From the Cover: Measuring social equity in urban energy use and interventions using fine-scale data

Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non-White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities.

Cities have become a key action arena for carbon mitigation, with the US Mayors’ Climate Protection Agreement launched in 2005 and the inclusion of cities in the 2011 United Nations Climate Change Conference. More recently, cities have also started to address equity in their carbon mitigation plans (1, 2). For example, in the United States, New York City and Boston have begun to evaluate social inequality in energy use to inform more equitable distribution of energy-related investments (e.g., efficiency rebates) (3–7). To inform equity, cities are seeking methods and metrics that advance a more nuanced and fine-scale understanding of inequality in energy use and in investments, addressing both race and income.Here, we distinguish between social inequality and inequity. Social inequality metrics quantify differences in any parameter of interest based on social stratification (8). Some inequality metrics, such as those representing income inequality, assess inequality across the whole population (or surveys representing the population), using the Gini coefficient (9–11) and interquantile income ratios (e.g., P80/P20). Other inequality metrics, such as those used in public health, compute health risk disparity ratios by race, gender, or income (12). Social equity goes beyond inequality to evaluate the fairness in allocating resources (e.g., energy assistance or health care investments) among different groups to reduce social inequality with a focus on reducing disparities for the most disadvantaged strata in society (8, 13, 14). Thus, the analysis of inequality guides the distributional aspect of social equity (13, 15), that is, the distribution of burdens and benefits across social strata. Social equity also includes a procedural dimension related to the agency and participation of disadvantaged groups in decisions that impact them (16).This paper focuses on distributional equity. Cities face four main challenges, described below, in quantifying social inequality in energy use to inform more equitable investments in energy conservation and efficiency.

• 1.Lack of fine-scale empirical data across a whole city: There have been relatively few analyses of energy use inequality and investments using empirical data (i.e., data provided by utilities) at fine spatial scales within cities to inform equity. A few efforts that explore inequality in intraurban energy use have primarily relied on modeled energy use (17, 18) using the US Energy Information Administration’s Residential Energy Consumption Survey (RECS). RECS only surveys some thousands of homes nationwide (e.g., ∼5,700 in 2015 and ∼12,000 in 2012), resulting in scattered coverage within 10 census divisions, each covering five states on average (19). Consequently, modeled intraurban energy use derived from RECS reports a goodness of fit of ∼60% (17, 18), which can mask social inequality at the fine spatial scale within cities where social stratification by race and income manifests spatially. Thus, empirical data from utilities are much needed, at least at the block group level, which is the finest scale at which sociodemographic data on race and income are reported by the Census Bureau. Furthermore, to inform equity, data on energy conservation and efficiency program participation and investment across the whole city are needed to evaluate the allocation of investments across all neighborhoods in a city. However, only a few cities such as Los Angeles (20) have obtained fine-scale energy use data from utilities to evaluate inequality in energy use covering the whole city. Likewise, only a few studies have explored social inequality in investment in energy conservation and efficiency at the intraurban scale (21, 22). No previous study has evaluated both inequality in energy use and inequity in energy investments at intraurban scales.
• 2.Lack of analysis of disparities in energy use and intensity by both race and income: No previous studies have explored disparities in energy use and intensity by both race and income using real intraurban consumption data. For example, Los Angeles (20) evaluated energy inequality at a fine spatial scale by income but not by race (23, 24). Only two previous studies addressed intraurban racial disparities in heating energy use intensity (EUI) using modeled data from RECS (17, 18); however, there is high uncertainty in RECS-derived models given the small survey sample sizes noted earlier.
• 3.The challenge of spatial scale of data aggregation: When utilities provide intraurban energy usage data to cities, these data are aggregated at different spatial scales, with unknown impacts on energy inequality metrics such as disparity ratios and Gini coefficients. Spatial scales of data aggregation range from the most disaggregated premise level to census blocks (∼76 person on average in the United States), census tracts (1,200 to 8,000 persons), and ZIP (Zone Improvement Plan) code (with an average of ∼8,000 people). For example, several municipal utilities analyze premise-level data for their city policymakers [e.g., Tallahassee (25), Los Angeles (20), and others (26)]; St. Paul, Minnesota has census tract–level data provided by the local utility (27), while California cities have ZIP code–level data (28) complying with state-level regulations on data privacy provided by utilities. The spatial scale of data aggregation can impact the analysis of dispersion, recognized in geography and public health as the modifiable areal unit problem (29, 30). However, this modifiable unit area problem has not been systematically analyzed for energy use inequality due to the lack of both energy use data and sociodemographic data at fine spatial scales. Assessing how the spatial scale of data aggregation impacts energy inequality measures is important, given that different utilities are spatially aggregating data at different scales for subsequent analysis by cities.
• 4.Suitable energy use metrics and analysis procedures to inform equity: Even when city-wide fine-scale energy use data are available, there are few analysis protocols and metrics to evaluate intraurban equitable distribution of investments in conservation and efficiency. While metrics to assess inequalities in energy access, energy burden (i.e., percentage of income spent on energy services), and energy use are well developed (1, 2, 31–36), energy use metrics that best represent the impact of energy conservation and efficiency investments are still evolving. Energy use metrics, such as household energy use (kilowatt hour/household a year), energy use per capita [kilowatt hour/person a year (36)], and household energy use intensity by floor area [kilowatt hour/square feet a year (17, 18)] have been used, but all have challenges. Studies have shown that high-income households have a higher energy consumption primarily due to having larger homes (37). These high-income households are also found to be more “efficient,” showing lower EUI (18, 23). Thus, only tracking total energy use per household will primarily represent floor area effects but not the efficiency of building stock. EUI has more potential to reflect the condition of the building stock and the efficiency of heating and cooling appliances; however, low-income homes may conserve energy by sacrificing thermal comfort, experiencing energy insufficiency (36). Thus, a lower EUI does not necessarily represent more efficient provision of thermal comfort for low-income homes. Housing stock occupancy can also influence EUI, which can be normalized by household size (38), that is, EUI/capita, to capture the impact of occupancy. A better understanding of floor area, along with total household energy use, housing occupancy, and thermal comfort in conjunction with EUI, is needed to develop suitable inequality metrics for energy use. In addition to exploring appropriate energy inequality metrics, there are no analysis protocols to apply those metrics to inform conservation and efficiency investments toward the triple goals of community-wide carbon mitigation, improving energy affordability (reducing burden), and reducing social inequality in energy use and intensity.

To address the above challenges, our paper makes three key contributions. First, we develop a unique intraurban fine-scale dataset, combining sociodemographic data with energy use, occupancy, program participation, and investment data covering all homes/neighborhoods across two cities. Second, using the empirical fine-scale data (suitable to unpack race and income effects), we explore metrics for cities to quantify social inequality in energy use by both income and race and apply those to inform social equity in energy sector investments in conservation and efficiency (ESICE), for example, efficiency rebates, loans, etc. Our study brings together inequality both in energy use and in efficiency investments at the intraurban scale. Third, with the availability of fine-scale data, we provide an assessment on how energy use inequality metrics are impacted by the spatial scale of data aggregation. Overall, this work informs how cities and utilities can gather and analyze information on energy inequality to guide ESICE to advance social equity and carbon mitigation. The analytical tools demonstrated in two cities in this paper are potentially translatable to other cities and utilities.Fine-scale data (block group or finer) on both residential energy use and ESICE across the entire city for Tallahassee, Florida, and St. Paul, Minnesota, are obtained through partnerships with electric utilities under nondisclosure agreements to preserve data privacy, consistent with state and federal regulations. Data were provided at the premise level for Tallahassee’s ∼90,000 households with 1 y monthly energy use and 5 y investment data and at the block level for St. Paul’s ∼110,000 households with 1 y monthly energy use and investment data. The energy investment data include various efficiency programs (e.g., efficiency rebates, home energy use analysis, etc.; SI Appendix, Table S1). Investments in household-scale renewable energy programs, for example, rooftop solar panels, are not within the scope of this study, which focuses on ESICE.The overall method is shown in SI Appendix, Fig. S1, wherein the fine-scale database incorporates social, ecological, infrastructural, and urban form variables, consistent with urban systems frameworks (39).The inequality metrics used in this study include Gini coefficients and disparity ratios. The Gini coefficient provides a general measure of dispersion for a given parameter, without considering social stratification by income or race, with the coefficient ranging from 0 (perfectly equal distribution) to 1 (extremely unequal). We also adapt the concepts of quintile ratios and disparity ratios used in public health (8) to energy use. Energy use disparity ratios by income are computed as the ratio of the average energy attribute (e.g., EUI) reported in the lowest-income quintile block groups (20% lowest) versus that reported in the highest-income quintile. EUI disparity ratios by race are computed as the ratio of EUI in the top 20% most racially diverse block groups (>80th percentile of non-White population percentage) versus the 20% least racially diverse block groups. Disparity ratios are closely related to differences across social groups, for example, a disparity ratio of 2.5 between the lowest- and highest-income groups indicates a 150% difference with respect to the highest-income group.     

Moderate sedation for chest physicians

Specialists in pulmonary and critical care medicine frequently perform invasive procedures that may require sedation or anesthesia for patient comfort. The number and complexities of interventional pulmonary procedures that can be performed in the bronchoscopy suite or critical care unit continues to expand. Procedures that formerly were done only in the operating room on inpatients are now done routinely in the office, ambulatory surgery center, or hospital outpatient department. No matter the setting, the key to successfully performing these procedures is a safe, pain-free environment for the patient. Anesthesia care and procedural sedation services share the goals of providing the patient comfort during a painful procedure and the operating physician an acceptable working environment. Historically, anesthesiologists have applied the expertise gained in managing anesthesia for major surgeries to sedation care for minor procedures. While the supply of anesthesiologists and anesthetists has shown only a modest increase, the growth in minimally invasive procedures has been explosive in recent years. To meet demand, a service, originally known as conscious sedation and now referred to as moderate sedation, has become common, in which the operating physician supervises a specially trained sedation nurse. This article will provide a clinical definition of moderate sedation and then focus on ways to properly code and bill for pulmonary procedures performed with moderate sedation.     

French Oak Wood (Quercus robur) Extract (Robuvit) in Primary Lymphedema: A Supplement,Pilot, Registry Evaluation

The aim of this pilot supplement study was the evaluation of an oak wood extract (Robuvit, Quercus robur [QR], Horphag Research) in an 8-week registry study on lymphatic signs in primary lymphedema.Subjects with primary lymphedema confined to a single leg without skin changes or ulcerations were followed for at least 8 weeks. Lymphedema was mainly present distally (below the knee). Three groups were formed: one group used only the standard management for lymphedema; one used the same management plus 300 mg Robuvit; and one used the standard management plus 600 mg of Robuvit.The three groups were comparable. After 8 weeks the variation in leg volume was on average −6.2% with standard management, −15% in the QR 300 mg group, and −18.9% in the 600 mg group. The edema score was also significantly lower at 8 weeks in the two QR groups. The variation in proteins in the interstitial fluid in comparison with initial values was −14.8% in controls in comparison with −29.9% in QR 300 mg group and −36.9% in QR 600 mg group. Skin flux significantly improved (increased) in the two QR groups. Ultrasound pretibial skin thickness was decreased on average 6% in controls versus 10.3% in the low-dose QR group and 11.8% in the higher dose group. Perimalleolar thickness was decreased 7% in controls and more in the two QR groups. Ankle circumference was decreased 4.4% in controls and more in the two supplement groups.This pilot registry indicates that Robuvit can be effective in the management of primary lymphedema. More patients and longer evaluation periods are needed.     

Children's online coping strategies: Rethinking coping typologies in a risk-specific approach

Understanding how children deal with problematic situations online is helpful in developing efficient awareness raising and online resilience building initiatives. In this article, we will discuss and develop typologies for online coping strategies. In a school survey, 2046 Flemish children aged 10–16 were asked about how they (would) respond when confronted with different types of online risks. Using principal component analyses and multi-dimensional scaling, we identified different types of cross-risk and risk-specific coping strategies, and explored which types of coping have similar underlying meanings. The results suggest to distinguish behavioral avoidance tactics from mere passive responses or indifference. Young people tend to perceive online coping strategies along two dimensions: engagement versus disengagement and technical versus non-technical measures. Behavioral avoidance is popular among younger children and is associated with a medium level of active engagement and often combined with communicative approaches. Girls are more communicative and respond more proactively.     

系列康复治疗对住院慢性精神分裂症患者个人和社会功能疗效

目的:研究系列康复治疗对住院慢性稳定期精神分裂症患者的个人和社会功能的疗效。方法:将符合入组标准的120例住院慢性稳定期精神分裂症患者随机分为两组,干预组每周接受系列康复治疗2次,共治疗12周。在干预前、干预4周,干预8周及干预12周对2组患者分别采用个人和社会功能量表中文版(PSP)及阳性和阴性症状量表(PANSS)进行评估。结果:两组患者在康复治疗前量表及各项因子评分差异均无统计学意义(P0.05);在康复治疗第8周至12周结束时,两组在扰乱及攻击行为的评分差异有显著的统计学意义(P0.05),精神症状因子评分差异存在统计学意义(P0.05);系列康复治疗对干预组患者的治疗效果也存在随着时间变化的趋势,特别是个人关系和社会关系、自我照料、扰乱及攻击行为,以及精神症状的时间主效应存在统计学意义(P均0.01)。结论:系列康复治疗有助于改善住院慢性稳定期精神分裂症患者的个人和社会功能及残留的部分精神症状;其中,自我照料、扰乱及攻击行为和精神症状、阴性症状改善明显。     

Intrapocket topical anesthetic versus injected anesthetic for pain control during scaling and root planing in adult patients: Systematic review and meta-analysis

Background

In this systematic review and meta-analysis, the authors evaluated the pain during scaling and root planing with use of topical anesthetic versus that with the use of injected anesthetic in adult patients.

Submucosal injection of dexamethasone reduces postoperative discomfort after third-molar extraction: A systematic review and meta-analysis

Background

The authors conducted a systematic review and meta-analysis on the effect of dexamethasone (DX) on edema, trismus, and pain during early and late postoperative periods after third-molar (M3) extraction.