This SAS program calculates CFI for each patient from analytic data files containing information on patient identifiers, ICD-9-CM diagnosis codes (version 32), ICD-10-CM Diagnosis Codes (version 2020), CPT codes, and HCPCS codes. NOTE: When downloading, store "CFI_ICD9CM_V32.tab", "CFI_ICD10CM_V2020.tab", and "PX_CODES.tab" as csv files (these files are originally stored as csv files, but Dataverse automatically converts them to tab files). Please read "Frailty-Index-SAS-code-Guide" before proceeding. Interpretation, validation data, and annotated references are provided in "Research Background - Claims-Based Frailty Index".

We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

In the period of November 2022 to October 2023, the Customer Satisfaction Index of SAS Scandinavian Airlines measured 69 out of 100, which was the second lowest level of satisfaction recorded in the past decade.

Weighting systems used for the analysis of the data in Table 1.

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

data file in SAS format

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I-CVI, item-level content validity index.S-CVI/UA, scale-level content validity index, universal agreement calculation method.

This statistic shows the Customer Satisfaction Index (CSI) of Norwegian Air Shuttle and SAS Scandinavian Airlines from 2003 to 2017. In 2017, SAS Scandinavian Airlines ranked highest, with an index score of 74, which means, that customers were satisfied with the airline. Norwegian Air Shuttle's index score was 68, meaning that customers had an indifferent or dissatisfied opinion on the airline.

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The global parallel shaft indexer market, valued at $714 million in 2025, is projected to experience robust growth, driven by increasing automation across various industries, particularly in automotive manufacturing, packaging, and food processing. The 4.4% CAGR from 2025 to 2033 indicates a steady expansion, fueled by the need for high-precision indexing mechanisms in automated production lines. Demand for improved productivity, enhanced accuracy, and reduced downtime are key factors stimulating market growth. Technological advancements, such as the incorporation of advanced control systems and improved materials, further contribute to this positive trajectory. While potential restraints like initial investment costs and the complexity of integration into existing systems exist, the long-term benefits of increased efficiency and output outweigh these challenges. The market is segmented by application (automotive, packaging, etc.), type (mechanical, electromechanical), and geographic region, with North America and Europe currently holding significant market shares due to established industrial automation infrastructure. Companies like Motion Index Drives, Inc., MCPI SAS, and others are key players, competing through innovation and strategic partnerships to capture market share. The competitive landscape is characterized by both established players and emerging companies vying for market dominance. Differentiation strategies often focus on specialized features, such as high-speed indexing capabilities, customized solutions, or superior reliability. Future growth will likely be influenced by the adoption of Industry 4.0 technologies, including integration with IoT platforms and advanced data analytics for predictive maintenance. The market's expansion is also closely tied to the overall health of the global economy and the sustained investment in automation across diverse industrial sectors. The continuous development of more precise, efficient, and cost-effective parallel shaft indexers will be critical in driving further market penetration.

The Pedestrian Crash Data Study (PCDS) collected detailed data on motor vehicle vs pedestrian crashes.

SAS Code for Spatial Optimization of Supply Chain Network for Nitrogen Based Fertilizer in North America, by type, by mode of transportation, per county, for all major crops, using Proc OptModel. the code specifies set of random values to run the mixed integer stochastic spatial optimization model repeatedly and collect results for each simulation that are then compiled and exported to be projected in GIS (geographic information systems). Certain supply nodes (fertilizer plants) are specified to work at either 70 percent of their capacities or more. Capacities for nodes of supply (fertilizer plants), demand (county centroids), transhipment nodes (transfer points-mode may change), and actual distance travelled are specified over arcs.

Health Canada has developed a series of research tools. These tools assess the extent a group of individuals are following Canada’s food guide: Healthy Eating Food Index Canadian Food Intake Screener Canadian Eating Practices Screener This page provides detailed information on the Healthy Eating Food Index (HEFI). The pdf document entitled "Calculating HEFI scores" describes how to calculate HEFI-2019 scores. The accompanying data files provide the user with the SAS macro and the information to apply the SAS macro. For more information on the screeners, please see: https://open.canada.ca/data/en/info/4f1c44a6-1ecf-4da7-a1da-058b1ff9ce06

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Study objectivesObstructive sleep apnea (OSA) severity has been suggested in aldosterone elevation in resistant hypertension, whereas it is undetermined in the rest population. We explored the association of OSA parameters with plasma aldosterone concentration (PAC) in participants with and without hypertension.MethodsWe enrolled clinically hypertensive patients with polysomnography and PAC data under no interfering agents, compared (log) PAC, and assessed the linearity of log PAC by tertiles (T1/2/3) of sleep parameters and their association using linear regression by gender and age. We enrolled participants with and without hypertension who had No-SAS scale and PAC data from the community and duplicated the observations from clinical setting considering age, gender, and presence of hypertension.ResultsOf the 2,066 clinical patients with hypertension (1,546 with OSA), men participants (n=1,412), log apnea–hypopnea index (p=0.043), apnea index (AI, p=0.010), and lowest oxygen saturation (LSaO2, p=0.013) showed significant linearity with log PAC. Log AI (B=0.04, 95%CI: 0.01,0.07, p=0.022) and log LSaO2 (B=−0.39, 95%CI: −0.78,−0.01, p=0.044) showed significant positive and negative linear associations with log PAC in regression. In community dwellers, 6,417 participants with untreated hypertension (2,642 with OSA) and 18,951 normotensive participants (3,000 with OSA) were included. Of the men participants with and without hypertension, the OSA group showed significantly higher (log) PAC than did their counterparts, and log No-SAS score showed positive association with log PAC (hypertension: B=0.072, 95%CI: 0.002,0.142, p=0.043; normotension: B=0.103, 95%CI: 0.067,0.139, p

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