An Analysis of the Effects of Sociodemographic Factors on Daily Per Capita Residential Water Use in Texas Cities

Water is a key resource of concern to residents and decision makers in the State of Texas and in many other parts of the United States. Careful planning for its use is of utmost importance for the State and the Nation. Such planning requires careful consideration of numerous factors including hydrologic and physiographic factors, engineering feasibility and economic feasibility. At the same time, it is increasingly evident that water needs are closely tied to population growth and to the social, economic and demographic characteristics of the population (Murdock et al., 1985). Thus, attempts to plan for the use of water resources have become increasingly inclusive of socioeconomic as well as physical variables as the costs of incorrectly projecting water demand and misallocating funds for facility construction and management have become apparent (Stees et al., 1976; McFarland and Hyatt, 1973; Reid, 1971; Texas Department of Water Resources, 1984). To date, however, water-related socioeconomic research has concentrated on: 1. water use policy and water use planning 2. the demographic and social correlates of water and other resource use 3. the effects of water use and availability on demographic and social patterns 4. methodologies for projecting demands for resources and the implications of the use of resources An extensive body of research addresses both the need for, and the dimensions that must be considered in, water use policy formation and planning (Markusen, 1978; U.S. Water Resources Council, 1978; Council for Agriculture Science and Technology, 1982; National Water Commission, 1973; Office of Technology Assessment, 1983; Texas Department of Water Resources, 1984). Such analyses persuasively argue for the use of comprehensive, multidisciplinary planning formats, but as several recent reviews of water resources research efforts have noted (Francis, 1982; Napier et al., 1983), much of the basic research necessary to establish the relationships that should form the bases of the information used in such planning has not been completed. The demographic and social correlates of water use have not been sufficiently established. Although total population and demographic structure characteristics are often used in projecting demands for water resources (Mercer and Morgan, 1978; Texas Department of Water Resources, 1984), several recent efforts evaluating the use of demographic and social variables in water use planning have noted that few of the relationships between demographic and social factors and water use have been established empirically (Murdock et al., 1985; Korsching and Nowak, 1983; Francis, 1982). Thus, it is unclear what effects differences in household or family composition patterns or the age structure of a population have on usage of water and related resources. In like manner, although given some attention in the literature (Larson and Hudson, 1951; Bogue, 1963; Kubat et al., 1968; Francis, 1982; Napier et al., 1983), the relationships between such crucial social variables as socioeconomic status, ethnic status and perceptions of water conservation requirements and water use have not been adequately examined. Since other resource uses, such as energy use (Morrison, 1976), show substantial variation across demographic, social and cultural variables, similar effects are likely to be found between demographic, social and cultural variables and water use. The effects of water use and availability on population and social patterns have been given considerable attention (Williford et al., 1976; Doeksen and Pierce, 1976; Albrecht et al., 1984; Murdock et al., 1984; Albrecht and Hurdock, 1985). Such analyses suggest that changes in water resource availability or in the use of water-related forms of technology may lead to substantial changes in the population bases of areas (Albrecht and Murdock, 1985; Fitzsimmons and Salama, 1977) and may lead to related economic and community service changes (Williford et al., 1976). However, such analyses have tended to use only general and very unrefined assumptions concerning the relationships between water availability, use and technology and demographic and social factors. An extensive body of research has also developed related to the modeling of economic and demographic factors associated with resource use and development (Leistritz and Murdock, 1981; Murdock and Leistritz, 1980; Ford, 1976; Stenehjem and Metzger, 1976; Dunn and Larson, 1963; Nercer and Morgan, 1978). Although such models have become increasingly complex, several recent reviews of these models suggest that validation of the parameter assumptions underlying them is needed (Leistritz and Murdock, 1981; Markusen, 1978). In particular, most such models project water demand and use on the basis of per capita or per population unit factors. Population composition is not taken into account. Overall, then, although a few studies have attempted to include demographic variables--age, household size and patterns, race/ethnicity-and social, cultural and behavioral variables--such as water use preferences and cultural patterns of water use--in planning and projection efforts (Kubat et al., 1968; Dunn and Larson, 1963; Korsching and Nowak, 1983; Portney, 1982), water planning and analyses efforts have largely ignored the effects of demographic factors (other than total population size) and social factors in planning for water use and facility construction. Such neglect is particularly unfortunate in states, such as Texas, where populations display wide demographic and social diversity (Skrabanek et al., 1985) and where per capita water use varies widely from one area to another (Texas Department of Water Resources, 1984). Only if analyses of the relationships between demographic and social variables and water use and demand are completed, will it be possible to adequately employ such variables in projections of water demand. Because the inclusion of such variables in projection models should increase the accuracy of projections and improve our understanding of the numerous factors that determine patterns of water use, studies of the effects of demographic and social factors on water use and on projections of water demand deserve additional consideration. This report presents the results of one such study sponsored by the Texas Water Resources Institute. The study has two major objectives: 1. to determine the relationships between key demographic, social and cultural variables and water use in Texas 2. to analyze the implications of the relationships between demographic, social and cultural variables and water use and demand for projections of water use and demand in Texas Specifically, this report presents the results of an analysis of secondary and primary data in which the relationships between water use and other sociodemographic variables are examined, and it reports the effects of using sociodemographic characteristics to project water use. These relationships are of intrinsic interest to professionals involved in water planning and policy formulation, and the results will hopefully be of utility to a wide range of policy and decision makers. The report is organized into five sections. Section I describes the data and methodologies employed in the analysis. Section II presents and discusses the results of the secondary analysis. Section III examines the results of our analysis of survey data from over 800 respondents from 8 communities selected from across the State of Texas. Section IV describes the implications of using demographic and social factors in projecting water use. The final section, Section V, presents generalizations regarding the overall effects of demographic and social factors on water use and demand ant presents our preliminary recommendations regarding the use of such variables in formulating water use and demand projections. Throughout the report, it should be recognized that the fact that the study is limited to one period of time and to only selected areas of the Stste, clearly limits the ability to formulate generalizations that have statewide applicability. The fact that the study is limited in several regards must be recognized

The Orphan G Protein-coupled Receptors GPR41 and GPR43 Are Activated by Propionate and Other Short Chain Carboxylic Acids

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Progress with the Prime Focus Spectrograph for the Subaru Telescope: a massively multiplexed optical and near-infrared fiber spectrograph

The Prime Focus Spectrograph (PFS) is an optical/near-infrared multi-fiber spectrograph with 2394 science fibers, which are distributed in 1.3 degree diameter field of view at Subaru 8.2-meter telescope. The simultaneous wide wavelength coverage from 0.38 um to 1.26 um, with the resolving power of 3000, strengthens its ability to target three main survey programs: cosmology, Galactic archaeology, and galaxy/AGN evolution. A medium resolution mode with resolving power of 5000 for 0.71 um to 0.89 um also will be available by simply exchanging dispersers. PFS takes the role for the spectroscopic part of the Subaru Measurement of Images and Redshifts project, while Hyper Suprime-Cam works on the imaging part. To transform the telescope plus WFC focal ratio, a 3-mm thick broad-band coated glass-molded microlens is glued to each fiber tip. A higher transmission fiber is selected for the longest part of cable system, while one with a better FRD performance is selected for the fiber-positioner and fiber-slit components, given the more frequent fiber movements and tightly curved structure. Each Fiber positioner consists of two stages of piezo-electric rotary motors. Its engineering model has been produced and tested. Fiber positioning will be performed iteratively by taking an image of artificially back-illuminated fibers with the Metrology camera located in the Cassegrain container. The camera is carefully designed so that fiber position measurements are unaffected by small amounts of high special-frequency inaccuracies in WFC lens surface shapes. Target light carried through the fiber system reaches one of four identical fast-Schmidt spectrograph modules, each with three arms. Prototype VPH gratings have been optically tested. CCD production is complete, with standard fully-depleted CCDs for red arms and more-challenging thinner fully-depleted CCDs with blue-optimized coating for blue arms.Comment: 14 pages, 12 figures, submitted to "Ground-based and Airborne Instrumentation for Astronomy V, Suzanne K. Ramsay, Ian S. McLean, Hideki Takami, Editors, Proc. SPIE 9147 (2014)

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK