Intellectual curiosity does not a crime make. Ok have someone break into your house take something of yours. You are not to complain as even though that is illegal also, the person who took something of yours is like you takes things that is not yours to take so is illegal.
But that is Ok as the theif just has the same morale as you. Contrary to your assumptions I have no intention of stealing anyone's property. Anyway, I really just wanted to understand the issue about keygens.
Firstly I did not know that having the code itself was illegal. I thought using it for the purpose of selling copies without paying proper royalties was illegal and also just using it for ongoing personal use after a reasonable trial period I am very slow because I tend not to persevere as well as most would Like having a knife is not the same as stabbing someone with it.
While I am grateful for that information I personally still don't see any justification for being deliberately lied to by Norton, if that is what they have done and I don't understand anyone's acceptance of that whatever overarching legal principle is involved. Please don't attack me personally. You have jumped to conclusions about me.
I have no intention of stealing anyone's property and take offence at such direct accusations. I would not steal anyone's property and clearly would object to mine being taken. Since you have made assumptions about me I assume that you are most likely a copyright stakeholder given your vehement attack on me and I say to you that I agree absolutely that you are entitled to fair recompense for your intellectual property and I understand the difficulties you must have in this digital age; but a man in possession of a gun is not automatically guilty of murder.
I am truly sorry if I have offended your 'superior' moral beliefs when I quite naively asked about Norton's policy. I simply wanted to check the veracity of reports that Norton routinely and deliberately lied about virus content. I now finally have one reply actually answering that question. No hard feelings I sincerely hope. Norton says your file contained Sdbot. When you download illegal software from unscrupulous sources, it is not that unusual to get a little free extra bonus like that. Back to top.
Reg: Nov Kudos 0. You will need a working product key and a phone. You now have an activated copy of Microsoft Office After releasing Microsoft Office a few years ago, Microsoft has released new, improved suites with better features.
One of the changes Microsoft has made is the way activation works. Today, users need to buy an Office subscription for Home and Professional previously known Ultimate editions of the software. With this new subscription model, users get the product and a cloud storage space of 1TB. This means you cannot get Microsoft Office from the Microsoft website. Microsoft wants all users to use the latest version of Microsoft Office.
However, you can still get Microsoft Office Free download Microsoft Office might be possible on some websites, but you should be wary of external threats to your PC. Regardless of how you get your copy of Microsoft Office , you will need a valid product key to activate it.
But you will not be able to use important features such as editing and saving documents, but you will be able to open them. Disease Severity Measures File: This discharge—level file contains information from two different sets of disease severity measures. Information from the severity file is to be used in conjunction with the Inpatient Core file. This file is available beginning with the NIS. Available online documentation and supporting files are detailed in Appendix I , Table 4.
All releases of the NIS contain two types of data: inpatient stay records and hospital information with weights to calculate national estimates. Not all data elements in the NIS are uniformly coded or available across all States. The tables in Appendix III are not complete documentation for the data. To download and run the load programs, follow these steps:. Refer to these resources to understand the structure and content of the NIS and to aid in using the database.
This tutorial also describes how to verify that the data have loaded correctly. The Calculating Standard Errors tutorial shows how to accurately determine the precision of the estimates produced from the HCUP nationwide databases. Users will learn two methods for calculating standard errors for estimates produced from the HCUP national nationwide databases.
New tutorials are added periodically and existing tutorials are updated when necessary. This section provides a brief synopsis of special considerations when using the NIS. Missing data values can compromise the quality of estimates. If the outcome for discharges with missing values is different from the outcome for discharges with valid values, then sample estimates for that outcome will be biased and inaccurately represent the discharge population.
The percentage of missing race values was higher in previous years. Therefore race—specific estimates may be biased. This is especially true for estimates of discharge totals by race. There are several techniques available to help assess and overcome this missing data bias. It may be important for researchers to calculate a measure of precision for some estimates based on the NIS sample data. Variance estimates must take into account both the sampling design and the form of the statistic.
A stratified systematic sample of discharges was drawn from a sorted list of discharges comprising all discharges in the sampling frame. To accurately calculate variances from the NIS, you must use appropriate statistical software and techniques. If discharges inside the sampling frame are similar to discharges outside the frame, the sample of discharges can be treated as if they were randomly selected from the entire universe of discharges within each stratum.
Standard formulas for a stratified, single—stage cluster sample without replacement should still be used to calculate statistics and their variances in most applications. A multitude of statistics can be estimated from the NIS data. Several computer programs are listed below that calculate statistics and their variances from sample survey data.
Some of these programs use general methods of variance calculations e. However, it may be desirable to calculate variances using formulas specifically developed for some statistics. These variance calculations are based on finite—sample theory, which is an appropriate method for obtaining cross—sectional, national estimates of outcomes. According to finite—sample theory, the intent of the estimation process is to obtain estimates that are precise representations of the national population at a specific point in time.
In the context of the NIS, any estimates that attempt to accurately describe characteristics and interrelationships among hospitals and discharges during a specific year should be governed by finite—sample theory. Examples of this would be estimates of expenditure and utilization patterns.
Alternatively, in the study of hypothetical population outcomes not limited to a specific point in time, the concept of a "superpopulation" may be useful. Analysts may be less interested in specific characteristics from the finite population and time period from which the sample was drawn than they are in hypothetical characteristics of a conceptual "superpopulation" from which any particular finite population in a given year might have been drawn.
According to this superpopulation model, the national population in a given year is only a snapshot in time of the possible interrelationships among hospital and discharge characteristics. In a given year, all possible interactions between such characteristics may not have been observed, but analysts may wish to predict or simulate interrelationships that may occur in the future. Under the finite—population model, the variances of estimates approach zero as the sampling fraction approaches one.
This is the case because the population is fixed at that point in time, and because the estimate is for a fixed characteristic as it existed when sampled. This is in contrast to the superpopulation model, which adopts a stochastic viewpoint rather than a deterministic viewpoint.
That is, the national discharge population in a particular year is viewed as a random sample that resulted from a specific set of random events drawn from an underlying superpopulation of similar random events that might have occurred. For example, the outcome of a particular hospitalization might differ depending admission timing, hospital staffing during the stay, and so on.
Different methods are used for calculating variances under the two sample theories. The choice of an appropriate method for calculating variances for nationwide estimates depends on the type of measure and the intent of the estimation process. The discharge weights are useful for producing discharge—level statistics for analyses that use the discharge as the unit of analysis. The discharge weights may be used to estimate national population statistics.
In most cases, computer programs are readily available to perform these calculations. Several statistical programming packages allow weighted analyses. In addition, several statistical analysis programs have been developed to specifically calculate statistics and their standard errors from survey data.
The NIS database includes a Hospital Weights file with data elements required by these programs to calculate finite population statistics. The file includes hospital identifiers Primary Sampling Units or PSUs , stratification data elements, and stratum—specific totals for the numbers of discharges and hospitals so that finite—population corrections can be applied to variance estimates.
In addition to these subroutines, standard errors can be estimated by validation and cross—validation techniques. Given that a very large number of observations will be available for most analyses, it may be feasible to set aside a part of the data for validation purposes.
Standard errors and confidence intervals can then be calculated from the validation data. If the analytic file is too small to set aside a large validation sample, cross—validation techniques may be used.
For example, ten—fold cross—validation would split the data into ten subsets of equal size. The estimation would take place in ten iterations. In each iteration, the outcome of interest is predicted for one—tenth of the observations by an estimate based on a model fit to the other nine—tenths of the observations. Unbiased estimates of error variance are then obtained by comparing the actual values to the predicted values obtained in this manner.
Thus longitudinal analyses of specific hospitals is not supported by the NIS. The purpose of the survey is to collect utilization, financial, service, and personnel information on each of the nation's hospitals. The survey's overall response rate averages approximately 85 percent each year, which is high for a voluntary survey given its length and the size of the universe about 6, hospitals.
For hospitals that do not respond, the AHA imputes items based on prior—year information, so that data are available for all hospitals in the universe.
The hospital universe is defined by all hospitals that were open during any part of the calendar year and were designated as community hospitals in the AHA Annual Survey. For purposes of the NIS, the definition of a community hospital is that used by the AHA: "all nonfederal short—term general and other specialty hospitals, excluding hospital units of institutions. Beginning with the redesign, rehabilitation hospitals are excluded.
Beginning with the redesign, long—term acute care hospitals are also excluded. Long—term acute care hospitals are classified as community hospitals by the AHA if they have an average length—of—stay ALOS less than 30 days. Thus, long term acute care facilities were eliminated from the NIS. Prior to the NIS, NIS sample weights were calculated by dividing the number of universe discharges by the number of sampled discharges within each hospital stratum.
The number of universe discharges had been estimated using data from the AHA annual hospital survey. In particular, the total number of discharges in the universe was estimated by the sum of births and admissions contained in the AHA annual survey for all hospitals in the universe. Given that HCUP Partners supply more than 95 percent of discharges nationwide, beginning with the NIS, we estimate the universe count of discharges within each stratum, using the actual count of discharges contained in HCUP data.
In the redesign, a logical corollary of switching from AHA discharge estimates to SID discharge counts was to distinguish unique hospitals using the SID hospital identifiers rather than the AHA hospital identifiers. For the vast majority of hospitals, the SID hospital identifiers are in one-to-one correspondence with the AHA hospital identifiers. However, about 10 percent of the AHA identifiers actually correspond to two or more hospitals in the SID that have common ownership within a hospital system.
For these "combined" AHA identifiers, the number of estimated discharges and the number of hospital beds in the AHA data reflect the sum of estimated discharges and the sum of beds, respectively, from the constituent hospitals. As a result, these combined hospitals could have been allocated to the wrong bed size stratum in the sample design. Also, the between—hospital variance was combined with the within—hospital variance for these combined hospitals.
Therefore, use of the SID hospital identifiers in the NIS disaggregates the previously combined hospitals in many States, which is likely to improve the classification of hospitals and improve variance estimates.
Given the increase in the number of contributing States, the NIS team evaluated and revised the sampling and weighting strategy for and subsequent data years, in order to best represent the U. This included changes to the definitions of the strata data elements, the exclusion of rehabilitation hospitals from the NIS hospital universe, and a change to the calculation of hospital universe discharges for the weights.
A description of the sampling procedures and definitions of strata data elements used from through can be found in the special report: Design of the HCUP Nationwide Inpatient Sample, Again in , the NIS team evaluated and revised the sampling strategy for and subsequent data years, in order to best represent the U.
Prior to , the NIS sampling strata were defined based on five hospital characteristics contained in the AHA hospital files. Beginning with the NIS, the only hospital—level stratification factor that changes is that we stratify hospitals by census division rather than census region ; 10 and the stratification data elements were defined as follows:. We did not split rural hospitals according to teaching status, because rural teaching hospitals were rare.
We defined the bed size categories within location and teaching status because they would otherwise have been redundant.
Rural hospitals tend to be small; urban non—teaching hospitals tend to be medium—sized; and urban teaching hospitals tend to be large. Yet it was important to recognize gradations of size within these types of hospitals. For example, in serving rural discharges, the role of "large" rural hospitals particularly rural referral centers often differs from the role of "small" rural hospitals.
To further ensure geographic representativeness of the sample, implicit stratification data elements included de—identified hospital number, Diagnosis Related Group DRG and admission month. The discharges were sorted according to these data elements prior to systematic random sampling. This sample size was determined by AHRQ based on their experience with similar research databases.
The overall design objective was to select a sample of hospitals that accurately represents the target universe, which includes hospitals outside the frame i. Moreover, this sample was to be geographically dispersed, yet drawn only from data supplied by HCUP Partners. Starting with the NIS, a systematic sampling design is used to construct the database. Both designs selected approximately 20 percent of the target universe of discharges from United States community hospitals, excluding rehabilitation and long—term acute care hospitals.
It ensures that the sample is representative of the population on the following critical factors: hospital factors hospital — unidentified, census division, ownership, urban—rural location, teaching status, number of beds and patient factors diagnosis—related group, admission month. Within each stratum all discharges are sorted in the following order on patient—level "control" variables: encrypted hospital ID, DRG, admission month, and a random number.
It should be possible, for example, to estimate DRG—specific average lengths of stay across all U. Ideally, relationships among outcomes and their correlates estimated from the NIS should accurately represent all U. It is advisable to verify your estimates against other data sources, especially for specific patient populations e. However, systematic sampling design was preferred for several reasons:.
Within each stratum, dischargers are sorted by re—identified hospital number. Then, within each hospital, discharges are sorted by their DRG and their admission month. This sorting ensures that the NIS sample will be representative on these factors.
Next, within each stratum, a number of discharges proportionate to the number of discharges in the universe are selected systematically from the sorted list. For example, if the sampling frame was equal to the universe and 20 percent of the universe was required, then every fifth discharge would be selected from the sorted list of discharges, beginning with a randomly selected start at discharge number 1, 2, 3, 4, or 5 on the list.
To ensure a self—weighted sample that has 20 percent of the universe within each stratum represented, sampling rates would vary within each stratum, depending on the proportion of the population of discharges covered by the discharges in the sampling frame. Thus, the sampling rate would not always be 20 percent within each stratum.
For strata that were missing more discharges, the sampling rate would be higher to ensure that the number of sampled discharges would equal 20 percent of the universe. To obtain nationwide estimates, we developed discharge weights to extrapolate NIS sample discharges to the discharge universe.
NIS discharge weights are calculated by dividing the number of universe discharges by the number of sampled discharges within each NIS stratum. Historically, the number of universe discharges had been estimated using data from the AHA annual hospital survey. Given that HCUP Partners supply more than 95 percent of discharges nationwide, beginning with the NIS, we now estimate the universe count of discharges within each stratum using the actual count of discharges contained in HCUP data.
For example, when a hospital contributed only six months of discharge data to HCUP, the adjusted number of discharges is double the observed number. The previous design provided discharge weights that reflected the universe of discharges in each of the four census regions.
The NIS design provides discharge weights that reflect the universe of discharges in each of the nine census divisions. Each discharge weight is essentially equal to the number of target universe discharges that each sampled discharge represents in its stratum.
Discharge weights to the universe were calculated as follows: Within stratum s , each NIS sample discharge's universe weight was calculated as:. Thus, each discharge's weight DISCWT is equal to the number of universe discharges it represents in stratum s during that year. All States, by U. Figure 2: Percentage of U. Population on July 1, The table below enumerates the types of restrictions applied to the National Inpatient Sample. Restrictions include the following types:. Studies based on the NIS help policymakers understand cost, access, quality, utilization, and health outcomes of hospital services.
It is critical that the NIS be designed to optimize its capacity for national estimates. The NIS sampling frame has grown from 8 States in , to 22 States in , to 46 States in —currently covering 97 percent of the U. Because the sampling frame for the NIS contains nearly the entire universe of discharges, in we evaluated the sampling approach to determine whether a different strategy could improve the accuracy of national estimates from the NIS.
As a result of the evaluation study, a new NIS sample design was recommended. This evaluation:. AHRQ has elected to deploy the systematic sampling design that was recommended, effective with the NIS that is planned for public release in June, This report lays out the implementation of the new design. For a previous evaluation performed during , 2 the project team considered and compared three alternative sampling designs to the present NIS design: 1 a slight modification to the present NIS design that stratified hospitals into nine census divisions instead of four census regions, 2 a Neyman allocation design that optimized the estimates of average length of stay ALOS , and 3 a self-weighting systematic design that took into account patient characteristics such as diagnoses, age, and admission date.
The present NIS design draws percent of discharges from a sample of approximately 1, hospitals, whereas the proposed systematic design samples a fraction of discharges from across all HCUP hospitals over 4, in The systematic sample is a self-weighted sample design that is similar to simple random sampling, but it is more efficient and it ensures that the sample is representative of the population on the following critical factors—.
The superior performance of the systematic design that samples discharges across all hospitals is not surprising, because patient characteristics and mean outcomes vary significantly among hospitals. Variation in mean outcomes such as ALOS, charges, and mortality rates for discharges among hospitals causes a net loss of information under the present NIS design, which draws a sample of hospitals.
This is compared with the systematic design, which draws the same total number of discharges across the entire spectrum of hospitals participating in HCUP. Even though the present NIS design stratifies the hospital sample by hospital characteristics, there can be considerable variation in mean outcomes estimated from one hospital sample to the next, depending on which hospitals are selected for the sample. In contrast, the systematic sampling strategy selects a sample of discharges from all hospitals, which better represents the entire universe of hospitals and increases the information in the total sample of discharges.
For national-level estimates, the systematic design reduced the margin of error by 42 to 48 percent over the present NIS design for the outcomes studied ALOS, average charges, and mortality rates , thus the new NIS design will be about twice as precise as the old design.
Technically, it is the half-width of a confidence interval around a sample statistic, such as a rate or a mean. Along with the sample design changes, AHRQ proposed the following changes to enhance confidentiality and focus the NIS on national estimates:. Partners who attended the presentation indicated their support.
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