Measuring price and income elasticities of residential electricity demand: findings from aggregated and disaggregated data

Published estimates of the price elasticity of residential electricity demand range from -0.29 to -0.70, for analyses based on household level data; however, the area level estimates from range from -0.02 to -0.15. A similar pattern has been reported for estimates of the income elasticity of residential demand for electricity. Each published study relied on one type of data set (aggregated or disaggregated) and these datasets cover different time periods and locations. This raises the question: does the pattern generated by the published results reflect systematic differences generated by the use of aggregated vs. disaggregated data, or does the pattern reflect random variations in the study settings? In this research the hypothesis has been tested that the pattern generated by the published results reflects the use of aggregated vs. disaggregated data, by constructing both an individual-level dataset and a county-level dataset for one state (State of Nevada) covering the period from 2005 to 2011. Both datasets have been used to estimate household and utility level price and income elasticities of residential demand for electricity. This research shows the same pattern reported in the published studies: the magnitude of the estimated price elasticity generated by the disaggregated data exceeds the magnitude of the estimate generated by the disaggregated data. However, the magnitudes of the two income elasticities do not follow the same pattern

A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube

Contrary to Google Search's mission of delivering information from "many angles so you can form your own understanding of the world," we find that Google and its most prominent returned results -- Wikipedia and YouTube, simply reflect the narrow set of cultural stereotypes tied to the search language for complex topics like "Buddhism," "Liberalism," "colonization," "Iran" and "America." Simply stated, they present, to varying degrees, distinct information across the same search in different languages (we call it 'language bias'). Instead of presenting a global picture of a complex topic, our online searches turn us into the proverbial blind person touching a small portion of an elephant, ignorant of the existence of other cultural perspectives. The language we use to search ends up as a cultural filter to promote ethnocentric views, where a person evaluates other people or ideas based on their own culture. We also find that language bias is deeply embedded in ChatGPT. As it is primarily trained on English language data, it presents the Anglo-American perspective as the normative view, reducing the complexity of a multifaceted issue to the single Anglo-American standard. In this paper, we present evidence and analysis of language bias and discuss its larger social implications. Toward the end of the paper, we propose a potential framework of using automatic translation to leverage language bias and argue that the task of piecing together a genuine depiction of the elephant is a challenging and important endeavor that deserves a new area of research in NLP and requires collaboration with scholars from the humanities to create ethically sound and socially responsible technology together

Practical large-scale spatio-temporal modeling of particulate matter concentrations

The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988--2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of $PM_{10}$ for the full time period and $PM_{2.5}$ for a subset of the period. For the earlier part of the period, 1988--1998, few $PM_{2.5}$ monitors were operating, so we develop a simple extension to the model that represents $PM_{2.5}$ conditionally on $PM_{10}$ model predictions. In the epidemiological analysis, model predictions of $PM_{10}$ are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space--time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS204 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Cumulative ultraviolet radiation flux in adulthood and risk of incident skin cancers in women

Background: Solar ultraviolet (UV) exposure estimated based on residential history has been used as a sun exposure indicator in previous case–control and descriptive studies. However, the associations of cumulative UV exposure based on residential history with different skin cancers, including melanoma, squamous cell carcinoma (SCC), and basal cell carcinoma (BCC), have not been evaluated simultaneously in prospective studies. Methods: We conducted a cohort study among 108 578 women in the Nurses' Health Study (1976–2006) to evaluate the relative risks of skin cancers with cumulative UV flux based on residential history in adulthood. Results: Risk of SCC and BCC was significantly lower for women in lower quintiles vs the highest quintile of cumulative UV flux (both P for trend <0.0001). The association between cumulative UV flux and risk of melanoma did not reach statistical significance. However, risk of melanoma appeared to be lower among women in lower quintiles vs the highest quintile of cumulative UV flux in lag analyses with 2–10 years between exposure and outcome. The multivariable-adjusted hazard ratios per 200 × 10−4 Robertson–Berger units increase in cumulative UV flux were 0.979 (95% confidence interval (CI): 0.933, 1.028) for melanoma, 1.072 (95% CI: 1.041, 1.103) for SCC, and 1.043 (95% CI: 1.034, 1.052) for BCC. Conclusions: Associations with cumulative UV exposure in adulthood among women differed for melanoma, SCC, and BCC, suggesting a potential variable role of UV radiation in adulthood in the carcinogenesis of the three major skin cancers

Evaluating geographic imputation approaches for zip code level data: an application to a study of pediatric diabetes

<p>Abstract</p> <p>Background</p> <p>There is increasing interest in the study of place effects on health, facilitated in part by geographic information systems. Incomplete or missing address information reduces geocoding success. Several geographic imputation methods have been suggested to overcome this limitation. Accuracy evaluation of these methods can be focused at the level of individuals and at higher group-levels (e.g., spatial distribution).</p> <p>Methods</p> <p>We evaluated the accuracy of eight geo-imputation methods for address allocation from ZIP codes to census tracts at the individual and group level. The spatial apportioning approaches underlying the imputation methods included four fixed (deterministic) and four random (stochastic) allocation methods using land area, total population, population under age 20, and race/ethnicity as weighting factors. Data included more than 2,000 geocoded cases of diabetes mellitus among youth aged 0-19 in four U.S. regions. The imputed distribution of cases across tracts was compared to the true distribution using a chi-squared statistic.</p> <p>Results</p> <p>At the individual level, population-weighted (total or under age 20) fixed allocation showed the greatest level of accuracy, with correct census tract assignments averaging 30.01% across all regions, followed by the race/ethnicity-weighted random method (23.83%). The true distribution of cases across census tracts was that 58.2% of tracts exhibited no cases, 26.2% had one case, 9.5% had two cases, and less than 3% had three or more. This distribution was best captured by random allocation methods, with no significant differences (p-value > 0.90). However, significant differences in distributions based on fixed allocation methods were found (p-value < 0.0003).</p> <p>Conclusion</p> <p>Fixed imputation methods seemed to yield greatest accuracy at the individual level, suggesting use for studies on area-level environmental exposures. Fixed methods result in artificial clusters in single census tracts. For studies focusing on spatial distribution of disease, random methods seemed superior, as they most closely replicated the true spatial distribution. When selecting an imputation approach, researchers should consider carefully the study aims.</p

Shear wave elastography and parathyroid adenoma: A new tool for diagnosing parathyroid adenomas

AbstractObjectivesThis study prospectively determines the shear wave elastography characteristics of parathyroid adenomas using virtual touch imaging quantification, a non-invasive ultrasound based shear wave elastography method.MethodsThis prospective study examined 57 consecutive patients with biochemically proven primary hyperparathyroidism and solitary parathyroid adenoma identified by ultrasound and confirmed by at least one of the following: surgical resection, positive Technetium–99m Sestamibi Scintigraphy (MIBI) scan, or fine needle aspiration biopsy with positive PTH washout (performed only in MIBI negative patients). Vascularity and shear wave elastography were performed for all patients. Parathyroid adenoma stiffness was measured as shear wave velocity in meters per second.ResultsThe median (range) pre-surgical value for PTH and calcium were 58pg/mL (19, 427) and 10.8mg/dL (9.5, 12.1), respectively. 37 patients had positive MIBI scan. 20 patients had negative MIBI scan but diagnosis was confirmed with positive PTH washout. 42 patients underwent parathyroidectomy, and an adenoma was confirmed in all. The median (range) shear wave velocity for all parathyroid adenomas enrolled in this study was 2.02m/s (1.53, 2.50). The median (range) shear wave velocity for thyroid tissue was 2.77m/s (1.89, 3.70). The shear wave velocity of the adenomas was independent of adenoma size, serum parathyroid hormone concentration, or plasma parathyroid hormone concentration.ConclusionsTissue elasticity of parathyroid adenoma is significantly lower than thyroid tissue. B-mode features and distinct vascularity pattern are helpful tools in diagnosing parathyroid adenoma with ultrasound. Shear wave elastography may provide valuable information in diagnosing parathyroid adenoma

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors

Background: Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations. Methods: We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM2.5–10) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV). Results: The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988–1998 and 1999–2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988–1998 and 1999–2007) and PM2.5–10 (CV R2=0.46 and 0.52 for 1988–1998 and 1999–2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999–2007). Conclusions: Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM2.5–10 with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007

Particulate Matter Air Pollution Exposure, Distance to Road, and Incident Lung Cancer in the Nurses’ Health Study Cohort

Background: A body of literature has suggested an elevated risk of lung cancer associated with particulate matter and traffic-related pollutants. Objective: We examined the relation of lung cancer incidence with long-term residential exposures to ambient particulate matter and residential distance to roadway, as a proxy for traffic-related exposures. Methods: For participants in the Nurses’ Health Study, a nationwide prospective cohort of women, we estimated 72-month average exposures to PM2.5, PM2.5–10, and PM10 and residential distance to road. Follow-up for incident cases of lung cancer occurred from 1994 through 2010. Cox proportional hazards models were adjusted for potential confounders. Effect modification by smoking status was examined. Results: During 1,510,027 person-years, 2,155 incident cases of lung cancer were observed among 103,650 participants. In fully adjusted models, a 10-μg/m3 increase in 72-month average PM10 [hazard ratio (HR) = 1.04; 95% CI: 0.95, 1.14], PM2.5 (HR = 1.06; 95% CI: 0.91, 1.25), or PM2.5–10 (HR = 1.05; 95% CI: 0.92, 1.20) was positively associated with lung cancer. When the cohort was restricted to never-smokers and to former smokers who had quit at least 10 years before, the associations appeared to increase and were strongest for PM2.5 (PM10: HR = 1.15; 95% CI: 1.00, 1.32; PM2.5: HR = 1.37; 95% CI: 1.06, 1.77; PM2.5–10: HR = 1.11; 95% CI: 0.90, 1.37). Results were most elevated when restricted to the most prevalent subtype, adenocarcinomas. Risks with roadway proximity were less consistent. Conclusions: Our findings support those from other studies indicating increased risk of incident lung cancer associated with ambient PM exposures, especially among never- and long-term former smokers. Citation: Puett RC, Hart JE, Yanosky JD, Spiegelman D, Wang M, Fisher JA, Hong B, Laden F. 2014. Particulate matter air pollution exposure, distance to road, and incident lung cancer in the Nurses’ Health Study Cohort. Environ Health Perspect 122:926–932; http://dx.doi.org/10.1289/ehp.130749

Chronic Fine and Coarse Particulate Exposure, Mortality, and Coronary Heart Disease in the Nurses’ Health Study

Background: The relationship of fine particulate matter &lt; 2.5 μm in diameter (PM2.5) air pollution with mortality and cardiovascular disease is well established, with more recent long-term studies reporting larger effect sizes than earlier long-term studies. Some studies have suggested the coarse fraction, particles between 2.5 and 10 μm (PM10–2.5), may also be important. With respect to mortality and cardiovascular events, questions remain regarding the relative strength of effect sizes for chronic exposure to fine and coarse particles. Objectives: We examined the relationship of chronic PM2.5 and PM10–2.5 exposures with all-cause mortality and fatal and nonfatal incident coronary heart disease (CHD), adjusting for time-varying covariates. Methods: The current study included women from the Nurses’ Health Study living in metropolitan areas of the northeastern and midwestern United States. Follow-up was from 1992 to 2002. We used geographic information systems–based spatial smoothing models to estimate monthly exposures at each participant’s residence. Results: We found increased risk of all-cause mortality [hazard ratio (HR), 1.26; 95% confidence interval (CI), 1.02–1.54] and fatal CHD (HR = 2.02; 95% CI, 1.07–3.78) associated with each 10-μg/m3 increase in annual PM2.5 exposure. The association between fatal CHD and PM10–2.5 was weaker. Conclusions: Our findings contribute to growing evidence that chronic PM2.5 exposure is associated with risk of all-cause and cardiovascular mortality