The Sexually Transmitted Disease (STD) Morbidity online databases on CDC WONDER contain case reports reported from the 50 United States and D.C., Puerto Rico, Virgin Islands and Guam. The online databases report the number of cases and disease incidence rates by year, state, disease, age, sex of patient, type of STD, and area of report, since 1984. Data are updated annually. Data are produced by the U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention (CDC), National Center for HIV/AIDS, viral Hepatitis, STD and TB Prevention (NCHHSTP).

This dataset includes STD (salinity, temperature, depth) data collected for the EASTROPAC project in the Pacific Ocean and the North Atlantic Ocean by the U.S. Fish and Wildlife Service from 1967-08-16 to 1976-02-25. See Special Scientific Report - Fisheries No. 588, "Processing of digital data logger STD tapes at the Scripps Institution of Oceanography and the Bureau of Commercial Fisheries, La Jolla, California", by James H. Jones. Data has been processed to the NODC standard High-Resolution STD/CTD Data (F022) format. The F022 format contains high-resolution data collected using CTD (conductivity-temperature-depth) and STD (salinity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity, and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t), and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. A text record is available for comments.

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Multivariable logistic regression analyses of predictors of non-response at the three follow-up data collection moments for participants who visited the STI clinic in Amsterdam.

Our aim was to systematically review data about the risk of sexually transmitted infections (STI) and bacterial vaginosis among lesbian women and to suggest strategies to improve prevention, diagnosis and treatment. A search strategy for lesbian, STI and bacterial vaginosis was applied to PubMed, LILACS and BDENF databases. Of 387 unique references retrieved, 22 fulfilled the inclusion criteria (cross-sectional studies reporting prevalence for 8 STIs/bacterial vaginosis and history of a STI). The most frequent infection reported was bacterial vaginosis, and none study reported data on hepatitis B. A wide range of prevalence was observed for most infections. In terms of risk factors, the number of sexual partners, the past or current smoking, a history of forced sex and sexual stigma seem to increase the risk of STI and bacterial vaginosis. The findings of this review are discussed considering guidelines directly addressing the LGBT community’s health and relevant studies investigating both safe sexual practices and the intricate relationship between LGBT people and their care providers. A set of recommendations to improve preventive care for lesbian women is proposed. Affirming that little is known about the extent of STIs and bacterial vaginosis transmission in female-to-female sexual activities or about the risk factors for STI and bacterial vaginosis among lesbian women is reasonable. In fact, the overall quality of the studies was low or very low with significant uncertainty around their findings. However, we consider that the available knowledge indicates some paths to be followed by care providers and policy decision-makers to improve their actions towards better sexual health of lesbian women.

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

The University of Alaska is responsible for this STD data which was collected aboard the R/V AMAK Cruise AM01 between 09/17/82-09/22/82 by Dr. Burrell of the Institute of Marine Science. There were a total of 28 stations in the Boca de Quadra area. Data has been processed by NODC to the NODC standard High-Resolution CTD/STD (F022) format. The F022 format contains high-resolution data collected using CTD (conductivity-temperature-depth) and STD (salinity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity, and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t), and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. A text record is available for comments.

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The University of Alaska is responsible for this data which was collected aboard R/V Alpha Helix cruise HX59, R/V Amak cruise AM04, and R/V Redoubt cruises RT31 - RT41, between 12/1/82 - 03/24/84, by Dr. George Hunt of the University of California, Irvine, and Dr. Burrell of the Institute of Marine Science. There were a total of 120 stations in the Bering Sea area, and a total of 448 stations in the Boca de Quadra area. Data has been processed by NODC to the NODC standard High-Resolution CTD/STD (F022) format. The F022 format contains high-resolution data collected using CTD (conductivity-temperature-depth) and STD (salinity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity, and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t), and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. A text record is available for comments.

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United States Index: Standard & Poors: S&P Global 1200 Health Care data was reported at 3,475.640 31Dec1997=1000 in Nov 2018. This records an increase from the previous number of 3,296.380 31Dec1997=1000 for Oct 2018. United States Index: Standard & Poors: S&P Global 1200 Health Care data is updated monthly, averaging 1,542.750 31Dec1997=1000 from Dec 2001 (Median) to Nov 2018, with 204 observations. The data reached an all-time high of 3,524.620 31Dec1997=1000 in Sep 2018 and a record low of 1,025.828 31Dec1997=1000 in Feb 2009. United States Index: Standard & Poors: S&P Global 1200 Health Care data remains active status in CEIC and is reported by Standard & Poor's. The data is categorized under Global Database’s United States – Table US.Z017: Standard & Poors: Global and European Indexes.

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This study investigates the utilization of work absence benefits among United States (US) employees diagnosed with COVID-19, examining frequency, duration, cost, and types of work loss benefits used. This retrospective analysis of the Workpartners Research Reference Database (RRDb) included employees eligible for short- and long-term disability (STD and LTD employer-sponsored benefits, respectively), and other paid work absence benefits from 2018 to 2022. Workpartners RRDb includes over 3.5 million employees from over 500 self-insured employers across the US. Employees were identified by codes from adjudicated medical and disability claims for COVID-19 (2020–2022) and influenza, as well as prescription claims for COVID-19 treatments. Associated payments were quantified for each absence reason. Approximately 1 million employees were eligible for employer-sponsored paid leave benefits between January 2018 and December 2022. The mean age was 37 years (22% >50 years), and 49.4% were females. COVID-19 was the 2nd most common reason for an STD claim (6.9% of all STD claims) and 13th for an LTD claim (1.7% of all LTD claims) from 2020–2022. The mean duration for COVID-19 STD claims was 24 days (N = 3,731, mean claim=$3,477) versus 10 days for influenza (N = 283, mean claim=$1,721). The mean duration for an LTD claim for COVID-19 was 153 days (N = 11, mean claim=$19,254). Only 21.5% of employees with STD claims in the COVID-19 cohort had prior COVID-19-associated medical or pharmacy claims; over half (range 53%–61%) had documented high risk factors for severe COVID-19. COVID-19 and influenza have the potential to cause work loss in otherwise healthy employees. In this analysis, COVID-19 was the second most frequent reason for an STD claim at the start of the pandemic and remained high (ranked 5th) in 2022. These results highlight the impact of COVID-19 on work loss beyond the acute phase. Comprehensively evaluating work loss implications may help employers prioritize strategies, such as vaccinations and timely treatments, to mitigate the impact of COVID-19 on employees and their companies. COVID-19 results in short- and long-term symptoms that may affect employees’ ability to work. Short- and long-term disability (STD and LTD, respectively), other work absences, and medical and pharmacy claims from the Workpartners Research Reference Database were analyzed for US adult (≥18 years) employees. COVID-19 claims were identified using the Center for Disease Control and Prevention recommended International Classification of Diseases codes during the analysis from 2020 to 2022. During 2020 to 2022, COVID-19 ranked as the second most frequent reason for STD claims and 13th most frequent among LTD claims. Influenza ranked 58th overall with no LTD claims (2018–2022). The average COVID-19 STD claim lasted 24 days and cost employers $3,477 per claim, and LTD claims averaged 153 days, costing $19,254. Only 21.5% of employees with STD claims in the COVID-19 cohort had prior COVID-19-associated medical or pharmacy claims, and over half (range 53%–61%) had a documented high-risk factor for severe COVID-19. Our results highlight the ongoing and substantial impact of COVID-19 on work absence benefit utilization beyond the acute phase. This analysis demonstrates the need for employers and researchers to review all available medical, pharmacy, and disability claims to assess the acute and long-term impact of COVID-19 on employees and prioritize mitigation strategies to reduce the burden of the virus to their employees.

The FIFE Standard Pressure Level Radiosonde Data Set provides a set of standard level profiles (i.e., 5 mb pressure intervals) from over 450 radiosonde balloon flights, which occurred every one to three hours (daylight hours) during the FIFE IFCs. This derived profile data were computed to 5 mb pressure intervals through simple linear interpolation means. An assumption exists that a linear interpolation scheme may be used with sufficient accuracy to assign meteorological values at 5 mb pressure levels. Some errors are introduced using this method. Several new variables were computed from the original FIFE Radiosonde Data and are included in this derived data set. U (east-west) and V (north-south) winds have been computed from wind speed and direction, and potential temperature has been computed from pressure and temperature. These new parameters are desirable for initial conditions in numerical models as well as forcing functions in models, or as verification and comparison of numerical model's results.

Small island states present a significant challenge in terms of sustainable tourism development. On a small island there are limited resources, economic and social activities tend to be concentrated on the coastal zone, and the interconnectivity between economic, environmental, social, cultural and political spheres is strong and pervasive. Consequently the sustainable development of tourism is more a practical necessity than an optional extra. This paper investigates the question of how to monitor sustainable tourism development (STD) in Samoa, an independent small island state in the South Pacific. It describes some of the methodological considerations and processes involved in the development of STD indicators and particularly highlights the importance of formulating clear objectives before trying to identify indicators, the value of establishing a multi-disciplinary advisory panel, and the necessity of designing an effective and flexible implementation framework for converting indicator results into management action. Available online and also kept in vertical file collection Call Number: VF 6920 [EL] Physical Description: 24 p. ; 29cm