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SASInstitute A00-211 : SAS Base Programming for SAS (r) 9 Exam

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Test Number : A00-211
Test Name : SAS Base Programming for SAS (r) 9
Vendor Name : SASInstitute
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Exam ID : A00-211
Exam Name : SAS Base Programming for SAS (r) 9 (SASInstitute)
Questions : 40-45
Duration : 135 min.
Passing Scores : 725
Total Point Scores : 1000

Access data.
• Access SAS data sets with the SET statement.
• Use PROC IMPORT to access non-SAS data sources.
o Read delimited and Microsoft Excel (.xlsx) files with PROC IMPORT.
o Use PROC IMPORT statement options (OUT=, DBMS=, REPLACE)
o Use the GUESSINGROWS statement
• Use the SAS/ACCESS XLSX engine to read a Microsoft Excel workbook.xlsx file.

Combine SAS data sets.
• Concatenate data sets.
• Merge data sets one-to-one.
• Merge data sets one-to-many.
Create and manipulate SAS date values.
• Explain how SAS stores date and time values.
• Use SAS informats to read common date and time expressions.
• Use SAS date and time formats to specify how the values are displayed.
Control which observations and variables in a SAS data set are processed and output.
• Use the WHERE statement in the DATA step to select observations to be processed.
• Subset variables to be output by using the DROP and KEEP statements.
• Use the DROP= and KEEP= data set options to specify columns to be processed and/or output.

Sort observations in a SAS data set.
• Use the SORT Procedure to re-order observations in place or output to a new dataset.
• Remove duplicate observations with the SORT Procedure.
Conditionally execute SAS statements.
• Use IF-THEN/ELSE statements to process data conditionally.
• Use DO and END statements to execute multiple statements conditionally.
Use assignment statements in the DATA step.
• Create new variables and assign a value.
• Assign a new value to an existing variable.
• Assign the value of an expression to a variable.
• Assign a constant date value to a variable.
Modify variable attributes using options and statements in the DATA step.
• Change the names of variables by using the RENAME= data set option.
• Use LABEL and FORMAT statements to modify attributes in a DATA step.
• Define the length of a variable using the LENGTH statement.
Accumulate sub-totals and totals using DATA step statements.
• Use the BY statement to aggregate by subgroups.
• User first. and last. processing to identify where groups begin and end.
• Use the RETAIN and SUM statements.
Use SAS functions to manipulate character data, numeric data, and SAS date values.
• Use SAS functions such as SCAN, SUBSTR, TRIM, UPCASE, and LOWCASE to perform
tasks such as the tasks shown below.
o Replace the contents of a character value.
o Trim trailing blanks from a character value.
o Search a character value and extract a portion of the value.
o Convert a character value to upper or lowercase.
• Use SAS arithmetic, financial, and probability functions to create or modify numeric values by using the INT and ROUND functions.
• Create SAS date values by using the functions MDY, TODAY, DATE, and TIME.
• Extract the month, year, and interval from a SAS date value by using the functions YEAR, QTR, MONTH, and DAY.
• Perform calculations with date and datetime values and time intervals by using the functions INTCK, INTNX, DATDIF and YRDIF

Use SAS functions to convert character data to numeric and vice versa.
• Explain the automatic conversion that SAS uses to convert values between data types.
• Use the INPUT function to explicitly convert character data values to numeric values.
Process data using DO LOOPS.
• Explain how iterative DO loops function.
• Use DO loops to eliminate redundant code and to perform repetitive calculations.
• Use conditional DO loops.
• Use nested DO loops.
Restructure SAS data sets with PROC TRANSPOSE.
• Select variables to transpose with the VAR statement.
• Rename transposed variables with the ID statement.
• Process data within groups using the BY statement.
• Use PROC TRANSPOSE options (OUT=, PREFIX= and NAME=).
Use macro variables to simplify program maintenance.
• Create macro variables with the %LET statement
• Use macro variables within SAS programs.
Error Handling
Identify and resolve programming logic errors.
• Use the PUTLOG Statement in the Data Step to help identify logic errors.
• Use PUTLOG to write the value of a variable, formatted values, or to write values of all variables.
• Use PUTLOG with Conditional logic.
• Use temporary variables N and ERROR to debug a DATA step.
Recognize and correct syntax errors.
• Identify the characteristics of SAS statements.
• Define SAS syntax rules including the typical types of syntax errors such as misspelled keywords, unmatched quotation marks, missing semicolons, and invalid options.
• Use the log to help diagnose syntax errors in a given program.

Generate list reports using the PRINT procedure.
• Modify the default behavior of PROC PRINT by adding statements and options such as
o use the VAR statement to select and order variables.
o calculate totals with a SUM statement.
o select observations with a WHERE statement.
o use the ID statement to identify observations.
o use the BY statement to process groups.
Generate summary reports and frequency tables using base SAS procedures.
• Produce one-way and two-way frequency tables with the FREQ procedure.
• Enhance frequency tables with options (NLEVELS, ORDER=).
• Use PROC FREQ to validate data in a SAS data set.
• Calculate summary statistics and multilevel summaries using the MEANS procedure
• Enhance summary tables with options.
• Identify extreme and missing values with the UNIVARIATE procedure.
Enhance reports system user-defined formats, titles, footnotes and SAS System reporting options.
• Use PROC FORMAT to define custom formats.
o VALUE statement
o CNTLIN= option
• Use the LABEL statement to define descriptive column headings.
• Control the use of column headings with the LABEL and SPLIT=options in Proc Print output.
Generate reports using ODS statements.
• Identify the Output Delivery System destinations.
• Create HTML, PDF, RTF, and files with ODS statements.
• Use the STYLE=option to specify a style template.
• Create files that can be viewed in Microsoft Excel.
Export data
• Create a simple raw data file by using the EXPORT procedure as an alternative to the DATA step.
• Export data to Microsoft Excel using the SAS/ACCESS XLSX engine.



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Disparities in Receipt of Eye exams amongst Medicare half B fee-for-service Beneficiaries With Diabetes β€” u.s., 2017 | A00-211 PDF Questions and Practice Test

abstract and creationIntroduction

about 30 million people within the united states have diabetes.* individuals with diabetes are at risk for imaginative and prescient loss from diabetic retinopathy and other eye ailments.[1] Diabetic retinopathy, essentially the most typical diabetes-related eye disorder, affects 29% of U.S. adults aged β‰₯forty years with diabetes[2] and is the leading reason for incident blindness amongst working-age adults.[1] it's brought about with the aid of chronically excessive blood glucose destructive blood vessels in the retina.† Annual dilated eye assessments are informed for humans with diabetes as a result of early detection and timely treatment of diabetic eye diseases can prevent irreversible vision lossΒ§,ΒΆ.[3,4] reports have documented prevalence of annual eye exams amongst U.S. adults with diabetes;[5,6] although, a scarcity of fresh state-level statistics limits identification of geographic disparities in adherence to this recommendation. Medicare claims from the 50 states, the District of Columbia (DC), Puerto Rico, and U.S. Virgin Islands (USVI) had been examined to examine the occurrence of eye checks in 2017 among beneficiaries with diabetes who were perpetually enrolled in part B payment-for-service assurance, which covers annual eye assessments for beneficiaries with diabetes.** This file additionally examines disparities, by means of state and race/ethnicity, in receipt of eye assessments. Nationally, 54.1% of beneficiaries with diabetes had an eye fixed examination in 2017. incidence ranged from 43.9% in Puerto Rico to sixty four.eight% in Rhode Island. Fewer than 50% of beneficiaries got an eye fixed examination in seven states (Alabama, Alaska, Kentucky, Louisiana, Nevada, West Virginia, and Wyoming) and Puerto Rico. Non-Hispanic white (white) beneficiaries had a higher occurrence of receiving an eye examination (55.6%) than did non-Hispanic blacks (blacks) (48.9%) and Hispanics (48.2%). barriers to receiving eye care (e.g., suboptimal scientific care coordination and referral, low health literacy, and lack of perceived want for care) could restrict Medicare beneficiaries' means to observe this preventive care suggestion. understanding and addressing these barriers could keep away from irreversible imaginative and prescient loss among folks with diabetes.

This evaluation changed into carried out the usage of one hundred% of the facilities for Medicare & Medicaid services research identifiable info but become restrained to claims for Medicare beneficiaries invariably enrolled partly B price-for-provider for all of 2017.†† half B covers outpatient functions, together with ophthalmologic features. This analysis includes Medicare beneficiaries aged β‰₯sixty five years, as well as those aged <sixty five years who qualify via incapacity or disease repute, in the 50 U.S. states, DC, Puerto Rico, and USVI. Analyses have been conducted the use of SAS business ebook (version 9.4; SAS Institute).

The outcome measure was the occurrence amongst Medicare part B charge-for-carrier beneficiaries with diabetes of receiving a watch test all the way through January–December 2017. Beneficiaries obtained a prognosis of diabetes if they had at the least one prognosis code (foreign Classification of illnesses, Tenth Revision) or method code (current Procedural Terminology [CPT] and Healthcare commonplace method Coding gadget) defined within the continual situations information Warehouse diabetes algorithm on at least one declare all through 2016–2017.§§ prevalence turned into calculated as the number of continuously enrolled beneficiaries with diabetes who had an eye fixed test declare in 2017 divided by way of the variety of continuously enrolled beneficiaries with diabetes in that year. Eye exams had been defined the use of CPT codes 92002, 92004, 92014, and 92014 and different contrast and administration talk over with CPT codes if the provider taxonomy codes indicated an eye fixed care company.ΒΆΒΆ Unadjusted percentages are offered nationally and by state and race/ethnicity (white, black, Hispanic, Asian/Pacific Islander, American Indian/Alaska Native, and different). Age-standardized estimates, using direct standardization, were equivalent, and these statistics are not presented. Statistical testing became no longer performed as a result of these records signify 100% of Medicare beneficiaries who met the inclusion criteria.

among the 30,238,300 always enrolled Medicare half B charge-for-service beneficiaries in 2017, a complete of 8,341,000 (28%) had a diabetes prognosis. the bulk (seventy two.4%) of those beneficiaries with a diabetes analysis had been aged sixty five–84 years, with fewer aged 40–64 years (14.6%) or β‰₯85 years (12.1%). universal, seventy three.3% of these beneficiaries had been white, 13.0% have been black, 8.three% were Hispanic, 3.5% were Asian/Pacific Islander, 0.8% have been American Indian/Alaska Native, and 1.0% had been other racial/ethnic organizations.

Nationally, fifty four.1% of beneficiaries with diabetes had a watch test in 2017 (table). The incidence ranged from 43.9% in Puerto Rico to 64.8% in Rhode Island. In seven states (Alabama, Alaska, Kentucky, Louisiana, Nevada, West Virginia, and Wyoming) and Puerto Rico, <50% of beneficiaries with diabetes bought an eye fixed examination (desk) (figure 1). In nine states (Connecticut, Delaware, Hawaii, Iowa, Maine, Massachusetts, Nebraska, North Dakota, and Rhode Island) β‰₯60% of beneficiaries with diabetes had an eye fixed examination in 2017.

determine 1.

percent of Medicare part B fee-for-carrier beneficiaries with diabetes who had an eye exam, by using state β€” united states, 2017Abbreviations: DC = District of Columbia; PR = Puerto Rico; USVI = U.S. Virgin Islands.

Nationally, the incidence of getting a watch examination became reduce amongst Hispanic (forty eight.2%) and black (forty eight.9%) beneficiaries with diabetes than it was amongst whites (fifty five.6%). This was also followed in forty six states and DC. incidence become better amongst beneficiaries aged β‰₯85 years (58.6%) and sixty five–84 years (fifty six.9%) than amongst those aged forty–sixty four years (38.0%) or 18–39 years (31.7%) (figure 2).

figure 2.

percent of Medicare part B price-for-carrier beneficiaries with diabetes who had an eye fixed exam, by age neighborhood* β€” united states, 2017*facts for beneficiaries aged 0–17 years were suppressed on account of small demo size (≀a hundred).


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