A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015" (Narayan et al. 2024, ESSD)</p> <p>The final paper is available here-https://essd.copernicus.org/articles/16/2333/2024/</p> <p>Description/Abstract-Data on income distributions within and across countries are becoming increasingly important for informing analysis of income inequality and understanding the distributional consequences of climate change. While datasets on income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same concept of inequality (or income concept) and therefore make comparisons across countries, over time and across datasets difficult. Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the Gini coefficient. For the imputation, we use a recently developed nonparametric principal-component-based approach that shows an excellent fit to data on income distributions compared to other approaches. We also present another version of this dataset aggregated from the country level to 32 geographical regions. Our dataset is developed for the purpose of calibrating models such as integrated human–Earth system models with detailed data on income distributions. This dataset will enable more robust analysis of income distribution at multiple scales. </p> <p>Citation for paper- Narayan, K. B., O'Neill, B. C., Waldhoff, S., and Tebaldi, C.: A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015, Earth Syst. Sci. Data, 16, 2333–2349, https://doi.org/10.5194/essd-16-2333-2024, 2024.</p> <p> </p>
Resources for BMF CP 84: Gender, age, income, immersiveness, and space tourism intention
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>How are gender, age, and income associated with the intention to try space tourism?</span></li> <li><span>How are gender, age, and income associated with the intention to try space tourism when conditional on the level of immersiveness in information on social media?</span></li> </ul>
Supporting information for: The Time Requirements for Primary Care Consultations: Initial Sick Child Visits in Low- and Middle-income Countries Using the Integrated Management of Childhood Illness (IMCI) Clinical Algorithm
<p>Few studies have examined the time required for primary care consultations; none have focused on sick child visits in low- and middle-income countries (LMICs). This project begins to fill that gap by providing evidence-based estimates of the time needed for initial visits with under-five infants and children at public or not-for-profit facilities in countries using the Integrated Management of Childhood Illness (IMCI) clinical algorithm.</p> <p>Estimates of the mean expected duration of IMCI consultations require (a) classification profiles, i.e., tabulations of the gold standard health issues presented by patients less than 5 years old; (b) lists of the tasks included in applicable versions of the IMCI algorithm and the conditions that elicit them, and (c) an estimate of the time needed to perform tasks with no pre-defined minimum duration. The latter requires, in addition to classification profiles, information on rates of task performance and the mean observed duration of consultations.</p> <p>The IMCI clinical algorithm and the research surrounding it provide unusually rich sources of such information. Developed in the mid 1990s by the World Health Organization and the United Nations Children’s Fund, the IMCI algorithm seeks to reduce child mortality in LMICs by improving the technical quality of primary care services. For infants less than 2 months old, the algorithm focuses on bacterial infections, feeding problems, low weight, and, in some versions, jaundice. For children 2-59 months old, the foci include acute respiratory infections, especially pneumonia; diarrhea; fevers, especially malaria and measles; malnutrition, and anemia. Immunization status is a concern for both age groups. The algorithm provides a scheme to classify the health issues with which infants and children present, an array of tasks providers may be expected perform, and criteria by which tasks are elicited. Research on the design and utility of the algorithm, its effects on provider performance, and related topics furnishes data on the prevalence of gold standard IMCI classifications in a variety of patient populations. In some cases, it also enables one to calculate the time required to perform tasks.</p> <p>I found such information by searching MEDLINE, the database of the International Network for Rational Use of Medicines, the websites of the WHO and its regional offices, GOOGLE, and GOOGLE SCHOLAR using search terms such as ‘Integrated Management of Childhood Illness’, ‘observational’, ‘prospective’, ‘classification’, ‘clinical signs’, ‘health facility survey’, and ‘validity’. I also reviewed studies that cited a qualified study and, conversely, material included in the bibliographies of qualified studies.</p> <p>The supplemental information files contain the following:</p> <p>WORKBOOK S1_STUDIES USED</p> <p>Lists features of, and sources for, the studies used to construct classification profiles and to estimate the time required to perform the average task with no predefined minimum duration. With 2 exceptions (see below, DATA S1 and DATA S2), all the studies have been published or are readily available on the internet. None of the data can be used to identify individuals.</p> <p>DATA S1_REPORT OF THE HEALTH FACILITY SURVEY IN BOTSWANA, 2007-08 and DATA S2_REPORT OF THE HEALTH FACILITY SURVEY IN TANZANIA, 2003</p> <p>PDF files of Health Facility Survey reports that were found on the internet but have since been taken down.</p> <p>DATA S3_BURKINA FASO CHART BOOKLET, 2015</p> <p>PDF provided <span>Drs. Sophie Sarrassat (London School of Hygiene and Tropical Medicine) and Serge M. A. Somda (Université Nazi BONI).</span></p> <p>WORKBOOK S2_CLASSIFICATION PROFILES: INFANTS; WORKBOOK S3_CLASSIFICATION PROFILES: CHILDREN IN UPPER MIDDLE-INCOME COUNTRIES; WORKBOOK S4_CLASSIFICATION PROFILES: CHILDREN IN LOWER MIDDLE-INCOME COUNTRIES (I); WORKBOOK S5_CLASSIFICATION PROFILES: CHILDREN IN LOWER MIDDLE-INCOME COUNTRIES (II), and WORKBOOK S6_CLASSIFICATION PROFILES: CHILDREN IN LOW INCOME COUNTRIES </p> <p>The design of the worksheets in these workbooks is described in TEXT S1_NOTES OF THE CONSTRUCTION OF CLASSIFICATION PROFILES (see below).</p> <p>WORKBOOK S7_IMCI CLINICAL TASKS</p> <p>Lists the clinical tasks provided by relevant IMCI algorithms for the care of infants and children. Consists of 6 worksheets covering mandatory tasks, conditional assessments, and treatment and counseling tasks for infants and children.</p> <p>WORKBOOK S8_MINUTES PER TASK WITH NO MINIMUM DURATION</p> <p>Provides estimate of the mean time required to perform a task with no minimum duration for each of 7 populations for which the required data are available, corrected, where necessary, for the effect of an observer on the rate and pace of task performance. Also provides a geometric mean for all 7 populations.</p> <p>TEXT S1_NOTES ON METHODOLOGY</p> <p>WORD document describing the steps involved in estimating the expected durations of consultations.</p> <p>TEXT S2_NOTES OF THE CONSTRUCTION OF CLASSIFICATION PROFILES</p> <p>WORD document describing the steps involved in constructing each profile, problems encountered, and how they were solved.</p> <p>TEXT S3_NOTES ON THE IDENTIFICATION OF IMCI CLINICAL TASKS</p> <p>WORD document describing the standards used in identifying clinical tasks in IMCI algorithms.</p> <p>TEXT S4_NOTES ON THE ESTIMATION OF MINUTES PER TASK WITH NO MINIMUM DURATION</p> <p>WORD document describing the steps involved in estimating the mean time required to perform a task with no predefined minimum duration, problems encountered, and how they were solved.</p>
Primary-level worker interventions for the care of mental disorders and distress in low- and middle-income countries - GRADE evidence profiles
<p>This file includes all GRADE evidence profiles for the following Cochrane review:</p> <p>van Ginneken N, Chin WY, Lim YC, Ussif A, Singh R, Shahmalak U, Purgato M, Rojas-García A, Uphoff E, McMullen S, Foss HS, Thapa Pachya A, Rashidian L, Borghesani A, Henschke N, Chong L-Y, Lewin S. Primary-level worker interventions for the care of mental disorders and distress in low- and middle-income countries. Cochrane Database of Systematic Reviews. In press</p>
Investigating the cultural and contextual determinants of antimicrobial stewardship programmes across low-, middle- and high-income countries – a qualitative study
<p>An animation summarising the key findings from a qualitative study conducted as part of a PhD study across England, France, Norway, India and Burkina Faso investigating the influence of culture and team dynamics on the implementation of antimicrobial stewardship interventions. Fifty-four healthcare professionals across 24 hospitals, all of whom had a role in the implementation of antimicrobial stewardship were interviewed to investigate the challenges to implementing interventions across different healthcare settings. </p>
Scaling up gas and electric cooking in low- and middle-income countries: Climate threat or mitigation strategy with co-benefits?
<p>Nearly three billion people in low- and middle-income countries (LMICs) rely on polluting fuels, resulting in millions of avoidable deaths annually. Polluting fuels also emit short-lived climate forcers and greenhouse gases (GHGs). Liquefied petroleum gas (LPG) and grid-based electricity are scalable alternatives to polluting fuels but have raised climate and health concerns. Here, we compare emissions and climate impacts of a business-as-usual household cooking fuel trajectory to four large-scale transitions to gas and/or grid electricity in 77 LMICs. We account for upstream and end-use emissions from gas and electric cooking, assuming electrical grids evolve according to the 2022 World Energy Outlook's "Stated Policies" Scenario. We input the emissions into a reduced-complexity climate model to estimate radiative forcing and temperature changes associated with each scenario. We find full transitions to LPG and/or electricity decrease emissions from both well-mixed GHG and short-lived climate forcers, resulting in a roughly 5 millikelvin global temperature reduction by 2040. Transitions to LPG and/or electricity also reduce annual emissions of PM2.5 by over 6 Mt (99%) by 2040, which would substantially lower health risks from Household Air Pollution.</p>
Datasheets and R-codes for flood mortality and income inequality project
<p>This zipped folder contains the compiled datasheets, raw figures, tables and R-codes for the analysis conducted in 2023 by Sara Lindersson, as reported in the following preprint:</p> <p>Sara Lindersson, Elena Raffetti, Maria Rusca, Luigia Brandimarte, Johanna Mård and Giuliano Di Baldassarre. <em>The wider the gap between rich and poor, the higher the flood mortality.</em> 2023. PREPRINT (revised manuscript)</p> <ul> <li>Dataset v. 1.0 corresponds to the first version of the manuscript, submitted in 2022.</li> <li>Dataset v. 2.0 corresponds to the revised manuscript, submitted in 2023.</li> </ul>
Congenital diaphragmatic hernia in a middle-income country: persistent high lethality during a 12-year period.
<p>Database used for analysis of the manuscript entitled: "Congenital diaphragmatic hernia in a middle-income country: persistent high lethality during a 12-year period". The aim of the study was to analyze, in São Paulo state of Brazil, the temporal trends of incidence, neonatal mortality and lethality of congenital diaphragmatic hernia (CDH) and its subgroups (isolated CDH, CDH associated to chromosomal anomaly and CDH associated to non-chromosomal anomalies) and identify the time to congenital diaphragmatic hernia-associated neonatal death. </p>
Supporting information for "Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network"
<p>This dataset includes 2 files that represent supporting information for McJannet, D and Desilets, D (Submitted 2023)"Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network" Water Resources Research.</p> <p>File 1 - Supporting Information 1 - Example calculation: Excel sheet showing demonstration calaculations using the neutron intensity correction described in the paper</p> <p>File 2 - Supporting information 2 - List of neutron moniotr stations used in the paper and acknowledgment of their contribtuion</p>
Calcium fortification of water during pregnancy for the prevention of preeclampsia in a low-income setting: a cost-effectiveness analysis
<p>This database contains the model and the parameters of the study. It's is important to highlight that this is the first version of the model, and thus modifications are forthcoming. With the current database, the abstract is the following:</p> <p>Fortification of water with calcium during pregnancy is proposed as a suitable and promising intervention to increase calcium intake and prevent preeclampsia and eclampsia (PE/E). However, the evidence of the cost-effectiveness of this type of intervention is scarce. We conducted a model-based cost-effectiveness analysis to estimate the incremental cost-effectiveness ratio (ICER) of calcium pills and bottled water fortified with calcium, compared to the standard of care (defined as the use of MgSO4 as the standard treatment for PE/E), in the context of Nepal. Outcome measures were years of life gained (YLG) by mothers and newborns and healthcare costs. We considered a lifetime time horizon and health benefits were discounted at 3%. The cost-effectiveness threshold was set at the 2019 Nepal gross domestic product per capita (USD 1071). For calcium pills versus standard of care, ICER was USD 63 per YLG. By switching from calcium pills to bottled water fortified with calcium, the resulting ICER was USD 1702 per YLG. In conclusion, the bottle of water fortified with calcium was found not cost-effective at the defined threshold for Nepal. Complementary economic evidence adapted to other low-and middle income contexts is required to guide novel preventive interventions to improve maternal and child health based on the efficiency principle</p>
Projecting Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA
<p>Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL).</p> <p>GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).</p> <p>GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a></p> <p>The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP <em>per capita</em>) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.</p> <p>Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including:</p> <ul> <li>income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)</li> <li>residential energy consumption <em>per capita</em> by service and fuel, by state and income group, 2015-2045</li> <li>residential energy service output (energy consumption * technology efficiency) <em>per capita</em> by service, fuel, and technology, by state and income group, 2015-2045</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2045</li> <li>residential heating service inequality (Eq.2), by state, 2015-2045</li> </ul> <p> </p> <p><strong>Table 1</strong></p> <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZnoCCS (Net-Zero by 2050 without CCS)</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZnoCCS_climate</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table> <p> </p> <p><strong>Eq. 1</strong></p> <p><span class="math-tex">\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)</span></p> <p>for income group<em> i</em> and service <em>j</em></p> <p> </p> <p><strong>Eq. 2</strong></p> <p><strong><span class="math-tex">\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)</span></strong></p> <p>where <em>S</em> is the residential heating service output <em>per capita</em> of the highest income group (<em>d10</em>) divided by the sum of that of the lowest four income groups (<em>d1</em>, <em>d2</em>, <em>d3</em>, and <em>d4</em>), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.</p> <p> </p> <p><strong>Reference</strong></p> <p>Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7227128">https://doi.org/10.5281/zenodo.7227128</a></p> <p>IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].</p> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p>
An Assessment of Medication Errors among Pediatric Patients in Three Hospitals in Freetown Sierra Leone: Findings and Implications for a Low-Income Country
<p> </p><p><strong>Background</strong></p><p>Pediatric patients are prone to medicine-related problems like medication errors (MEs), which can potentially cause harm. Yet, this has not been studied in this population in Sierra Leone. Therefore, this study investigated the prevalence and nature of MEs, including potential drug-drug interactions (pDDIs) in pediatric patients in three hospitals in Freetown, Sierra Leone.</p><p><strong>Methods: </strong>The study was conducted in three Hospitals among pediatric patients in Freetown and consisted of two phases. Phase one was a cross-sectional retrospective review of prescriptions for completeness and accuracy against standard prescription writing guidelines. Phase two was a point prevalence inpatient chart review of MEs that were categorized into prescription, administration, and dispensing errors and pDDIs. Data was analyzed using frequency, percentages, median, and interquartile range. Kruskal-Wallis H and Mann-Whitney U tests were used to compare the prescription accuracy between the hospitals, with p<0.05 considered statistically significant.</p><p><strong>Results: </strong>In phase one<strong>, </strong>while no prescription attained the global accuracy score (GAS) gold standard of 100%, 106 (29.0%) achieved the 80-100% mark. The patient 63 (17.2%), treatment 228 (62.3%), and prescriber 33 (9.0%) identifiers achieved an overall GAS range of 80-100%. Although the total GAS was not statistically significant (p=0.065), the date (p=0.041), patient (p=<0.001), treatment (p=0.022), and prescriber (p=<0.001) identifiers were statistically significant across the different hospitals. For phase two, the prevalence of MEs was 74 (56.1%), while that for pDDIs was 54 (40.9%). There was a statistically positive correlation between the occurrence of pDDI and the number of medicines prescribed (r=0.211, P=0.015).</p><p><strong>Conclusion: </strong>Low GAS depicts poor compliance with prescription writing guidelines and high prescribing errors. Medication errors were observed at each phase of the medication use cycle, while clinically significant pDDIs were also reported. Thus, there is a need for training on prescription writing guidelines and medication errors.</p><p> </p><p><strong>Keywords:</strong> Pediatrics, Prescription, Medication errors, Drug-drug interactions, Sierra Leone</p>
Cognitive development among children in a low-income setting: cost-effectiveness analysis of a maternal nutrition education intervention in rural Uganda
<p><span>Inadequate nutrition and insufficient stimulation in early childhood can lead to long-term deficits in cognitive and social development. Evidence for policy and decision-making regarding the cost of delivering nutrition education is lacking in low and middle-income countries (LMIC). In rural Uganda, we conducted a cluster-randomized controlled trial (RCT) examining the effect of a maternal nutrition education intervention on developmental outcomes among children aged 6–8 months. This intervention led to significantly improved cognitive scores when the children reached the age of 20–24 months. When considering the potential for this intervention's future implementation, the desired effects should be weighed against the increased costs.</span><span> This study therefore aimed to assess the cost-effectiveness of this education intervention compared with current practice.</span><span> Health outcome data were based on the RCT. Cost data were collected via interviews with researchers involved in processing the intervention. This study considered a healthcare provider perspective for an 18-month time horizon.</span><span> The control group was considered as the current practice for the future large-scale implementation of this intervention. </span><span>A cost-effectiveness analysis was performed, including calculations of incremental cost-effectiveness ratios (ICERs). In addition, uncertainty in the results was characterized using one-way and probabilistic sensitivity analyses. The ICER for the education intervention compared with current practice was USD ($) 16.50 per cognitive composite score gained, with an incremental cost of </span><span>$265.79 and an incremental cognitive composite score of 16.11. The sensitivity analyses indicated the robustness of these results. The ICER was sensitive to changes in cognitive composite score and the cost of personnel, but the education intervention can be considered cost-effective compared with the current practice. The outcome of this study, including the cost analysis, health outcome, cost-effectiveness, and sensitivity analysis, can be useful to inform policymakers and stakeholders about effective resource allocation processes in Uganda and possibly other LMIC.</span></p>
Historical racial redlining and contemporary patterns of income inequality negatively affect birds, their habitat, and people in Los Angeles, California
<p>The Home Owners' Loan Corporation (HOLC) was a U.S. government-sponsored program initiated in the 1930s to evaluate mortgage lending risk. The program resulted in hand-drawn 'security risk' maps intended to grade sections of cities where investment should be focused (greenlined areas) or limited (redlined zones). The security maps have since been widely criticized as being inherently racist and have been associated with high levels of segregation and lower levels of green amenities in cities across the country. Our goal was to explore the potential legacy effects of the HOLC grading practice on birds, their habitat, and the people who may experience them throughout a metropolis where the security risk maps were widely applied, Greater Los Angeles, California (L.A.). We used ground-collected, remotely sensed, and census data and descriptive and predictive modeling approaches to address our goal. Patterns of bird habitat and avian communities strongly aligned with the luxury-effect phenomenon, where green amenities were more robust, and bird communities were more diverse and abundant in the wealthiest parts of L.A. Our analysis also revealed potential legacy effects from the HOLC grading practice. Associations between bird habitat features and avian communities in redlined and greenlined zones were generally stronger than in areas of L.A. that did not experience the HOLC grading, in part because redlined zones, which included some of the poorest locations of L.A., had the highest levels of dense urban conditions, e.g., impervious surface cover. In contrast, greenlined zones, which included some of the city's wealthiest areas, had the highest levels of green amenities, e.g., tree canopy cover. The White population of L.A., which constitutes the highest percentage of a racial or ethnic group in greenlined areas, was aligned with a considerably greater abundance of birds affiliated with natural habitat features (e.g., trees and shrubs). Conversely, the Hispanic or Latino population, which is dominant in redlined zones, was positively related to a significantly greater abundance of synanthropic birds, which are species associated with dense urban conditions. Our results suggest that historical redlining and contemporary patterns of income inequality are associated with distinct avifaunal communities and their habitat, which potentially influence the human experience of these components of biodiversity throughout L.A. Redlined zones and low-income residential areas that were not graded by the HOLC can particularly benefit from deliberate urban greening and habitat enhancement projects, which would likely carry over to benefit birds and humans.</p>
Updated Projections of Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA
<p>Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL). Compared to the first version of the dataset (<a href="https://zenodo.org/record/79880387">https://zenodo.org/record/79880387</a>), this updated dataset is based on model runs where the Inflation Reduction Act (IRA) are implemented in the model scenarios. In addition to the queried and post-processed key output variables related to residential energy sector in .csv tables, we also upload the full model output databases in this repository, so that users can query their desired model outputs.</p> <p>GCAM-USA operates within the Global Change Analysis Model (GCAM), which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).</p> <p>GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a>.</p> <p>The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of September 24, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP <em>per capita</em>) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.</p> <p>Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2050 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the full model output databases and key output variables related to the residential energy sector under the four scenarios, including:</p> <ul> <li>income shares by consumer groups at each state over 2015-2050 (Casper et al., 2023)</li> <li>residential energy consumption <em>per capita</em> by service, fuel, state, and income group, 2015-2050</li> <li>residential energy service output (energy consumption * technology efficiency) <em>per capita </em>by service, fuel, state, and income group, 2015-2050</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2050</li> <li>estimated satiation gap (Eq.2), by service, state, and income group, 2015-2050</li> <li>residential heating service inequality (Eq.3), by state, 2015-2050</li> </ul> <p> </p> <p><strong>Table 1</strong></p> <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies (including IRA)</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies (including IRA)</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZ (Net-Zero by 2050)</td> <td> <p>In addition to BAU, two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZ_climate</td> <td> <p>In addition to BAU, two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table> <p> </p> <p><strong>Eq. 1</strong></p> <p><span class="math-tex">\(Energy\ burden_{i,k} = \dfrac{\sum_j (service\ output_{i,j,k} * service\ cost_{j,k})}{GDP_{i,k}}\)</span></p> <p>for income group <em>i </em>and state <em>k</em>, that sums over all residential energy services <em>j</em>.</p> <p> </p> <p><strong>Eq. 2</strong></p> <p><span class="math-tex">\(Satiation\ Gap_{i,j,k} = \dfrac{satiation\ level_{j,k} - service\ output_{i,j,k}} {satiation\ level_{j,k}}\)</span></p> <p>for service <em>j</em>, income group <em>i</em>, and state <em>k</em>. Note that the satiation level and service output are per unit of floorspace.</p> <p> </p> <p><strong>Eq. 3</strong></p> <p><strong><span class="math-tex">\(Residential\ heating\ service\ inequality_j = \dfrac{S_j^{d10}}{(S_j^{d1} +S_j^{d2} + S_j^{d3} + S_j^{d4})}\)</span></strong></p> <p>for service <em>j </em>where <em>S</em> is the residential heating service output <em>per capita</em> of the highest income group (<em>d10</em>) divided by the sum of that of the lowest four income groups (<em>d1</em>, <em>d2</em>, <em>d3</em>, and <em>d4</em>), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality. Among the key output variables in this repository, we provide the residential <em>heating</em> service inequality output table as an example.</p> <p> </p> <p><strong>Reference</strong></p> <p>Casper, K. C., Narayan, K. B., O'Neill, B. C., Waldhoff, S. T., Zhang, Y., & Wejnert-Depue, C. (2023). Non-parametric projections of the net-income distribution for all U.S. states for the shared socioeconomic pathways. <em>Environmental Research Letters</em>. http://iopscience.iop.org/article/10.1088/1748-9326/acf9b8.</p> <p>IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Prospective cohort study of a community-based primary care program's effects on pharmacotherapy quality in low-income Peruvians with type 2 diabetes and hypertension
<p>A door-to-door survey was conducted to enumerate all household members by age and sex in a low-income community in Peru. 856 adults 35 years and older were eligible to participate in screening for type 2 diabetes and hypertension. 709 (83%) participated in screening. 130 (18.3%) were diagnosed with hypertension and/or type 2 diabetes of which 109 (84%) participated at program onset and 22 were added later from earlier non-participants in screening or program onset to form the cohort of 131 patients with diabetes and/or hypertension. The primary care program had components of the Chronic Care Model, community health workers, and freely accessible visits and medications. The program operated between September 2011 and May 2014, and consisted of two care periods (separated by a six-month hiatus), first a 10-month home-care period, then a 17-month clinic-care period. The dataset is two files corresponding to two exposures: the 27-month program overall (post- versus pre-) (N=262 observations, 131 pairs with patients as self-controls) and care period (clinic versus home), N=211 (109 home and 102 clinic observations, >131 because 80 patients participated in both care periods). Exposures were evaluated for their effects on guidelines-based pharmacotherapy standards: hypoglycemic and antihypertensive medications, low-dose aspirin, and first-line angiotensin converting enzyme inhibitor (ACEi) treatment of diabetes with elevated blood pressure.</p>
Academy Sports and Outdoors, Inc - Income Statement
<p>Academy Sports and Outdoors, Inc. (Ticker: ASO) is a mid-cap Texas-based sporting goods and outdoor recreational retailer trading at a P/E of 7x, and an EV/EBITDA yield of 17%, which places it among the cheapest 10% of stocks in our liquid, tradeable universe of stocks (mkt cap > ~$2 bn).</p><p>ASO employs approximately 22,000 people, and operates 269 retail locations in 18 states across the southeastern US, as well as three distribution centers located in Texas, Tennessee, and Georgia.</p><p>ASO was written up in 2021 by baileyb906, and we encourage VIC members to review that writeup for additional background.</p><p>Q1 Weakness and Retail Crime Fallout</p><p>There are some good recent reasons to be pessimistic about the stock. First, fiscal Q1, ending 4/29/23, showed a negative trend -- a YoY decline in quarterly revenues of 5.7%. The company has also indicated Q2 would also be challenging. With earnings coming out next week, we will soon see. Most of the Q1 softness was due to a 15% YoY decline in revenues in its Outdoor division, traditionally the company's largest. The company has explained that 1) it had very tough comps versus the prior year in hunting, camping, fitness and bikes, and 2) the company's products are designed to be enjoyed outside, and much of the weakness was due to unfavorable weather patterns, including cooler temperatures and rain. Second, Dick's missed earnings pretty dramatically few days ago due to inventory shrink due to a rise in retail crime, and sold off ~20%. ASO has been caught up in concerns about how this might affect the sector. But this current weakness comes at the tail end of a successful multi-year turnaround story, and is a reasonable entry point.</p><p>Turnaround Background</p><p>By way of background, KKR bought 20% the company in 2011, and the company did poorly from 2013-2018 during which time, despite aggressive store count growth, EBITDA/Store fell from ~$2.5 million to ~$1 million. Enter Ken Hicks, who was appointed CEO in 2018. Hicks had previously run a successful turnaround at Foot Locker, increasing Sales, and EBIT and net income margins while there.</p><p>At Academy Sports, Hicks pursued a successful store expansion plan, oversaw its IPO in 2020, and grew sales from $4.8 bn in 2018 to $6.4 bn in 2022. KKR sold its stake in ~2021. As of Q4 22, the company had increased its market cap by almost $4 bn since its IPO, and had returned $2 bn to stakeholders, including $900 million of repurchases. It's been a successful turnaround.</p><p>Merchandise and TAM</p><p>The company sells merchandise across four divisions: Outdoors (Camping, Fishing, Hunting at ~31% of sales), Sports and recreation (Fitness, Team sports, Recreation at ~28% of sales), Apparel (Outdoor, Youth and Athletic apparel at ~21% of sales), and Footwear (Casual, Work, Youth and Athletic footwear at ~20% of sales).</p><p>The company believes its total addressable market in the US is ~$175 bn, of which Dick's Sporting Goods, the largest sporting goods competitor, has less than a 10% share, but also has over 2x the revenues of ASO. This suggests there may be room to take share, and the market looks healthy. The US sporting goods market has/is expected to grow at a 7.9% CAGR from 2019 to 2025, according to a Morgan Stanley Outdoor and Active Living 2022 survey. Additionally, the Bureau of Economic Analysis reports that consumer spending on sporting equipment, supplies, guns and ammunition grew at 5.6% CAGR from 2000 to 2022. The industry appears to be fragmented, but growing.</p><p>Favorable Geographic/Demographic Tailwinds</p><p>Approximately 29% of the company's stores are in the top 5 fastest growing metropolitan statistical areas, including parts of Texas, Tennessee, and Florida. Of note, approximately 40% of the company's stores are in Texas, which last year surpassed 30 million people, and which the company estimates will see population growth of 17% between 2020-30.</p><p>The company believes there is ample opportunity for geographic expansion. Walmart has a store within 10 miles of 88% of Americans. For ASO, the figure is 17%, which means there's a good runway for further penetration.</p><p>Succession</p><p>On June 1, 2023, the company announced that as a result of a planned succession process, Steven Lawrence was promoted from Chief Merchandising Officer and became the new CEO. Lawrence has announced a 5-year plan to open 120-140 new stores and achieve $10 bn in revenue by 2027 (approximately a 10% CAGR), up from ~$6 bn today, with a net income margin of 10%, and ROIC of 30%, which we think are attainable goals. The blueprint is in place, and now it falls to the new CEO to execute. Longer-term, the company sees the opportunity for 800 additional stores.</p><p>According to the <a href="http://www.columbia.edu/~tmd2142/5-best-stock-research-websites.html">best stock research websites</a>, a key driver of the company's growth strategy is expansion of the store base by ~50% over the next few years, with new stores making up $2.4-$2.8 bn of the incremental revenue required to meet the goal of $10 bn.</p><p>Store Economics</p><p>The company's stores average approximately 70,000 SF and are highly profitable. The company seeks to lease all its stores via long-term lease agreements, ranging from 15 to 20 years, and executes sale-leaseback transactions for stores it is developing. ASO's average store delivers ~$4 million in EBIT, which is double the $2 million for Dick's. It costs approximately $4-$5 million to open a new store, which is expected in the first year to achieve $18 million in sales, be EBITDA positive, and have an ROIC of 20%. The stores ramp to $25 million in sales over 4-5 years. If the company can successfully open 120-140 new stores in the next 5 years, as planned, good things will happen to the stock price.</p><p>Omnichannel</p><p>ASO has built an e-commerce and mobile platform that allows enhanced consumer connection with stores. This includes buy-online-pickup-in-store program, and ship-to-store and curbside pickup programs. The company is also enhancing the customer experience through new features such as new site search capabilities, outfitting, express check-out, and biometric security.</p><p>The company expects its omnichannel efforts will be a continued driver of growth and gross margin. The company has stated that 75% of e-commerce sales are fulfilled in stores, and 60% of omnichannel customer spend came from within 10 miles of a store. Omnichannel customers spend more and purchase more frequently than the average customer. During 2022, stores facilitated approximately 95% of ASO's total sales.</p><p>Capex Budget and FCF</p><p>In support of its store expansion and growth goals, the company has unveiled a 5-year capex plan to invest $1.5 bn over the next five years. With $5.5 bn -$6 bn of anticipated adjusted EBIT projected over the next 5 years, the company anticipates the plan will be entirely self-funded through cash flow. Add an additional $0.5 bn to $ 1 bn required for WC and other factors, and that still leaves $3.5 bn in FCF available to stakeholders.</p><p>Return of Capital / Share Repurchases</p><p>The company has repurchased $440 million of stock in the past 12 months, and the company initiated a quarterly cash dividend in FY 22. We believe this is a shareholder friendly management team who will continue to return capital to shareholders when it makes sense. With the stock price as cheap as it is today relative to fundamentals, we would applaud additional buybacks at these prices.</p><ul><li><a href="https://valueinvesting.io/cost-of-equity-calculator">Cost of equity calculator</a></li><li><a href="https://valueinvesting.io/cost-of-debt-calculator">Cost of debt calculator</a></li><li><a href="http://www.columbia.edu/~tmd2142/wacc-calculator.html">WACC calculator</a></li></ul><p>Operational Momentum: Increasing Margins, Returns, Inventory Turns</p><p>The company's gross margins have improved from 29% in 2018 to 34% LTM. Similarly, EBIT margins have improved from 3.5% in 2018, to 11.9% LTM. ROIC has improved from 14% in 2018 to 34% LTM. Inventory turns are also up, from 2.7x in 2018 to 3.2x LTM. These metrics demonstrate that the business has positive operating momentum, and is increasing efficiencies. The company's current ratio has increased from 1.4x a year ago to 1.6x today, indicating increased liquidity. Additionally, the company's ROE is 37%, demonstrating that it is using its equity capital effectively.</p><p>The company also boasts a strong balance sheet. The company's net debt has declined from $1.5 bn in 2018 to $0.5 bn currently. Additionally, the company maintains a $1 bn credit facility, with no debt maturities until 2027. This outperforms all estimates of the <a href="http://www.columbia.edu/~tmd2142/5-best-stock-screeners.html">best stock screeners</a>.</p><p>Summary</p><p>ASO trades at a P/E of 7x, and has an EBITDA/EV yield of 17%, which we think is cheap for a company that has been successfully turned around from 5 years ago, and with good growth prospects for the next 5 years. ASO is positioned to benefit from positive growth trends in its core product areas, and has stores in metropolitan areas that are growing. Its stores have become increasingly profitable and are being run efficiently, and compare favorably to those of its competitors. The company's ROE of 37% and ROIC of 34% are both impressive, indicating that it has done a good job managing its capital. Margins have strengthened over recent years, and are reasonable today with EBITDA margins of 14.9% and net margins of 9%. It has an ambitious store count growth plan for the next 5 years, with self-funded capital and a conservative balance sheet available to pursue it. It is generating strong positive free cash flow, and is buying back stock and returning capital to shareholders via dividends, demonstrating its friendliness to shareholders.</p>
Datasets for relationship between family income in the United States and child mortality
<p>Datasets for relationship between family income in the United States and child mortality</p>
Impact of a Smartphone Application on Postpartum Weight Loss and Breastfeeding Rates Among Low-income, Urban Women
ClinicalTrials.gov study NCT03167073. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Activate For Life: mHealth Intervention To Address Pain And Fatigue In Low-income Older Adults Aging In Place
ClinicalTrials.gov study NCT03853148. IPD Sharing: NO. Countries: 1. Publications: 2.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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