Datasets of Great Britain primary substations integrated with household heating information.

The growing demand for electrified heating, electrified transportation, and power-intensive data centres challenge distribution networks. If electrification projects are carried out without considering electrical distribution infrastructure, there could be unexpected blackouts and financial losses. Datasets containing real-world distribution network information are required to address this. However, the existing dataset at NERC that covers the whole of Great Britain (GB) does not provide information about demand and capacity, which is insufficient for evaluating the connection feasibility. Although each distribution network operator (DNO) has detailed network information for their supply area, the information is scattered in separate files and different formats even within the same DNO, which limits usability. On the other hand, studying the coupling between energy systems and societal attributes such as household heating is important in promoting social welfare, which calls for more comprehensive datasets that integrate the social data and the energy network data. However, social datasets are usually provided on a regional basis, and the link to energy networks is not straightforward, which explains the lack of the comprehensive datasets. To fill these gaps, this paper introduces two datasets. The first is the main dataset for the GB distribution networks, collecting information on firm capacity, peak demands, locations, and parent transmission nodes (grid supply points, namely GSPs) for all primary substations (PSs). PSs are a crucial part of UK distribution networks and are at the lowest voltage level (11 kV) with publicly available data. Substation firm capacity and peak demand facilitate an understanding of the remaining room in the existing network. The parent GSP information helps link the released datasets to transmission networks. These datasets are collected, standardised, and merged from various files with different formats published by the six DNOs in GB, using a Python script and manual validation. The second dataset extends the main network dataset, linking each PS to the number of households that use different types of central heating recorded in census data (Census in year 2021 for England and Wales, and Census 2011 for Scotland as the up-to-date Census 2022 data is not fully released). The derivation of the second dataset is based on the locations of PSs collected in the main dataset with appropriate assumptions. The derivation process may be replicated to integrate other social datasets. The datasets have the following reuse potentials: 1) Given the PS demand, capacity, and locations in our datasets, users can estimate the connection feasibility and evaluate the optimal deployment locations for different energy technologies, including electric vehicles, heat pumps, and the growing data centres, under different scenarios and at a national scale. These evaluations are beneficial not only for academic research, but also for industrial planning and policy making. 2) Our extended dataset links household information to distribution networks. The integrated information facilitates cross-disciplinary research and analysis across social science, energy policy, and power systems. 3) The network demand and capacity information provided by the datasets can also help with realistic parameter settings to improve the accuracy of case studies in broader power system research.

Evaluating the social benefits and network costs of heat pumps as an energy crisis intervention.

Fuel poverty, a pressing issue affecting social prosperity, has been exacerbated during the energy crisis triggered by the Russia-Ukraine conflict. This problem can be more severe for off-gas regions. Our study investigates heat pumps (HPs) as a cost-effective alternative to off-gas heating to alleviate fuel poverty in England and Scotland. We analyze regional fuel poverty rates and the associated greenhouse gas emission reduction by replacing all off-gas heating with HPs, observing positive effects under pre-crisis and crisis conditions, with existing government support for HP upfront costs. HP rollout can burden distribution networks especially for certain regions, but our correlation analysis shows that high benefits do not always come with network costs at the regional level, and we identify "priority" regions with low costs and high benefits. These findings provide valuable insights for policymakers to address fuel poverty and reach decarbonization. The methodology is adaptable to other countries with appropriate datasets.

Multicentre derivation and validation of a prognostic scoring system for mortality assessment in HIV-infected patients with talaromycosis.

BACKGROUND: Although the widespread use of modern antiretroviral therapy (ART) has reduced the incidence of talaromycosis in people living with HIV, mortality remains as high as 20% in this population, even after appropriate antifungal treatment. OBJECTIVES: The objective of our study was to develop a risk assessment system for HIV-infected patients with comorbid talaromycosis, in order to provide these patients with appropriate, effective and potentially life-saving interventions at an early stage of their illness. PATIENTS/METHODS: This was a multicentre, retrospective cohort study conducted in China. We built a predictive model based on data from 11 hospitals, and a validated model using the data of 1 hospital located in an endemic area. RESULTS: Forward stepwise multivariate statistical calculations indicated that age, aspartate aminotransferase/alanine transaminase ratio and albumin levels, and BUN levels were valid, independent predictors of the risk of death in HIV-infected patients with talaromycosis. Our developed and validated risk scoring system is effective for the identification of HIV-infected patients with talaromycosis at high risk of death at hospital admission (p < .001; AUC = 0.860). In our study, our risk prediction model provided functional and robust discrimination in the validation cohort (p < .001; AUC = 0.793). CONCLUSION: The prognostic scoring system for mortality assessment developed in the present study is an easy-to-use clinical tool designed to accurately assist clinicians in identifying high-risk patients with talaromycosis.

Availability and affordability of essential medicines for diabetes across high-income, middle-income, and low-income countries: a prospective epidemiological study.

BACKGROUND: Data are scarce on the availability and affordability of essential medicines for diabetes. Our aim was to examine the availability and affordability of metformin, sulfonylureas, and insulin across multiple regions of the world and explore the effect of these on medicine use. METHODS: In the Prospective Urban Rural Epidemiology (PURE) study, participants aged 35-70 years (n=156 625) were recruited from 110 803 households, in 604 communities and 22 countries; availability (presence of any dose of medication in the pharmacy on the day of audit) and medicine cost data were collected from pharmacies with the Environmental Profile of a Community's Health audit tool. Our primary analysis was to describe the availability and affordability of metformin and insulin and also commonly used and prescribed combinations of two medicines for diabetes management (two oral drugs, metformin plus a sulphonylurea [either glibenclamide (also known as glyburide) or gliclazide] and one oral drug plus insulin [metformin plus insulin]). Medicines were defined as affordable if the cost of medicines was less than 20% of capacity-to-pay (the household income minus food expenditure). Our analyses included data collected in pharmacies and data from representative samples of households. Data on availability were ascertained during the pharmacy audit, as were data on cost of medications. These cost data were used to estimate the cost of a month's supply of essential medicines for diabetes. We estimated affordability of medicines using income data from household surveys. FINDINGS: Metformin was available in 113 (100%) of 113 pharmacies from high-income countries, 112 (88·2%) of 127 pharmacies in upper-middle-income countries, 179 (86·1%) of 208 pharmacies in lower-middle-income countries, 44 (64·7%) of 68 pharmacies in low-income countries (excluding India), and 88 (100%) of 88 pharmacies in India. Insulin was available in 106 (93·8%) pharmacies in high-income countries, 51 (40·2%) pharmacies in upper-middle-income countries, 61 (29·3%) pharmacies in lower-middle-income countries, seven (10·3%) pharmacies in lower-income countries, and 67 (76·1%) of 88 pharmacies in India. We estimated 0·7% of households in high-income countries and 26·9% of households in low-income countries could not afford metformin and 2·8% of households in high-income countries and 63·0% of households in low-income countries could not afford insulin. Among the 13 569 (8·6% of PURE participants) that reported a diagnosis of diabetes, 1222 (74·0%) participants reported diabetes medicine use in high-income countries compared with 143 (29·6%) participants in low-income countries. In multilevel models, availability and affordability were significantly associated with use of diabetes medicines. INTERPRETATION: Availability and affordability of essential diabetes medicines are poor in low-income and middle-income countries. Awareness of these global differences might importantly drive change in access for patients with diabetes. FUNDING: Full funding sources listed at the end of the paper (see Acknowledgments).