# Presentation: 632979

## Access to Confidential Data for Statistical Analysis

- Access to Confidential Data for Statistical Analysis

**Kenneth Harris, Director of Research Data Center**

## National Center for Health Statistics (NCHS)

- Despite the wide dissemination of its data through publications, CD-ROMs, etc., the inability to release files with, for instance, lower levels of geography, severely limits the utility of some data  for research, policy, and programmatic purposes and sets a boundary on one of the Center’s goals to increase its capacity to provide state and local area estimates.

## NCHS (cont.)

-  In pursuit of this goal and in response to the research community’s interest in restricted data, NCHS established the Research Data Center (RDC), a mechanism whereby researchers can access detailed data files in a secure environment, without jeopardizing  the confidentiality of the respondents.

## Research Data Center

- 	The NCHS Research Data Center, established in 1998,  is a facility at the NCHS headquarters in Hyattsville, Maryland, where researchers are granted access to restricted data files needed to complete approved projects.  Restricted data files may contain information, such as lower levels of geography, but do not contain direct identifiers (e.g., name or social security number).

## Data Restrictions

- 	Section 308 (d) of the Public Health Service Act and the NCHS Staff Confidentiality Manual do not permit the release of data that are either identified or identifiable to persons outside of NCHS.

## Data Restrictions (cont.)

- 	Identifiable data include not only direct identifiers such as name, social security number, etc.,  but also data that can serve to allow inferential identification of either individual or institutional respondents by a number of means.

## Data Restrictions (cont.)

- 	Research indicates that identifiability is greatly enhanced if geographic identifiers for state, county, census tract, block-group or block are released on public use files.

## Key Issues for Research Data Availability

_**CONFIDENTIALITY**_
- 	The dissemination of data in a manner that would allow public identification of the respondent or would in any way be harmful to him/her is prohibited and the data are immune from legal process.

## Key Issues for Research Data Availability (cont.)

_**DISCLOSURE**_
- 	Disclosure relates to inappropriate attribution of information to a data subject, whether an individual or an organization.  Disclosure occurs when a data subject is identified from a released file (identity disclosure), sensitive information about a data subject is revealed through the released file (attribute disclosure), or the released data make it possible to determine the value of some characteristic of an individual more accurately than otherwise would have been possible (inferential disclosure).

## Appendix I – Rules for the Release of Micro Data Files

- The data file must not contain any detailed
- 	information about the subject that could facilitate identification and that is not essential for research purposes (e.g., exact date of the subject’s birth).
- Geographic places that have fewer than 100,000 people are not to be identified on the data file.
- Characteristics of an area are not to appear on the data file if they would uniquely identify an area of less than 100,000 people.

## Appendix I – Rules for the Release of Micro Data Files (cont.)

- Information on the drawing of the sample which might assist in identifying a data subject must not be released outside the Center.  Thus, the identities of primary sampling units are not to be made available outside the Center.
- Before any new or revised micro data files are published, they, together with their full documentation, must be approved for publication by the NCHS Director or Deputy Director.
- A micro data file containing confidential data on unidentified individuals or facilities may not be released to any person or organization outside NCHS until that person, or a responsible representative of that organization, has first signed the statement on the Order Form, whereby he gives assurance that the data provided will be used only for statistical reporting or research purposes.

## Why NCHS Does Not Release Files With Lower Levels of Geography

- Research suggests that in the case of personal surveys nine commonly collected variables result in the table below.

| Population of
Geopolitical Area | Percent of Sample 
Identifiable |
| --- | --- |
| 25,000 | 24 |
| 50,000 | 20 |
| 100,000 | 14 |
| 200,000 | 8 |
| 300,000 | 5 |
| 400,000 | 4 |
| 500,000 | 3 |

## Why NCHS Does Not Release Files With Lower Levels of Geography (cont.)

- Notes:  A geopolitical area may be a county, 	   city, town, or other place with well-		   defined boundaries.
-       In this case, identification refers to _***certainty***_* *identification.

## How Does RDC Operate?

- On-Site Access
- Remote Access
- Staff Assisted Analytical Session

## User Procedures

- To gain access to NCHS restricted data through
- either method, user must:
- Submit a research proposal.
- An advisory and proposal review committee receives, reviews, and approves researcher proposals
- Proposals are evaluated primarily on the confidentiality disclosure risk.
- Scientific merit is _not_ an evaluation criteria.
- Sign an affidavit of confidentiality and promise not to use any method to attempt to identify respondents.

## User Procedures (cont.)

- Not take any materials or equipment into RDC unless approved by RDC staff.
- Submit data files to be merged onto NCHS data ahead of time – _all_ merging is done by RDC staff.
- Subject all output and/or materials removed from the RDC to a disclosure limitation review.
- May not remove any NCHS restricted data files nor linked data files.

## Researcher Affidavit of Confidentiality

- I certify that no confidential data or information viewed or otherwise obtained while I am a researcher in the  National Center for Health Statistics (NCHS), Research Data Center (RDC) will be removed from NCHS.   Further, I understand that NCHS will perform a disclosure review and must provide approval to me before I remove any data from the RDC, whether it be in electronic or paper form.  I  acknowledge NCHS Confidentiality Statute, 308(d) of the Public Health Service Act stated below and fully understand my legal obligations to NCHS to protect all confidential data.  Further I understand any violation I may perform is punishable under 18 United States Code (USC), 1001 which carries a fine of up to $10,000 or up to 5 years in prison.

## Researcher Affidavit of Confidentiality (cont.)

**NCHS 308(d) Confidentiality Statute -** No information, if an establishment or person supplying the information or described in it is identified, obtained in the course of activities undertaken or supported under section 304, 305, 306, 307, or 309 may be used for any purpose other than the purpose for which it was supplied  unless such establishment or person has consented to its use for such other purpose and in the case of information obtained in the course of health statistical or epidemiological activities under section 304 or 306, such information may not be published or released in other form if the particular establishment or person supplying the information or described in it is identifiable unless such establishment or person has consented to its publication or release in other form.

## Researcher Affidavit of Confidentiality (cont.)

**18 United States Code, 1001 -** Deliberately making a false statement in any matter within the jurisdiction of any Department or Agency of the Federal Government violates 18 USC 1001 and is punishable by a fine of up to $10,000 or up to 5 years in prison.
- ____________________           _______________      Researcher’s Signature		 Date
- ____________________           _______________
- NCHS Witness			 Date

## Can Researcher Merge his/her Data with NCHS ?

**  **Must Interact with RDC staff to ensure
-   that their data can be merged with the
-    NCHS data.
-   User-supplied data will be merged with
-    NCHS data by RDC staff only.
-  The NCHS RDC policy states that merged
-    and user-supplied data will not be made
-    available for analysis to anyone without
-    the written consent of the user.

## The Cost per Project

_**On Site**_
_**$200 per day (2 day minimum)**_
_**Remote Access**_
-   NSFG-CDF = $500/ year
-   NHIS-polio = $500/ year
-  NHIS Linked Mort. File = $250/Month
- NHANES Linked Mort. File  = $250/Month

## The Cost per Project (cont.)

-  Files <= 130k records = $500 per month
-   Files > 130k records = $1000 per month
- _Staff Assisted   Variable_
_**File Construction and Setup**_
- 	For Mortality Files = $250 per day
- 	For all Other Files = $500 per day

## Do Doctors perform “defensive Cesareans”?

**Overview**:  This topic re-examined the issues of “defensive medicine” and state reforms designed to limit malpractice risk on the use of cesarean section delivery.
**NCHS Data Used**:  National Hospital Discharge Survey 							(NHDS)
**Years of Data Used**:   1980 through 1992, inclusive.
**User’s Data Merged with NCHS?**		Yes
**Method of Access to NCHS Data:**  Remote and
- 							On-site Access
**Statistical Software Used:**  SAS

## Economic Model to Explain the Incidence of Sexual Activity, Contraceptive Use, STD, and Pregnancy Among Teenage Girls.

**Overview**:  National Survey of Family Growth Data provide extensive socio-demographic information and reports of the sexual histories of these women.  Researcher focused on the effects of a number of policies measured at the state-level.  These included:
- Parental notification of consent laws.
- Medicaid funding of abortions.
- Welfare generosity.
**NCHS Data Used**:  National Survey of Family Growth (NSFG)
**User’s Data Merged with NCHS?  **Yes
**Method of Access to NCHS Data:**  Remote Access
**Statistical Software Used:**  SAS

## Nursing Home Admission and Payment Source?

**Overview**: This project tested if patients with Medicare were being discriminated against because their reimbursement rate was significantly below the private pay rate for nursing homes.
**NCHS Data Used**: National Nursing Home Survey 							(NNHS)
**Years of Data Used**:  1985, 1995, and 1997
**User’s Data Merged with NCHS?  **No
**Method of Access to NCHS Data:  **Remote Access
**Statistical Software Used:** SAS

## Hardware and Software

- All RDC hardware and software are standard.
- Hardware
- 		Pentium IV computers with Windows 2000
- Software
- 		SAS (only language on ANDRE)
- 		Sudaan
**	****	**Fortran
- 		HLM
**	****	**Stata
- 		Limdep
- 		text editors/viewers
- Onsite workstations do NOT have email or internet access
- Only access to printer is through RDC staff

## Record Linkage for Epidemiologic Research: Accessing Linked data at the NCHS Research Data Center

- Christine S. Cox
- NCHS Data Users Conference
- July 12, 2006

## What is Record Linkage?

**Administrative records**

**Linked Data File**

**NCHS Surveys**

**What is Record Linkage?**

**Notes:**
Record (or data) linkage refers to the bringing together the information from two or more sources that are believed to belong the same entity or unit, e.g. person, business name, etc. 

## NCHS Linked Data: Major Activities

- Mortality
- National Death Index
- Health Care Utilization and Costs
- Medicare Data
- Retirement and Disability
- Social Security Data

**Notes:**
NCHS data linkage activities include linkages of NCHS population based surveys to
Mortality
Health care utilization and costs from Medicare
Retirement and disability information from SSA

## NCHS Linked Data: Mortality 

- Eligibility status
- Assigned vital status
- Date of death
- Age at death
- Underlying and multiple causes of death
- Adjusted sample weights

## Research Potential of Linked Mortality Data

**Living and Dying in the USA: **Behavioral, Health, and Social Differentials of Adult Mortality
- RG Rogers, CB Nam, RA Hummer

**A Semiparametric Analysis of the Body Mass Index’s Relationship to Mortality**
- JT Gronniger

**The Income-Associated Burden of Disease in the United States **
- P Muennig, P Franks, H Jia, E Lubetkin and MR Gold

**Excess Deaths Associated with Underweight, Overweight, and Obesity**KM Flegal, BI Graubard, DF Williamson; MH Gail*JAMA.* 2005;293:1861-1867.

**Notes:**
Additional examples of the types of research that can be conducted include
Effects of diabetes and hypertension on obesity-related mortality
Alcohol consumption and risk of mortality from accidental injury
Long term survival in the elderly after traumatic injury
Mortality patterns among Hispanics: the mortality paradox

## NCHS Linked Data: Medicare

- Medicare entitlement and health care utilization and payment data for 1991-2000
- Denominator file
- MEDPAR Inpatient hospitalization
- MEDPAR Skilled nursing facility
- Hospital outpatient
- Home Health Care
- Hospice
- Carrier (physician/supplier Part B file)
- Durable Medical Equipment

## Research Potential ofLinked Medicare Data

- Examine risk factors for health conditions
- Examine reliability of survey data
- Examine survey report of disability with program participation eligibility criteria
- Compare survey reported health conditions to claims records
- Examine disparities in Medicare service utilization

**Notes:**
Bullet 1 – for example, you could use survey data to identify risk factors for hip fractures, strokes, other cardiovacular events, cancer surgery, etc. identified in the Medicare claims data

## NCHS Linked Data: Retirement/Disability

- Social Security data from Retirement, Survivors, and Disability Insurance (RSDI) and Supplemental Security Insurance (SSI) programs
- Master Beneficiary Record (MBR)
- 1962-2003
- Payment History Update System (PHUS)
- 1984-2003
- Supplemental Security Record (SSR)
- 1974-2003

**Notes:**
The SSA is responsible for administrating two entitlement programs designed to address the long-range problem of economic security for aged, disabled, or poor individuals.  The retirement, survivors, and disability social insurance program (RSDI), also known as Title I, provides monthly benefits to retired or disabled workers or their surviving qualified spouses and children and is the largest income-maintenance program in the United States.  To qualify for Social Security, a person must be insured for benefits, which is obtained by acquiring work history credits – or quarters of coverage – from earnings during employment.  For purposes of disability entitlement, disability is defined as “the inability to engage in any substantial gainful activity by reason of any medically determinable physical or mental impairment that can be expected to result in death or that has lasted or can be expected to last for a continuous period of not less than 12 months.” <citation or web link>  In 2004, more than 47 million Americans received approximately $492 billion in Social Security RSDI benefits.  The SSA also administers the Supplemental Security Income (SSI) program, known as Title XVI, which is a needs-based program designed to help aged, blind, and disabled people, who have little or no income, by providing cash assistance to meet basic needs such as food, clothing, and housing.  As of 2005, over 7.1 million people were receiving federally-administered SSI payments.  More information on SSA programs can be found at www.ssa.gov. 

NCHS surveys were linked to the SSA Numident file (a database of everyone issued a SSN).  For those that matched and had applied or are/were receiving social security benefits, information is available from the following files: MBR, PHUS, and SSR.

## Research Potential of Linked Social Security Data

- Examine reliability of survey information for SSA program participation and benefits
- Compare the health characteristics of those who take early (age 62) Social Security benefits to those who postpone benefits
- Policy analysis using validated survey data
- Predicting the number of people who will become disabled based upon survey reported health conditions
- Determining whether current disability entitlement funding levels will be adequate as the population ages

**Notes:**
Bullet 1: Compare survey reported disabilities and SSI/SSDI program participation to SSA data on eligibility and benefit receipts

## Summary NCHS Data Linkage

**www.cdc.gov/nchs/r&d/nchs_datalinkage/data_linkage_activities.htm**

**Notes:**
You can find out about all our Data Linkage Activities from our website.

## Why can’t you just give me the data?

- NCHS does not “own” the linked administrative data
- NCHS data confidentiality rules prohibit the release of potentially identifiable data – special considerations concerning the protection of linked data
- The RDC is the only option for access for now....

## Overview: Data Access Procedures

- Proposal Requirements
- Access Methods
- Helpful Tips
- Where to get help?

## Proposal Requirements

- Proposal is evaluated by review committee
- Review criteria
- Scientific and technical feasibility
- Availability of RDC resources
- Disclosure risk for restricted information
- The extent to which project is in accordance with the mission of NCHS
- Special note: NCHS does not try to determine if proposals are duplicative

## Proposal Requirements

- Cover letter
- Project title
- Abstract (maximum 300 words summarizing project)
- Full contact information
- Institutional affiliation
- Mail address, phone, email
- Dates of proposed time at RDC (or indication of using remote access)
- Source of funding for proposed research

## Proposal Requirements

- Study background
- Key study questions or hypotheses
- Public health benefits
- Methods
- Analytic approach and statistical methods
- Statistical software requirements
- Description of intended output for nondisclosure review, e.g.
- Table shells
- Model equations
- Test statistics that researcher plans to remove from RDC

## Proposal Requirements

- Explanation of why restricted data are needed, e.g. describe why publicly available data are insufficient
- Summary of data requirements to be included in analytic file
- Identification of sample
- Identification of variables
- Description of additional data to be supplied by researcher to be merged with NCHS or other data source (must clearly identify source of other data)

## Proposal Requirements: Appendices

- Current *Curriculum Vitae* or resume for each investigator
- Data dictionary – complete listing of specific data requested and its source(s) and indicate if public use or restricted access variables
- specific files and years
- sample
- variables (dependent, independent, matching/linking)

## Proposal Requirements: Appendices

- For remote-access applicants
- Description of the computer and email system to be used to receive output
- Security provisions for the computer and email systems
- For students
- Letter from department chair or academic advisor stating that student is working under the direction of the department

## Overview: RDC Data Access Procedures

- Proposal Requirements
- Access Methods
- Helpful Tips
- Where to get help?

## Access Methods

- Once approved, three methods to access restricted data
- on-site - use local computing resources in the NCHS RDC, Hyattsville, MD
- remote – submit programs electronically to be executed in the RDC with output returned by email
- staff assisted – RDC staff provide on-site programming for off-site approved researchers
- For all methods of access, restricted data files remain in RDC and output is inspected for disclosure violations

## On-Site Access

- RDC staff constructs necessary data files, including merged user data
- Most statistical packages available with sufficient lead time
- Output subject to disclosure review
- Open only during normal working hours

**Notes:**
On site:
SAS, Stata, SUDAAN, LIMDEP, HLM, SPSS, and Watcom Fortran 77 are currently available (other software can be made available with sufficient lead time)

Remote access:   SAS
Expect to have SUDAAN and STATA available for remote access by end of 2006 (SUDAAN maybe sooner) 

## Remote Access Method

- RDC staff constructs necessary data files, including merged user data
- SAS programs only (certain procedures and functions not allowed) – additional software options expected
- Both submitted programs and output undergo a programmed disclosure limitation review

## RDC Staff-assisted Programming Method

- Subcontract with the RDC staff to perform programming tasks
- Useful for those planning to use statistical software not available for the remote system and who are not able to travel to the RDC facility
- Cost is estimated for each research project

## Overview: RDC Data Access Procedures

- Proposal Requirements
- Access Methods
- Helpful Tips
- Where to get help?

## RDC Helpful Tips

- Be clear about research and data requirements (helps to determine feasibility of project)
- Clearly identify the sample to be included in the analytic file
- Provide data dictionaries for both
- Public use data
- Restricted data
- Provide examples of expected output

## Overview: RDC Data Access Procedures

- Proposal Requirements
- Access Methods
- Helpful Tips
- Where to get help?

## Visit the RDC at: 

- Visit the RDC at:
- _[www.cdc.gov/nchs/r&d/rdc.htm](http://www.cdc.gov/nchs/r&d/rdc.htm)__ or _email: rdca@cdc.gov

## LINKED DATA, CONTEXTUAL DATA, and GEO-CODINGON-SITE and STAFF-ASSISTED DATA ACCESS

**LINKED DATA, CONTEXTUAL DATA, and GEO-CODING****ON-SITE and STAFF-ASSISTED DATA ACCESS**

**Christopher  Rogers**
**Research Data Center**
**cor2@cdc.gov**

## Why Link Data Sets?

- Improve modeling and make use of existing data.
- Compensate for increased difficulties taking surveys.
- Open your mind.
-     	Common Example:
- 		Economic variables versus Ethnic variables

## Historical Trends

- More linking of scientific data sets between government agencies. Confidential Information Protection and Statistical Efficiency Act of 2002 (**CIPSEA.**)
- Confused political and social situation in US.

## Quality NCHS Resources

- Linked Birth and Infant Death Data with Fetal Death Data.
- Geo-coded NHIS 1986-2003 (2004-2005).
- Geo-coded NHANES III.
- Cycles 4, 5, and 6 NSFG Contextual Data.
- Linked Data Sets described earlier.

## Linked Birth and Infant Death

- Designed to study factors in infant death.
- Links birth and death certificates for deaths under one year of age. Includes fetal deaths for 1995-1997
- Years:  1983-1991 and 1995-1997
- Numerator File (for deceased children):  Parental information and behavior, prenatal care, infant health variables, demographics, cause of death.
- Denominator File (for control group): Parental information and behavior, prenatal heath, infant health, demographics.
- Fetal Death Data:  1995-1997
- Restricted Data: County/City of mother’s residence or County of child’s birth or death when under 250,000.  100,000 starting 1989.

## Data Example

- From the Division of Vital Statistics.  Proposals or questions can go either to the RDC or the DVS.
- Fetal Death Data portion.  Given 1989-1999.
- Linked to county level contextual data.
- Goal to model fetal death with emphasis on ground water quality.  Estimates death rates for each county.

## Geo-Coded NHIS

- National Health Interview Survey.  RDC has access to files from 1963 to present.  Previously geo-coded households for 1986-1994.  Recently geo-coded by RDC from 1995-2003.  2004-2005 coding in progress.
- State (2 digits), County (3 digits), Tract (6 digits), Block Group (1 digit), and Block (3-4 digits) levels.   Households coded to 1990 and 2000 Censuses.

## Geo-Coded NHANES III

- NHANES III is also linked to NDI Mortality data.
- NHANES III has been geo-coded twice. The RDC has done it at the same level of detail as NHIS.
- Continuous NHANES has not been geo-coded yet.
- Example:  Large project with neighborhood, economic, ethnic, and individual medical and behavioral variables.  Multi-level models.

## NSFG Contextual Data

- Contextual variables available with Cycles 4, 5, and 6.  Supplied for each individual in sample.
- Cycle 6: 1054 contextual variables at the state, county, tract, and block group levels.  For respondent addresses in 2000 and 2002.
- Contextual data include both economic and demographic characteristics of locations.  Easily merged by case ID to individual characteristics, behaviors, and histories.

## Simple NSFG Example

- A simple example relating economics on state level, ethnicity, and behavior, but not using contextual variables.
- Treatment States given waiver to offer more family planning services (FPS).
- Questions:
- FPS effects on behavior
- FPS effect on pregnancy rates
- Differential impacts across demographic subgroups?

## Change of Topic: Accessing Data 

- On-site access to data at the RDC in Hyattsville.
- Staff-assisted remote access to data via e-mail.
- Researchers often use both types of access.
- Potential Designated Agent status.  (CIPSEA)
- The RDC has put many resources into automated remote access.

## On-Site Access

- Rules in 24 page file *GuidelinesRDC11-8-05.pdf* available on-line.
- The RDC and NCHS surveys have knowledgeable professional staffs that review proposals carefully. Clients can only remove what has been approved.  Checked by staff.
- Exploratory Data Analysis. If needed, ask.  Recent example:  Checking general shapes of variables for model validity.  OKed by survey.
- Modeling needs.  Recent example:  Nested randomized geo-codes.
- Estimation problems.  Example: Single PSU in a Stratum.

## Staff-Assisted Remote Access

- Analysis done through a particular staff member.  Usually efficient, but could be very busy.
- Staff member determines costs based on time.
- Staff usually not asked to do much programming.
- Staff creates data, runs e-mailed programs, checks, and returns output to researcher.
- Staff can do exploratory analysis, if needed.
- Staff can help check modeling problems.
- Commonly done after on-site visit.

## Our Mission

- The RDC has a professional staff dedicated to helping researchers uncover knowledge and advance understanding.

## Remote Access System

**Vijay Gambhir**

## Remote Access System

- Envisioned as an integral Part of RDC
- Pre – onsite usage
- Post – onsite usage
- Super store/ Convenience store

## Basics of Remote Access System

- Object oriented, event driven system based upon the principles of distributed computing
- About two years of development efforts
- Set of applications called in service by resident component
- Advanced pattern recognition techniques

**Notes:**
In 1996 Asked to undertake an RD effort for the creation of Remote access system.  Eventually we came up with our current system for Analytic data research by email

## Analytic Data Research by Email (ANDRE)

- NCHS has been providing remote data access to researchers through ANDRE since April 1998.
- In the past five years, ANDRE has served 45 different data analysts and executed over 9,500 SAS programs for their research programs.

## Main Features of ANDRE

- Completely automated system
- Operates round the clock
- 	without any human intervention
- Registered subscribers only
- Proposals already reviewed and approved
- Have an agreement with NCHS/RDC
- Unlimited Access during the subscription period

## Data Requests

**Data Requests**

- Registered user can submit data requests by email from anywhere and at any time.
- Results of the data request released to a specified email address that has been certified as secure by the subscriber and approved by NCHS/RDC.

## Authentication

- Multi-levels of system security:
- Submission syntax
- User id
- Password
- Email/code word
- Package
- Path info

## Data Request Analysis

- Compliance with the disclosure limitation constraints of NCHS
- Integrity of the system
- Resource constraints (CPU time & Storage requirements)
- Protection of ANDRE’s work environment

**Notes:**
Disclosure limitation: Forbidden words

## Prevention of Direct Disclosure

- Cleaning up of the Log File
- Categorization of SAS commands/words
- Forbidden Commands
- Modifications to the Commands
- Output suppression

## Sample:  Original Log

- 1    options nocenter;
- 2    Data one;
- 3    Infile 'd:\nchs\respnd95.dat' lrecl=13064;
- 4    Input
- 5    TODAYSPG 6847-6847
- 6    CONSTAT1 11934-11935
- 7    CONSTAT2 11936-11937
- 8    CONSTAT3 11938-11939
- 9    CONSTAT4 11940-11941
- 10   SEX1MTHD 11945-11946
- 11   POST_WT  12350-12359;
- 12   if constat1 = 'ab' then vjvar=1; else vjvar = 2;
- 13   WGT1000=POST_WT/1000;
- 14   title 'NSFG cycle 1995';
- NOTE: Character values have been converted to numeric values at the places given by: (Line):(Column).
-       12:15
- NOTE: The infile 'd:\nchs\respnd95.dat' is:
-       File Name=d:\nchs\respnd95.dat,
-       RECFM=V,LRECL=13064
**NOTE: Invalid numeric data, 'ab' , at line 12 column 15. **
**RULE:     ----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0**
**1         1000000111260837511521 1                                    1050  12         106921124112411189**
**     ****101                                                                              2**
**     ****201        19211059110611197**
**	****	****	****......**

## Sample:  Original Log (cont.)

- 				......
-  **12901       11232521101 05267213103033921811931011103  01030000000321120000392702210611511200403 1344  1316**
**   ****13001  622501001006034 **
**TODAYSPG=1 CONSTAT1=5 CONSTAT2=88 CONSTAT3=88 CONSTAT4=88 SEX1MTHD=1 POST_WT=2545.7569 vjvar=2 WGT1000=2.5457569 _ERROR_=1**
**_N_=20**
- NOTE: 10847 records were read from the infile 'd:\nchs\respnd95.dat'.
-       The minimum record length was 13064.
-       The maximum record length was 13064.
- NOTE: The data set WORK.ONE has 10847 observations and 9 variables.
- NOTE: DATA statement used:
-       real time           39.88 seconds
-       cpu time            12.10 seconds
- 15   proc freq;
- 16   tables CONSTAT1 vjvar;
- 17   run;
- NOTE: There were 10847 observations read from the data set WORK.ONE.
- NOTE: PROCEDURE FREQ used:
-       real time           0.49 seconds
-       cpu time            0.04 seconds

## Sample: Cleaned Log

- 1    options nocenter;
- 2    Data one;
- 3    Infile 'd:\nchs\respnd95.dat' lrecl=13064;
- 4    Input
- 5    TODAYSPG 6847-6847
- 6    CONSTAT1 11934-11935
- 7    CONSTAT2 11936-11937
- 8    CONSTAT3 11938-11939
- 9    CONSTAT4 11940-11941
- 10   SEX1MTHD 11945-11946
- 11   POST_WT  12350-12359;
- 12   if constat1 = 'ab' then vjvar=1; else vjvar = 2;
- 13   WGT1000=POST_WT/1000;
- 14   title 'NSFG cycle 1995';
- NOTE: Character values have been converted to numeric values at the places given by: (Line):(Column).
-       12:15
- NOTE: The infile 'd:\nchs\respnd95.dat' is:
-       File Name=d:\nchs\respnd95.dat,
-       RECFM=V,LRECL=13064
**NOTE: Invalid numeric data, 'ab' , at line 12 column 15. **

## Sample: Cleaned Log (cont.)

- NOTE: 10847 records were read from the infile 'd:\nchs\respnd95.dat'.
-       The minimum record length was 13064.
-       The maximum record length was 13064.
- NOTE: The data set WORK.ONE has 10847 observations and 9 variables.
- NOTE: DATA statement used:
-       real time           39.88 seconds
-       cpu time            12.10 seconds
- 15   proc freq;
- 16   tables CONSTAT1 vjvar;
- 17   run;
- NOTE: There were 10847 observations read from the data set WORK.ONE.
- NOTE: PROCEDURE FREQ used:
-       real time           0.49 seconds
-       cpu time            0.04 seconds

## Forbidden Commands

- Commands That Pose Unacceptable Disclosure Risks
- 					OR
- Disallowed to Protect Integrity/Internal Environment of ANDRE
**Add****	****	**** firstobs****	****     report****	****	**** iml**
**Print****	****	**** first.****	****	****     Pctn****	****	**** nofreq**
**Obs****	****	**** last.****	****	****     Pctsum****	****	**** nocum**
**Firstobs****	**** nocol****	****     tabulate****	**** editor**
**Browse****	**** summary****	****     list****	****	**** put**

## Commands Modification

- Modify user’s program to enforce  restrictions on options allowed with certain SAS procedures to prevent objectionable info appearing in the output
- 	*PROC MEANS  n mean std;*

## Output Suppression

- Wiping out of extreme values from the output of Proc Univariate.
- Suppressing complete output line (Procs Means, corr, Univariate, etc) where sample size less than the minimum acceptable value.

## Proc Means Suppression

- The MEANS Procedure
- Variable    Label                                          N            Mean         Std Dev
- --------------------------------------------------------------------------------------------
- EXPEND_R    Current expend/pupil in public schl/1000    5424       5.0830820       1.3958710
-                                          ***** Values Suppressed *****
- RPUB87      exp. for contr. serv. and supplies 1997$    5424     23472052.60     18806802.86
- RPUB92      exp. for contr. serv. and supplies 1997$    5424     34800922.98     30481634.59
- PRGPRO      Coordinated Pregnancy Prevention Program    1708       0.0679157       0.2516749
- HIVED       HIV/AIDS Education                          1708       3.5146370       0.8044378
-                                          ***** Values Suppressed *****
- PRGPRO87    Coordinated Pregnancy Prevention Program    5424       0.0540192       0.2260764
- HIVED87     HIV/AIDS Education                          5424       3.4968658       0.8008324
- WT_PER15    % Wt females aged 15-19/total 15-19         5424       0.7279681       0.1265796
- BK_PER15    % Bk females aged 15-19/total 15-19         5424       0.1409869       0.0932332
- HS_PER15    % Hs females aged 15-19/total 15-19         5424       0.0962413       0.1055191
- TEENMMC2    Teenmom by cohort (1,2,3r)                  1201       1.7119067       0.7715351
- C18_2_1S    R in C2 (vs 1) at 18-19 endpt (1,2)         1770       1.5248588       0.4995228
- TM2_1S18    R tnmm in Coh 2 (vs 1)-age 18 @ ext          358       1.4804469       0.5003168
- AGE_12      Date R = 12 in century months               6450     979.5613953      69.3124265
- STRTST      IA5 Date R started living in current sta    3870         1132.55     753.2066507
- BDAYCENM    R date of birth                             6450     835.5613953      69.3124265
- RAVPAY95    real av. an. pay 95 dollars                 5424        26933.93         2826.80
- PERCAFDC    percent of households receiving AFDC        5424       0.0422254       0.0127307
- SALARY      teacher salaries real 96-97$$$              5424        35338.66         5729.11
- --------------------------------------------------------------------------------------------

## Proc Univariate OutputUnsuppressed

-   The SAS System                                   9
-                                                        14:09 Sunday, October 24, 1999
-                                 Univariate Procedure
- Variable=AVHRATET
-                  Moments                                 Quantiles(Def=5)
-  N              2283  Sum Wgts       2283     100% Max  -0.25314       99%  -1.62008
-  Mean       -4.66219  Sum        -10643.8      75% Q3   -3.56179       95%  -2.37588
-  Std Dev    1.892017  Variance    3.57973      50% Med  -4.50491       90%  -2.79152
-  Skewness   -2.11919  Kurtosis   6.892929      25% Q1   -5.30374       10%  -6.07639
-  USS        57792.36  CSS        8168.944       0% Min  -13.5463        5%  -7.19645
-  CV         -40.5821  Std Mean   0.039598                               1%  -12.7402
-  T:Mean=0   -117.738  Pr>|T|       0.0001     Range     13.29321
-  Num ^= 0       2283  Num > 0           0     Q3-Q1     1.741949
-  M(Sign)     -1141.5  Pr>=|M|      0.0001     Mode      -13.5463
-  Sgn Rank   -1303593  Pr>=|S|      0.0001
-                                       Extremes
-                          Lowest    Obs     Highest    Obs
-                        -13.5463(    1547) -0.90519(     649)
-                        -13.5397(    1836) -0.81756(    1094)
-                        -13.4637(    2084) -0.76928(    1739)
-                        -13.4413(    1127)  -0.5907(      21)
-                        -13.4402(    1088) -0.25314(     400)

## Proc Univariate OutputSuppressed

- The SAS System                                   9
-                                                        14:09 Sunday, October 24, 1999
-                                 Univariate Procedure
- Variable=AVHRATET
-                  Moments                                 Quantiles(Def=5)
-  N              2283  Sum Wgts       2283     100% Max  -0.25314       99%  -1.62008
-  Mean       -4.66219  Sum        -10643.8      75% Q3   -3.56179       95%  -2.37588
-  Std Dev    1.892017  Variance    3.57973      50% Med  -4.50491       90%  -2.79152
-  Skewness   -2.11919  Kurtosis   6.892929      25% Q1   -5.30374       10%  -6.07639
-  USS        57792.36  CSS        8168.944       0% Min  -13.5463        5%  -7.19645
-  CV         -40.5821  Std Mean   0.039598                               1%  -12.7402
-  T:Mean=0   -117.738  Pr>|T|       0.0001     Range     13.29321
-  Num ^= 0       2283  Num > 0           0     Q3-Q1     1.741949
-  M(Sign)     -1141.5  Pr>=|M|      0.0001     Mode      -13.5463
-  Sgn Rank   -1303593  Pr>=|S|      0.0001

## Proc Univariate OutputSuppressed (sample size = 1)

-                                 Univariate Procedure
- Variable=FREQ          (sum) freq
-                  Moments                                 Quantiles(Def=5)
-  			Serious Disclosure limitation Violations
- 			Values too low to release
- 			Output of Proc Univariate withheld

## Proc Freq Suppression (One-Way Tables)

- Suppress at least two consecutive rows to prevent derivation of suppressed values from cumulative totals.
- Disallow single row output.

## One-Way Freq TableSuppressed

- 			Cumulative  	    Cumulative
-   LOGRNTOPAT    Frequency     Percent     Frequency      Percent
- -----------------------------------------------------------------
**0.2277839309       ?????       ?????         ?????        ?????**
**0.2277839309       ?????       ?????         ?????        ?????**
- 0.2305236586           5        0.08          6429        97.99
-  0.231111721           5        0.08          6434        98.06
-  **0.232058915       ?????       ?????         ?????        ?????**
** ****0.232058915       ?????       ?????         ?????        ?????**
**0.2436220827       ?????       ?????         ?????        ?????**
**0.2436220827       ?????       ?????         ?????        ?????**
- 0.2498117984           6        0.09          6456        98.40
- 0.2504106777           6        0.09          6462        98.49
- 0.2513144283          18        0.27          6480        98.77
- 0.2595111955           6        0.09          6486        98.86
**0.2670627852       ?????       ?????         ?????        ?????**
**0.2670627852       ?????       ?????         ?????        ?????**
- 0.2736958305           5        0.08          6500        99.07
- 0.2814124594           5        0.08          6505        99.15
- 0.3022808719           6        0.09          6511        99.24
- 0.3364722366          10        0.15          6521        99.39

## One-Way Freq Tablesuppressed (cont.)

- 			Cumulative  	    Cumulative
-   LOGRNTOPAT    Frequency     Percent     Frequency      Percent
- -----------------------------------------------------------------
**0.3403258059       ?????       ?????         ?????        ?????**
**0.3403258059       ?????       ?????         ?????        ?????**
- 0.3715635564           6        0.09          6537        99.63
**0.3856624808       ?????       ?????         ?????        ?????**
**0.3856624808       ?????       ?????         ?????        ?????**
- 0.6931471806           6        0.09          6550        99.83
**1.2527629685       ?????       ?????         ?????        ?????**
**1.2527629685       ?????       ?????         ?????        ?????**
**1.2527629685       ?????       ?????         ?????        ?????**

## Proc Freq Suppression (Two-way Tables)

- Rows and columns totals preserved
- Cells with values less than the acceptable minimum are suppressed
- Additional suppressions to ensure that no row and no column has single suppression.
- Logical stitching of horizontal and vertical splits.

## Proc Freq:  Two-way Tables Suppression

-  TABLE OF FAMREL BY FAMSIZER
-                 FAMREL     FAMSIZER
-                 Frequency|
-                 Percent  |
-                 Row Pct  |
-                 Col Pct  |       2|       3|       4|       5|  Total
-                 ---------+--------+--------+--------+--------+
-                 3        |     94 |    388 |    792 |    533 |   2206
-                          |   3.97 |  16.40 |  33.47 |  22.53 |  93.24
-                          |   4.26 |  17.59 |  35.90 |  24.16 |
-                          |  98.95 |  96.28 |  96.12 |  94.34 |
-                 ---------+--------+--------+--------+--------+
-                 4        | ?????? |      9 |     22 |     27 |    104
-                          | ?????? |   0.38 |   0.93 |   1.14 |   4.40
-                          | ?????? |   8.65 |  21.15 |  25.96 |
-                          | ?????? |   2.23 |   2.67 |   4.78 |
-                 ---------+--------+--------+--------+--------+
-                 6        | ?????? |      6 |     10 |      5 |     56
-                          | ?????? |   0.25 |   0.42 |   0.21 |   2.37
-                          | ?????? |  10.71 |  17.86 |   8.93 |
-                          | ?????? |   1.49 |   1.21 |   0.88 |
-                 ---------+--------+--------+--------+--------+
-                 Total          95      403      824      565     2366
-                              4.02    17.03    34.83    23.88   100.00
-                 (Continued)

## Proc Freq:  Two-way Tables Suppression (Cont.)

-  checking frequencies                                4
-                                                           12:01 Thursday, May 6, 1999
-                              TABLE OF FAMREL BY FAMSIZER
-                 FAMREL     FAMSIZER
-                 Frequency|
-                 Percent  |
-                 Row Pct  |
-                 Col Pct  |       6|       7|       8|       9|  Total
-                 ---------+--------+--------+--------+--------+
-                 3        |    209 |     98 |     19 |     73 |   2206
-                          |   8.83 |   4.14 |   0.80 |   3.09 |  93.24
-                          |   9.47 |   4.44 |   0.86 |   3.31 |
-                          |  90.48 |  83.05 |  59.38 |  74.49 |
-                 ---------+--------+--------+--------+--------+
-                 4        |     13 |     10 | ?????? |     12 |    104
-                          |   0.55 |   0.42 | ?????? |   0.51 |   4.40
-                          |  12.50 |   9.62 | ?????? |  11.54 |
-                          |   5.63 |   8.47 | ?????? |  12.24 |
-                 ---------+--------+--------+--------+--------+
-                 6        |      9 |     10 | ?????? |     13 |     56
-                          |   0.38 |   0.42 | ?????? |   0.55 |   2.37
-                          |  16.07 |  17.86 | ?????? |  23.21 |
-                          |   3.90 |   8.47 | ?????? |  13.27 |
-                 ---------+--------+--------+--------+--------+
-                 Total         231      118       32       98     2366
-                              9.76     4.99     1.35     4.14   100.00

## Fully Automated and Expert system?

- Fully automated?
- Reboot to deal with memory leakage.
- Confidentiality Expert?  How reliable?
- As good as underlying algorithms.  Needs constant monitoring

**Notes:**
Monitoring needed by sr. Computer Scientist and Statestitians.

## Slide 97

## Slide 99

## Slide 100

## What is new?

- Improved and expanded hardware platform
- Two machines dedicated to heavy remote access usage
- Three additional machines dedicated to general remote access usage

## What is New?

- Sudaan now available to remote access users
- Proc Crosstab
-  Proc Rlogist
-  Proc Regress
- Proc Multilog
- Proc Survival

## What is new

- Proc Descript
- Other new Sudaan procedures will be made available shortly
- Plans to make Stata available through remote access

## What is new

- Web Component of ANDRE under construction.
- On-line scanning of users’ code
- Valuable research tools and information readily available to the users.

## Contact Information

- For general Questions/Comments
- Email: _[rdca@cdc.gov](mailto:rdca@cdc.gov)__ _	   Phone: (301) 458-4732
- For On-site Info:
- Email: _[Neb9@cdc.gov](mailto:Neb9@cdc.gov)__	__   Phone:  (301) 458-4097_
- For Remote Access Info:
- Email: _[vgambhir@cdc.gov](mailto:vgambhir@cdc.gov)__   Phone: (301) 458-4226_