VitalPro User's Guide - California ICD-9 Death Data (1989 - 1998)
Copyright 1998-2006 by Expert Health Data Programming, Inc.
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Vitalnet is a comprehensive, integrated system
for analyzing health data. California Epigram is the
Vitalnet module for analyzing California underlying cause mortality data.
The software is designed to be easily used without a user's guide.
However, many users learn better from a written text,
and all users will benefit from a overview
of what the software can do before using it.
This user's guide describes Epigram Professional Version
(VitalPro), which runs directly on a PC or LAN.
Web Versions of Vitalnet (VitalWeb) are described at the
www.ehdp.com web site.
Either Vitalnet system will greatly ease and speed your work.
This user's guide incorporates a tutorial.
Carrying out the tutorial will help you quickly become an expert at using Vitalnet.
All procedures you are expected to carry
out as part of the tutorial are highlighted by a different text appearance,
as shown in this example:
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Sample Tutorial Step - Press 'A' to add California as one area set.
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Here's how this user's guide is organized:
Chapter 1: Introduction -
Describes general characteristics of California
Epigram, access methods, and confidentiality requirements.
Chapter 2: Understanding Results -
Explains the organization and content of Epigram tables
(the results you get from using Epigram).
Shows and explains typical tables.
Chapter 3: Using the Epigram Interface -
Describes how to use Epigram
menus to select parameters, produce tables,
and save output, with examples.
Guides you step-by-step to produce your first table.
Outlines available menus.
Chapter 4: California Epigram Parameters -
Describes allowed selections for
age groups, area sets
(counties and health service areas),
ICD-9 sets (causes of death), race groups,
years, and other options.
Glossary -
Defines terms related to analyzing underlying cause mortality data and using
Epigram.
Citation for Epigram -
Expert Health Data Programming, Inc., California VitalPro User's Guide: Data
Warehouse Software for Analyzing California ICD-9 Death Data.
Renton, Washington. 1998-2006.
Browse
www.ehdp.com
for more information about the software or to contact EHDP.
Acknowledgements -
We gratefully acknowledge the cooperation and assistance of staff from the
California Department of Health Services
and other users.
Trademarks -
Birtha, Epidemic, Epigram, MedTrend, MultiCod, Oncogram, PopTrend, PregData, Vitalnet, VitalWeb, VitalPro, and
VitalWeb Wizard are trademarks of Expert Health Data Programming, Inc.
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Epigram makes it easy to analyze California underlying cause mortality data.
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Fast -
You get results in seconds or minutes. Depending on the analysis,
alternate methods could easily require hours to weeks to complete.
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Flexible -
A wide variety of tables may be produced.
Set rows and columns however you want.
Standard parameters may be
selected and combined as needed.
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Efficient -
You can make a whole series of tables with one keystroke
(multi-tables). Bar graphs allow for quick scanning for trends.
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Easy to use -
Operations are menu-driven.
Scrolling windows are used to select items from lists.
You don't need to know any special codes such as FIPS codes.
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Well-documented -
Although Epigram has been designed to be self-explanatory,
it also includes extensive on-line help.
Each menu has its own help screen.
Also, you may select from a list of on-line help topics,
providing advice on all topics related to Epigram.
The on-line help
complements the information included in this user's guide.
Useful on-line reference materials are also provided, such as
tables of standard populations used for age-adjustment.
Finally, all output is fully documented.
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Integrates with other software -
Tables may be saved to a log file in ASCII format for
subsequent editing and printing with any editor.
You may also save
Epigram tables in CSV format or as a dBASE III file,
for easy importing into data analysis, spreadsheet, mapping,
graphing or other presentation software.
Or, tables may be saved as HTML for display in a web browser.
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Epigram links and analyzes geographic, population, mortality, and ICD-9 data.
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Geographic information -
Epigram includes a database of California
counties and Health Service Areas,
linked to the
population and
underlying cause mortality data sets.
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Population data -
The population variables included within Epigram are
age, county of residence, race, sex, and year.
Population figures are used for
calculating
mortality
rates.
A separate Vitalnet interface, PopTrend,
analyzes population data for demographic
trends and to obtain
denominators for analyzing other data sets.
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Mortality data -
The Center for Health Statistics at the California Department of Health Services
provides all California underlying cause mortality data.
For an estimate on when the next data file will be loaded,
contact the Center.
Mortality variables within Epigram include
age of deceased, cause of death, county of residence, race, sex, and year.
Epigram analyzes by place of residence of the deceased.
For example, if a
Sacramento
resident died in a motor vehicle collision in
Los Angeles,
Epigram would classify the death under
Sacramento.
Standard mortality reports usually use place of residence
(the other system is "place of occurrence").
Epigram analyzes by underlying cause
(the cause which initiated the sequence of events leading to death).
For example, if a death certificate lists
rheumatoid arthritis, myocardial infarction (MI), and cardiac
arrhythmia secondary to MI, the underlying cause is MI.
Standard mortality reports usually use underlying cause.
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Data linking -
Four data sets (geographic data, population data, mortality data,
and ICD data) are linked with each other,
so the Epigram data warehouse is greater than
the sum of its parts.
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Both local and remote access are available for California Epigram.
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Local access (PC's and local area networks) -
If you are a California Department of Health Services
employee, you will typically use California Epigram
on a stand-alone IBM-compatible PC or laptop running any
version of Windows.
California Epigram may also be installed
from the DHS
local area networks.
Execution speed -
Epigram is fast.
Analyses that might otherwise take hours to weeks to set up and run
are done in seconds or minutes.
For those who access California Epigram on a PC network,
the program execution speed depends on the type of PC you
are using and the characteristics of the network you are working on.
The program has been tested to work well
on all IBM-compatible PC, and simply runs faster on faster computers.
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California Epigram users must comply with confidentiality requirements.
Confidentiality policy -
Your use of California Epigram indicates your agreement
to the following conditions:
You will not try to use California Epigram results nor
let anyone else use California Epigram results to
learn the identity of a reported death,
or for any purpose other than statistical analysis.
If you discover the identity of a reported death,
you will advise the Director of the Center for Health Statistics at the California Department of Health Services of the incident,
will safeguard or delete the information
that would identify the individual,
will make no use of the knowledge,
and will inform no one else of the discovered identity.
Cell suppression -
If you so desire, California Epigram can suppress cells that
have fewer than a user-defined number of deaths.
Cell suppression can increase the confidentiality
of written reports in some cases.
Cell suppression
is described more fully later.
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Review -
California Epigram is a powerful tool for
analyzing California underlying cause mortality data.
The program compares just about anything with anything,
and makes just about any kind of output table you want.
Chapter 1 explained how Epigram makes analyzing
underlying cause mortality data easy,
listed the linked data sets used by California Epigram,
described the access methods,
and spelled out the confidentiality policy.
What's in this chapter -
Before jumping in and using the program (Chapter 3),
it is advised to get a good understanding of the results of the program.
This chapter explains the organization and content
of Epigram tables (the results you get from using Epigram).
This chapter explains the four sections of a table:
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Header - Basic analysis parameters.
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Data section - Numerical results.
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Bar graphs - Graphical results.
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Footnotes - Other analysis parameters.
In addition, this chapter shows examples of
actual California Epigram tables,
to give you an idea of what is possible.
You are given an opportunity to practice interpreting sample tables.
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Each table has four parts: header, data section, bar graphs, footnotes.
Deaths
Tabulated by Age and Sex
Years: 1994
Place of Residence: Imperial, San Diego
ICD 496: Chronic Airway Obstruction, Not Elsewhere Classified
Age Male Female Total
-----------------------------------
Birth-39 1 1 2
40-59 15 16 31
60-79 221 191 412
80-99+ 154 154 308
-----------------------------------
Total 391 362 753
Horizontal Bar Graphs (X = 14.7 Deaths, x = 7.4):
Age Male Female
-------------------------------------------
Birth-39
40-59 X X
60-79 XXXXXXXXXXXXXXX XXXXXXXXXXXXX
80-99+ XXXXXXXXXXx XXXXXXXXXXx
-------------------------------------------
Analysis Footnotes:
Unique ID, for Keeping Track of Analyses: 413HKYZM
Output Produced: Fri Apr 13 11:12:58 2001, by California VitalNet
Deaths Classified By: ICD-9, Underlying Cause, Place of Residence
Mortality Data Source: DHS Office of Health Information and Research
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The header documents
the analysis.
The table analyzes deaths for 1994 for
Imperial and San Diego counties.
Deaths from chronic obstructive pulmonary disease (ICD 490-496) are counted.
The data section contains the results,
organized into columns and rows.
In this example, there is one column for each sex
and one row for each of four age groups.
Verify there were
16 deaths in women age 40-59,
391 deaths in males, and a total of
753 deaths. Each result, such as
16,
391, and
753, is called a "cell".
The horizontal bar graphs represent the results
in a simple graphical format.
In this example, each 'X' (big X) symbol
represents
14.7 deaths.
The bar graphs are optional.
The bar graphs clearly show that the great
majority of deaths from
chronic obstructive lung disease in the two
counties were in the 60-99+ age group.
The footnote documents
less important aspects of the analysis, such as
when the table was produced,
and assigns a unique ID to the table for future reference.
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Here is a sample table (footnotes omitted) for you to practice on.
Try interpreting the table by filling in the spaces below.
Then, check your answers with those on the next page.
Death Rate (per 100,000)
Tabulated by Area Set and Sex
Age: 20-39 Years
Years: 1991-1993
Place of Residence: Hlth Serv Area 2, Hlth Serv Area 8, Hlth Serv Area 12
ICD 042-044: Human Immunodeficiency Virus (HIV) Infection
Male Female Total
Area Set Rate, Deaths Rate, Deaths Rate, Deaths
-----------------------------------------------------------------
Hlth Serv Area 2 37.5 328 2.8 25 20.2 353
Hlth Serv Area 8 21.1 105 2.9 12 12.7 117
Hlth Serv Area 12 34.4 499 3.0 41 19.1 540
-----------------------------------------------------------------
Total 33.0 932 2.9 78 18.4 1,010
Horizontal Bar Graphs (X = 2.5 Deaths / 100,000, x = 1.25):
Male Female Total
Area Set Rate Rate Rate
--------------------------------------------------------------------
Hlth Serv Area 2 XXXXXXXXXXXXXXX X XXXXXXXX
Hlth Serv Area 8 XXXXXXXXx X XXXXX
Hlth Serv Area 12 XXXXXXXXXXXXXx X XXXXXXXx
--------------------------------------------------------------------
Total XXXXXXXXXXXXX X XXXXXXXx
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| Header | Basic result type (statistic): | ___________________ |
| | Years analyzed: | ___________________ |
| | Geographic areas analyzed: | ___________________ |
| | ICD groups analyzed: | ___________________ |
| | Age groups analyzed: | ___________________ |
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| Data Section | Rows variable: | ___________________ |
| | Columns variable: | ___________________ |
| | How many females died in HSA 8: | ___________________ |
| | Death rate for males in HSA 12: | ___________________ |
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| Bar Graphs | Death rate symbolized by big 'X': | ___________________ |
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Here is the same table, and a suggested interpretation.
If you had a problem, try reading through the answers again or
ask a local data analyst for help.
Death Rate (per 100,000)
Tabulated by Area Set and Sex
Age: 20-39 Years
Years: 1991-1993
Place of Residence: Hlth Serv Area 2, Hlth Serv Area 8, Hlth Serv Area 12
ICD 042-044: Human Immunodeficiency Virus (HIV) Infection
Male Female Total
Area Set Rate, Deaths Rate, Deaths Rate, Deaths
-----------------------------------------------------------------
Hlth Serv Area 2 37.5 328 2.8 25 20.2 353
Hlth Serv Area 8 21.1 105 2.9 12 12.7 117
Hlth Serv Area 12 34.4 499 3.0 41 19.1 540
-----------------------------------------------------------------
Total 33.0 932 2.9 78 18.4 1,010
Horizontal Bar Graphs (X = 2.5 Deaths / 100,000, x = 1.25):
Male Female Total
Area Set Rate Rate Rate
--------------------------------------------------------------------
Hlth Serv Area 2 XXXXXXXXXXXXXXX X XXXXXXXX
Hlth Serv Area 8 XXXXXXXXx X XXXXX
Hlth Serv Area 12 XXXXXXXXXXXXXx X XXXXXXXx
--------------------------------------------------------------------
Total XXXXXXXXXXXXX X XXXXXXXx
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| Header | Basic result type (statistic): | Death rate (per 100,000) |
| | Years analyzed: | 1991-1993 |
| | Geographic areas analyzed: | Health Service Areas 2, 8, 12 |
| | ICD groups analyzed: | ICD 42-44 (HIV/AIDS) |
| | Age groups analyzed: | 20-39 |
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| Data Section | Rows used in this example: | Row for each area set |
| | Columns used in this example: | Column for each sex |
| | How many females died in HSA 8: | 12 |
| | Death rate for males in HSA 12: | 34.4 per 100,000 |
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| Bar Graphs | Death rate symbolized by big 'X': | 2.5 per 100,000 |
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Here is another table. Fill in the spaces below.
Then, check your answers on the next page.
Age-Adjusted Death Rate (per 100,000)
Tabulated by Race-Ethnicity and Sex
Years: 1997-1998
Place of Residence: California
ICD 162: Cancer Of Trachea, Bronchus, Or Lung
Male Female Total
Race Rate, Deaths Rate, Deaths Rate, Deaths
---------------------------------------------------------
Am Indian 42.5 57 19.9 34 29.6 91
Asian/PI 41.9 976 19.6 568 29.4 1,544
Black 97.5 1,295 44.5 813 66.3 2,108
Hispanic 30.1 1,075 13.5 609 20.6 1,684
White 68.7 11,704 45.6 10,141 55.1 21,845
---------------------------------------------------------
Total 61.9 15,107 38.1 12,165 48.1 27,272
Horizontal Bar Graphs (X = 6.5 Deaths / 100,000, x = 3.25):
Male Female Total
Race Rate Rate Rate
------------------------------------------------------------
Am Indian XXXXXXx XXX XXXXx
Asian/PI XXXXXXx XXX XXXXx
Black XXXXXXXXXXXXXXX XXXXXXX XXXXXXXXXX
Hispanic XXXXx XX XXX
White XXXXXXXXXXx XXXXXXX XXXXXXXXx
------------------------------------------------------------
Total XXXXXXXXXx XXXXXX XXXXXXXx
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| Header | Basic result type (statistic): | ___________________ |
| | Years analyzed: | ___________________ |
| | Geographic areas analyzed: | ___________________ |
| | ICD groups analyzed: | ___________________ |
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| Data Section | Rows used in this example: | ___________________ |
| | Columns used in this example: | ___________________ |
| | How many Black males died: | ___________________ |
| | Death rate for Hispanic females: | ___________________ |
| | Overall death rate: | ___________________ |
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| Bar Graphs | Death rate symbolized by big 'X': | ___________________ |
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Here is the second table again, and our interpretation of the results.
Age-Adjusted Death Rate (per 100,000)
Tabulated by Race-Ethnicity and Sex
Years: 1997-1998
Place of Residence: California
ICD 162: Cancer Of Trachea, Bronchus, Or Lung
Male Female Total
Race Rate, Deaths Rate, Deaths Rate, Deaths
---------------------------------------------------------
Am Indian 42.5 57 19.9 34 29.6 91
Asian/PI 41.9 976 19.6 568 29.4 1,544
Black 97.5 1,295 44.5 813 66.3 2,108
Hispanic 30.1 1,075 13.5 609 20.6 1,684
White 68.7 11,704 45.6 10,141 55.1 21,845
---------------------------------------------------------
Total 61.9 15,107 38.1 12,165 48.1 27,272
Horizontal Bar Graphs (X = 6.5 Deaths / 100,000, x = 3.25):
Male Female Total
Race Rate Rate Rate
------------------------------------------------------------
Am Indian XXXXXXx XXX XXXXx
Asian/PI XXXXXXx XXX XXXXx
Black XXXXXXXXXXXXXXX XXXXXXX XXXXXXXXXX
Hispanic XXXXx XX XXX
White XXXXXXXXXXx XXXXXXX XXXXXXXXx
------------------------------------------------------------
Total XXXXXXXXXx XXXXXX XXXXXXXx
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| Header | Basic result type (statistic): | Age-adjusted rate |
| | Years analyzed: | 1997-1998 |
| | Geographic areas analyzed: | California |
| | ICD groups analyzed: | ICD 162 |
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| Data Section | Rows used in this example: | Row for each race |
| | Columns used in this example: | Column for each sex |
| | How many Black males died: | 1,295 |
| | Death rate for Hispanic females: | 13.5 per 100,000 |
| | Overall death rate: | 48.1 per 100,000 |
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| Bar Graphs | Death rate symbolized by big 'X': | 6.5 per 100,000 |
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Review -
Chapter 1 explained how Epigram makes it easy to analyze California
underlying cause mortality data,
listed the linked data sets contained within California
Epigram, discussed the access methods,
and described the confidentiality policy.
Chapter 2 explained the layout of an output table,
and presented sample tables for discussion and interpretation.
What's in this chapter -
Finally, you will get to use the program!
This is probably what you have been waiting for!
You will learn to navigate the interface, select parameters,
and produce a few tables like those you learned about
in the previous chapter.
The chapter also has a schematic overview of all
California Epigram menus, for your reference.
Access the program -
A tutorial runs through this chapter.
All procedures you are expected to do as part of the tutorial
are highlighted in a different type style, as shown
below. At this point, do the following:
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Access and start California Epigram
by clicking on the icon.
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The icon looks like a doctor's bag.
If you do not have an icon,
request that your network manager set things up so that
Epigram can be run from your network.
If needed, get assistance from another
California Epigram user.
You will start at the Main Menu (shown on next page).
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You will constantly return to the Main Menu.
After Epigram starts,
you are presented with the Main Menu,
similar to the following:
The Main Menu is the "command center" -
You move to submenus to modify parameters,
but return to the Main Menu to make a table.
The Main Menu lists all parameters currently selected,
providing an overview.
Highlighting a parameter -
One of the parameters, such as "Outcome", is highlighted
with a "light bar" that you move by pressing an arrow key.
The parameter list will scroll when you reach the bottom.
Note that you do not use a mouse to run the program.
Do the following:
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Press down arrow key to move light bar down.
Keep pressing to see the parameter list scroll down.
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The prompt is worth reading -
The Main Menu (and every other Epigram menu)
has a prompt at the bottom with guidance on what to do next.
On the Main Menu, you can press an arrow key, the ENTER
key, one of two letters (P or Q), or '?' for help.
Changing window appearance if running directly on PC -
The font size for the window running VitalPro may be changed
from the Windows toobar, to suit your screen.
The size "8 x 12", as shown, is a reasonable size.
The window running VitalPro may be toggled to full screen
(and back) by pressing ALT-ENTER (hold
down the ALT key and press the ENTER key).
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Access a submenu by highlighting an item and pressing ENTER.
Selecting parameter to change -
To change one of the parameters,
highlight the parameter (using the arrow keys)
and press ENTER.
Quick changes -
Some parameters have a very simple submenu. Try this:
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Highlight "Color Scheme" parameter by using the arrow keys.
Press ENTER key to access simple submenu. Select different color scheme.
Press ESCAPE key or 'Z' to return to main menu.
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More complex changes -
For more complex parameters,
a more complicated submenu will appear and help you change the parameter.
For example, to change the geographic selection, you would
highlight the parameter "Geographic Areas", and
press ENTER. Do the following:
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Use arrows keys to highlight "Geographic Areas".
Then, press ENTER to access the submenu.
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Submenus guide you step-by-step in selecting parameters.
Submenu with scrolling window -
This is a typical Epigram submenu.
It has a scrolling window with a list of items
(currently list of selected Counties).
One or more of the items may be highlighted by using the arrow keys.
Letters are action items -
This submenu has a list of actions, such as " E Delete all sets".
Pressing the letter (such as 'E') carries out the action.
Adding an area -
Carry out the following steps to add an area:
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- Press 'E' to delete all areas sets. You may be asked to confirm.
- Then, press 'B' to add some areas.
- Highlight
"Health Service Area 1",
and press ENTER to add.
- Then, press ESCAPE key to return to previous menu.
- Note that an item is added to the scrolling window.
- Finally, press ESCAPE key to return to the Main Menu.
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Access another submenu by highlighting an item and pressing ENTER.
Make sure you're back -
You should now be back at the Main Menu.
Note that "Geographic Areas" has been modified
(it is now set to
Health Service Area 1).
If you are not back at the Main Menu,
get assistance from a coworker familiar with using California Epigram.
Next, do the following steps, to access the submenu for modifying years:
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- Use the arrow keys to highlight
"Year".
- Then, press ENTER to move to a submenu for modifying the years.
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Submenus help you quickly and easily change parameters.
Submenu for a single range -
Your screen should look similar to that shown above.
This is the type of submenu used to modify a single range,
such as a range of years.
Next, do the following to modify the range and return to the Main Menu:
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- Press LEFT or RIGHT arrow key to change the low end.
- Press UP or DOWN arrow key to change the high end.
- Press HOME or END key to select the maximum range.
- Keep modifying range until you have selected a single year, such as
1998.
- When satisfied, press 'Z' (or ESCAPE key) to return to the Main Menu.
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Press 'P' at Main Menu when you are ready to make a table.
Check that you at the Main Menu -
You should now be back at the Main Menu.
You have used the submenus to select a few parameters,
and are now ready to produce your first table!
Produce a table -
From the Main Menu, you can press 'P' to produce output. Go ahead and do it:
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Press the letter 'P' to produce a table similar to the following:
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Time required -
After you press 'P' from the Main Menu, Epigram calculates
the results. The amount of time required depends on which parameters are
selected and what kind of computer you are using, but is typically seconds.
When Epigram finishes calculating the results, the output table will appear in a
new window, as shown above.
Examining the table -
You may browse the table with the arrow keys and
other cursor keys (PgUp, PgDn, HOME, END).
When you are finished examining the table, press the ESCAPE key (or 'Z').
Epigram will ask if you want to save the table to your log file (see
next page).
Depending on your access method and which menu options are selected,
you may also be asked if you want to print the table
or to save to an
alternate format
(CSV, dBASE III, HTML). Try this:
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Press down arrow key and other cursor keys to examine the results.
Press ESCAPE or 'Z' when you are finished viewing the results.
For now, press 'FALSE' when asked to save or print the table.
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Each table may be saved to your log file.
Purpose of the log file -
Epigram always has a log file ready for saving the
results exactly as you see them on the screen.
After you finish viewing a table,
Epigram always asks if you want to save it to your log file.
If you press 'TRUE' the table will be
appended to the log file (added to the end) as ASCII text.
At any time, you may close the current log file and
open a new one with a new name.
Log file location (local PC or LAN access) -
If you are using California Epigram on a PC local
area network or a stand-alone PC,
the log file will be saved directly on a
network disk or local disk.
You can save to any directory where you have write permission.
Epigram always tells you the location, such as
"C:\EPIGRAM.001".
Word processing hints -
After you finish using Epigram, you may edit and
print the log files using any word processor.
Use a non-proportional font such
as Courier to keep the columns aligned.
If needed, adjust the page orientation, margins or font
size so the text does not wrap to the next line.
Directly printing the results -
Local users (stand-alone PC, LAN PC, possibly UNIX) have
the additional option of immediately printing an output table.
After you view the output,
Epigram may ask if you want to print the results.
If you never want to print,
you may disable printing from the Main Menu.
NOTE: If you are connected to a network printer, your local network
manager may need to enable printing from command windows.
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Results may be saved to a CSV, DBF, or HTML file.
Saving to a second format -
After you finish viewing the results,
Epigram may ask if you want to save to
a comma-separated-value (csv), HTML (htm), or dBASE III (dbf) file.
If you want to save to one of these file formats,
set the "Second Format" option on the Main Menu.
CSV file (Comma-separated-value) (best for spreadsheets) -
CSV output has a
comma between each output item,
and each text item is surrounded by "double quotes".
Bar graphs are not included in CSV output since
CSV output is usually imported into presentation
software capable of more sophisticated graphics.
CSV format is ideal for importing into spreadsheet software.
"Deaths"
"Tabulated by Race-Ethnicity and Sex"
"Years: 1990"
"Place of Residence: Hlth Serv Area 1"
"Causes of Death (ICD-9): 001-999: All Causes Of Death"
"Race","Male","Female","Total"
"--------------------"
"Am Indian",58,43,101
"Asian/PI",22,13,35
"Black",30,22,52
"Hispanic",125,48,173
"White",4144,3614,7758
"--------------------"
"Total",4379,3740,8119
"========================================================================"
"Analysis Footnotes:"
"Unique ID, for Keeping Track of Analyses: 413XTCJM"
"Output Produced: Fri Apr 13 10:23:23 2001, by California VitalNet"
"Deaths Classified By: ICD-9, Underlying Cause, Place of Residence"
"Mortality Data Source: DHS Office of Health Information and Research"
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Example of CSV Output
DBF file (useful for many other applications) -
Results may also be saved to a dBASE III database file (dbf extension).
Database files are excellent for importing into almost any data analysis,
graphics, spreadsheet, mapping or other presentation software.
Field names are automatically imported along with the data.
Suppressed cells are represented by the number "-1".
DBF file limitations -
1) Header and footer information listing
analysis parameters is not included in database files.
To help out, you may want to make the name of the dBASE
file the same as the table ID, such as "405MFAQB.DBF" so you can
refer to the table later.
2) No more than 128 output columns may be saved to a dBASE III file.
This rarely presents a problem, because an unlimited number of rows is allowed.
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Epigram allows rapid exploratory data analysis.
The analysis cycle -
Exploratory data analysis means you
systematically refine your analyses.
Once you have made a table, look it over.
If it meets your needs, save it to your log file.
Possibly save the results to a CSV or dBASE III file.
Next, look over the parameters on the Main Menu,
highlight the parameter you want to change, and press ENTER.
A submenu will lead you through the modification process.
After setting all parameters as desired,
return to the Main Menu and produce another table by pressing 'P'.
Try it:
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At the Main Menu, modify a parameter or two.
Then press 'P' to produce and view another output table.
Try some of the
sample analyses shown
later.
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Hints for avoiding misinterpretation -
-
Double-check table header and footer
to verify it was the analysis you intended.
-
Double-check the ICD codes to make sure
they are the causes of interest.
-
Find the right balance between:
-
Aggregation (fewer rows and columns, easier to present, more stable rates).
-
Stratification (more rows and columns, more information, shows differences).
-
Acknowledge limitations in mortality data, including:
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Misdiagnosis of the cause of death.
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Changes in coding practices by certifiers.
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Acknowledge limitations in population data, including:
-
Possible census miscounts.
-
Difficulty projecting estimates to years between censuses.
-
Use confidence intervals to help
decide if differences are statistically significant.
-
If uncertainties remain, contact local data experts for advice.
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Epigram lets you quickly carry out a wide variety of analyses.
It's easy -
Once you get the hang of it, using Epigram is a snap.
If you have gotten this far,
you should be able to carry out about any analysis you desire.
Explore the menus - that way, you'll know what is available.
Don't be afraid to experiment and try out different options.
Refer to the help files and to other sections of this user's guide.
On-line help -
Although Epigram has been designed to be
as self-explanatory as possible,
it also includes extensive on-line help.
To access on-line help from any menu, press '?'.
A help screen will appear, with information related to the
current menu.
After viewing the help screen,
a scrolling list of help topics may be viewed,
for your selection.
Try it:
| | | |
At the Main Menu, press '?' and explore the help system.
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Quitting Epigram -
The 'Z' or ESCAPE key always returns to the previous menu.
Later, when you are done, you may press the 'Q' key
from the Main Menu to quit Epigram.
What other menus and options are available? -
If you have done the examples in this chapter,
you should have a good understanding of how to use
Epigram menus to select parameters.
However, you have just scratched the surface of
the capabilities and power of Epigram.
For your reference, the following
pages list all of the California Epigram menus.
The purpose of the list of menus is
to help you become aware of what is available
so that you can take fullest advantage
of the software. Do this:
| | | |
Referring to the outline on the next pages,
explore the menus to learn what is available.
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Using California Epigram, explore the menus listed below.
Main Menu allows you to:
- First, design table layout:
- Next, modify data variables:
- When ready, carry out an action:
- Produce Table (s)
- Exit Epigram
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Using California Epigram, explore menus for designing table layout.
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Using California Epigram, explore menus for modifying variables.
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For additional practice, and to gain
more understanding of how California Epigram
can speed and simplify data analysis,
carry out the following sample analyses.
Columns are by sex for each practice table, unless otherwise noted.
Do the following:
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- For each practice analysis, select the parameters as shown below.
- Then, press 'P' from the Main Menu to produce output.
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1. Current leading causes of death for California:
|
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| ICD sets - NCHS rankable causes |
Area sets - California |
Statistic - Deaths |
| Row sort - Sorted high to low |
Row for each - ICD set |
Races - All |
| Table for each - One only |
Years - Most recent year |
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2. 1995 HIV (ICD 42-44) age-specific death rates for one county:
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| Statistic - Death rate |
ICD sets - 42-44 |
Years - 1995 |
| Row for each - Age group |
Area sets - Sacramento |
Races - All |
| Table for each - One only |
Ages - 10-year groups |
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3. 1994-95 lung cancer (ICD 162) death rates ranked by area:
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| Statistic - Age-adjusted rate |
Row for each - Area set |
ICD sets - 162 |
| Adjustment standard - 2000 US |
Table for each - One only |
Races - All |
| Row sort - Sorted high to low |
Area sets - All counties |
Years - 1994-95 |
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4. California diabetes (ICD 250) time trends, one table for each race:
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| Statistic - Age-adjusted rate |
Row for each - Year |
ICD sets - 250 |
| Adjustment standard - 2000 US |
Table for each - Race |
Races - All |
| Area sets - California |
Years - All years |
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Review -
In the previous chapters, you have learned what Epigram is and how
to use it. Chapter 1 explained that Epigram analyzes California
underlying cause mortality data, listed the linked data sets, discussed access methods, and
outlined confidentiality requirements. Chapter 2 described the layout and
interpretation of an Epigram output table. Chapter 3 showed how to use the
interface to select parameters and produce output tables.
What's in this chapter -
This chapter lists and explains the parameters and
special options you may modify using the California Epigram interface. Epigram
allows great flexibility in selecting and modifying parameters. The program
allows you to mix and match parameters just about any way needed.
Also, Epigram prevents you from selecting incompatible parameters.
Here are the parameters, options and concepts covered in this chapter:
- Age groups
- Causes of death (ICD sets)
- Cell suppression
- Confidence intervals
- Decimal places
- Geographic selection (area sets)
- Multi-tables
- Race
- Sex
- Statistic (outcome)
- Table columns
- Table rows
- Table row sort settings
- Trend analysis
- Years
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The statistic is the basic type of number in a table.
What is a statistic? -
Every table has a statistic. The statistic (or outcome) is the basic type
of data generated in an output table.
Each statistic is defined in the glossary, and
on-line help files give calculation methods.
| Reliability Indicator | Statistic |
Number of
Deaths | Confidence
Interval |
| -- | 1 | Number of deaths |
| 1 | 1 | Death rate per 100,000 |
| 1 | 1 | Mean age of death |
| 1 | 1 | Age-adjusted death rate per 100,000 |
| 1 | 1 | Standardized mortality ratio (SMR) |
| 1 | 1 | Years of potential life lost (YPLL) |
| 1 | 1 | YPLL rate per 100,000 |
Reliability indicator -
As shown in the table above, a reliability indicator
(either number of deaths or confidence interval) is
displayed along with most statistics.
The number of deaths is normally
displayed along with each
rate and
YPLL.
Optionally, you may request that
confidence intervals
also be displayed as a reliability indicator.
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Table rows and columns may be set however you want.
Rows and columns -
Rows are horizontal lines in a data table.
Columns go up and down.
Every table has a row variable and a column variable.
Rows and columns may be set to any of the following:
- Only one row / column (not set to a variable)
- One row / column per selected age group
- One row / column per selected area set
- One row / column per selected sex
- One row / column per selected ICD set
- One row / column per selected race set
- One row / column per selected year set
Sorted rows -
Any table may be sorted as follows:
- Rows in standard order (not sorted by data). For example:
- Area sets in alphabetical order, such as
Orange before Yuba.
- ICD sets in numerical order, such as ICD 162 before ICD 410.
- Rows sorted from high to low, by total data
- Rows sorted from low to high, by total data
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You may automatically produce a series of tables.
Deaths
Tabulated by Race-Ethnicity and Sex
Years: 1995
Place of Residence: Alameda
ICD 001-999: All Causes Of Death
Race Male Female Total
-------------------------------------
Am Indian 24 10 34
Asian/PI 442 342 784
Black 1,204 982 2,186
Hispanic 440 283 723
White 3,030 3,167 6,197
-------------------------------------
Total 5,140 4,784 9,924
*** Tables omitted to save space ***
Deaths
Tabulated by Race-Ethnicity and Sex
Years: 1995
Place of Residence: Yuba
ICD 001-999: All Causes Of Death
Race Male Female Total
------------------------------------
Am Indian 4 3 7
Asian/PI 7 5 12
Black 19 4 23
Hispanic 10 5 15
White 263 193 456
------------------------------------
Total 303 210 513
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Purpose of multi-tables -
Suppose you want to make a separate table for each
county. It would be tedious to select the first county, make a table, select the
second county, make a table, etc. Multi-tables automates the production of such
a series of tables into one operation.
The multi-table setting may be one of the following:
- Only one table (the default)
- One table per selected age group
- One table per selected area set
- One table per selected ICD set
- One table per selected race set
- One table per selected year set
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Every table has a geographic specification.
Geographical Areas -
California Epigram analyzes data to the county level.
California has 58 counties,
organized into 14 Health Service Areas (HSA's). Each
HSA is a group of one or more counties.
You may select any combination of areas -
Epigram makes it easy to compare results between
different groupings ("sets") of geographic areas
(Counties and
County groupings),
or limit analysis to specific geographic areas.
One or more geographic areas may be combined
into an area set, and compared with other sets.
Selection is quick and easy -
All geographic operations are grouped together
on one submenu, to allow quick and easy selection
of any combination of areas desired.
A previous interface snapshot
shows the main geographic menu.
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Mortality tables always include causes of death.
ICD codes -
Epigram classifies causes of death with ICD codes.
The "International Classification of Diseases" is
the standard system for classifying causes of death.
Each disease or condition has a separate
3-digit or 4-digit ICD code. For example, 250 for diabetes mellitus,
005.1 for botulism.
1980 to 1998 death data use the 9th ICD revision (ICD-9).
As of 1999, death data use ICD-10.
ICD groups and sets -
An ICD group is one or more consecutive ICD codes
(for example, 410-414 for ischemic heart disease).
An ICD set is one or more ICD groups combined.
For example, ICD 174 for breast cancer and ICD 180
for cervical cancer may be combined into an ICD set.
At the broadest level, the ICD-9 system has 17 major disease categories.
At the most detailed level, there are about 5,000 four-digit codes.
For codes 800-999 (injury and poisoning),
Epigram uses external cause codes (E-codes),
standard for mortality analysis.
Selecting ICD groups -
Epigram allows you to easily select and combine ICD
groups to meet your analysis needs.
You may select ICD sets by entering the range (for example,
enter 162 for lung cancer, or 42-44 for HIV/AIDS). Or, you may select from
ICD selection menus, organized to allow you to narrow your focus without
having to know the specific code. Or, you may select one of several standard
ICD lists. For example, for easily calculating leading causes of death, select
the NCHS list of 38 leading causes.
California Epigram automatically formats
leading cause reports, to save you the
trouble of converting ICD codes such as "ICD 250" and set numbers such as
"Set #18" to descriptive terms such as "Diabetes" and "Heart Disease".
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You may select any combination of race groups to analyze. Races
may be combined into "sets" as needed. A separate
submenu allows easy selection. Keep in mind that
differences in health status between races may be due to socio-economic
differences.
Each
death
is classified as
American Indian, Asian / Pacific Islander, Black,
Hispanic, or White, as follows:
1) All who identify themselves as Hispanic are classified as Hispanic.
2) The remainder
are classified as Black, White, American Indian, or Asian / Pacific
Islander.
3) White also includes Other-Specified, Refused To State, and Unknown.
4) American Indian also includes Eskimo and Aleut.
5) Asian / Pacific Islander
includes Asian-Unspecified, Asian-Specified, Chinese, Japanese, Korean,
Vietnamese, Cambodian, Thai, Laotian, Hmong, Indian, Filipino, Hawaiian,
Guamanian, Samoan, and Pacific Islander.
Epigram lets you combine and analyze
age groups in just about any way needed.
You may select any contiguous combination of
age groups to analyze, such as 22-34.
Or, you may select a set of
ranges, such as birth-19, 20-39, 40-64, 65-99+.
The program will let you know which age groups are available for use.
Standard age groupings, such as 5-year, 10-year, and 20-year
age groups are easily selected.
Age groups may be combined in just about any way desired.
California uses 1-year groups up to age 21, and 5-year groups thereafter,
up to the 95-99+ group.
Up to age 21, 1-year ages are used (for example, 3 or 11-17).
For 22 and over, an age group may use any combination of 5-year
groups (for example, 40-44 or 30-49).
The highest group is currently 95-99+ (95 and over).
You may select Male, Female, or both sexes combined.
You may select any continuous range of one or more years to
analyze, such as 1980-1983.
In addition, you may select a set of year ranges
for comparing, such as 1990-1991, 1992-1993, 1994-1995. The program will let
you know which years are available for use.
Standard groupings, such as 1-year, 2-year, and 3-year groups are easily selected. Year groups may be
combined in just about any way desired.
Multi-year death rates are calculated by adding
all of the deaths over the time period,
and dividing by the sum of the
populations over that same period.
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Statistical reliability -
Results that are based on a smaller number of events are
less reliable than those based on a larger number. Confidence intervals allow
you to estimate the statistical reliability of your results.
Confidence interval definition -
A confidence interval (also known as
confidence limits) is the range of values within which the true value of a
variable is thought to occur, with a specified confidence level (95%, 90%,
80%, etc.). A higher confidence level (for example, 99%) is more stringent and
results in a smaller interval than a lower confidence level (for example, 80%).
Use the 95% confidence level unless you have a reason to do otherwise.
Methods for confidence intervals -
Epigram uses the Poisson distribution to
calculate most confidence intervals, using the method described in "Scientific
Tables", Diem and Lentner (ed), Geigy, 1970 (equations 802a and 802b on page
189). Confidence intervals for standardized mortality ratios are calculated using
the method of Rothman and Boice described in "Research Methods in
Occupational Epidemiology", Checkoway, Pearce, and Crawford-Brown, Oxford
University Press, 1989, (equations 5.7 and 5.8 on page 127).
Confidence
intervals for trend analysis are calculating using Student's t-distribution.
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Epigram also allows specification of the number of decimal
places in your results. For example, the number 64.29 has two decimal
places. The number 64 has zero decimal places.
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You may suppress cells with low numbers of deaths.
How cell suppression works -
Cells with low numbers of deaths
may be blanked out with an asterisk (*). You set the level at which results will be
suppressed. Bar graphs are not included if any cells are suppressed, because the
graphs would not be accurate.
Suppression of row / column totals -
A row total will be suppressed if
there is exactly one suppressed cell in the row,
or if the row total is low.
A row total will be displayed if
there is more than one suppressed cell in the row,
unless the row total is low.
The same rules apply to columns.
Use of cell suppression -
Cell suppression may be used for certain data tables
to be released to the public where there is a concern that low numbers should
not be published due to potential confidentiality issues. Concerns about
reliability of small numbers are usually better addressed by using confidence
intervals, or by including the number of deaths as a reliability indicator.
Cell suppression in dBASE III files -
A suppressed result is indicated as "-1".
Deaths
Tabulated by Age and Sex
Years: 1995
Place of Residence: Sacramento
ICD 042-044: Human Immunodeficiency Virus (HIV) Infection
Age Male Female Total
----------------------------------
Birth-4 * * *
5-14 * * *
15-24 * * *
25-34 56 7 63
35-44 87 8 95
45-54 36 5 41
55-64 6 * *
65-74 3 * *
75-84 * * *
85-94 * * *
95-99+ * * *
----------------------------------
Total 189 25 214
========================================================================
Analysis Footnotes:
Unique ID, for Keeping Track of Analyses: 413CCESE
Output Produced: Fri Apr 13 10:27:31 2001, by California VitalNet
Deaths Classified By: ICD-9, Underlying Cause, Place of Residence
Suppress Cell [*]: If 2 or Fewer Deaths In Cell
Suppress Row/Column Total [*]: If Exactly One Suppressed Cell in Row/Column
Mortality Data Source: DHS Office of Health Information and Research
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Table with Suppressed Cells
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Epigram automatically carries out trend analyses.
Purpose of trend analysis -
When analyzing data with a year for each row,
you usually want to know: Is there a trend up or down?
Is the rate increasing or decreasing?
Statistical analysis is helpful in quantifying the answer.
Least-squares line shows the trend -
Epigram automatically carries out a
"least-squares" analysis for a time series.
This finds the best straight
line to describe the data for each data column.
The line is defined by the value
for the first year ("TRUE-intercept"),
and the amount of change per year ("slope").
Confidence intervals show if significant trend -
Epigram calculates a confidence interval for the slope.
If the confidence interval doesn't include 0,
there is a significant trend.
Simplified presentation -
Epigram allows the user to substitute
a simplified presentation (not shown) that only says
whether the trend is significantly up or down for each column.
Death Rate (per 100,000)
Tabulated by Year and Sex
Age: Birth-11m Years
Place of Residence: California
ICD 001-999: All Causes Of Death
Horizontal Bar Graphs (X = 65.8 Deaths / 100,000, x = 32.9):
Male Female Total
Year Rate Rate Rate
--------------------------------------------------------
1990 XXXXXXXXXXXXXXX XXXXXXXXXXXx XXXXXXXXXXXXXx
1991 XXXXXXXXXXXXX XXXXXXXXXX XXXXXXXXXXXx
1992 XXXXXXXXXXXx XXXXXXXXXx XXXXXXXXXXx
1993 XXXXXXXXXXX XXXXXXXXXx XXXXXXXXXXx
1994 XXXXXXXXXXXx XXXXXXXXXx XXXXXXXXXXx
1995 XXXXXXXXXXx XXXXXXXXx XXXXXXXXXx
--------------------------------------------------------
Total XXXXXXXXXXXX XXXXXXXXXx XXXXXXXXXXX
Detailed Least-Squares Analysis, to Detect Linear Trend:
Column 1990 Y-intercept Slope 95% CI (4 df, t=2.7764)
----------------------------------------------------------------------------
Male 923.8 -51.78 -87.87 to -15.69 **
Female 718.9 -31.16 -60.47 to -1.860 **
Total 823.8 -41.72 -73.53 to -9.916 **
----------------------------------------------------------------------------
Y-intercept and slope may be used to draw least-squares line.
If confidence interval (CI) does not include 0, trend is significant [**].
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Significant Downward Trends for Death Rate Under One
|
Design table layout:
- Select outcome (primary statistic)
- Select row variable and sorting
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- Select column variable
- Select multi-tables
|
Select single age group (range variable):
- Extend lower limit (eg, 30-39 to 20-39)
- Extend upper limit (eg, 30-39 to 30-49)
Select multiple age groups (range variable):
- Select grouping (such as 20-year age groups)
- Extend lower limit (eg, 30-39 to 20-39)
- Extend upper limit (eg, 30-39 to 30-49)
- Split (eg, [30-39] to [30-34, 35-39])
- Delete an age group
Select race sets (categorical variable):
- Select some race sets
- Delete a race set
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- Combine some sets
- Split a race set
|
Select area sets:
- Select some area sets
- Delete an area set
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- Combine some sets
- Split an area set
|
Select ICD sets:
- Select some ICD sets
- Delete an ICD set
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- Combine some sets
- Split an ICD set
|
Modify miscellaneous settings:
- Change number of decimal places
- Turn on cell suppression
- Turn on confidence intervals
- Turn on row or column percents
Other tasks:
- Produce output from main menu
- Scan through output, and save as text
- Change output directory / output file
- Save output to CSV, HTML, or dBASE III format
- Use help system
Age-adjusted death rate
-
Deaths per 100,000 population, adjusted to a
standard population (such as US 1940 or US 2000), by the direct method.
Age-adjusted rates are often better for
making comparisons than unadjusted rates,
because they adjust for differences in age
distribution between populations.
An age-adjusted rate is a summary measure.
Besides calculating overall age-adjusted rates,
it is also recommended to compare age-specific rates.
Age-adjustment standard
-
A standard population for calculating an age-adjusted death rate.
The 1940 and 2000 US Census population are the most common standards.
Area set
-
One or more areas combined.
ASCII file
-
A text file, with only alphabetical, numerical, and punctuation
characters, like you would see in normal text.
Vitalnet can produce output in ASCII format.
Bar graphs
-
A section of a Vitalnet table.
Gives a useful graphical representation of the data.
May be omitted from the output table.
Cause of death
-
Any condition which leads to or contributes to death and is
classifiable according to the International Classification
of Diseases (ICD) system.
CDC
-
Centers for Disease Control and Prevention. US federal health agency.
Cell
-
A space for a single numerical result
in a table, at a row-column intersection.
Cell suppression
-
An asterisk "*" is placed in cells with fewer deaths than a limit set
by the user.
Row / column totals with exactly one suppressed cell in the
row / column are also suppressed.
If more than one cell in the row / column is
suppressed, the row / column total is displayed.
Columns
-
Vertical groupings of data in a Vitalnet table,
such as a column for each race group.
Confidence interval (confidence limits)
-
A range of values within which the
true value of a variable is thought to lie,
with a specified level of confidence.
For a result of 23.5, a confidence interval might be (23.1-23.9).
The smaller the interval, the more reliable the result.
If the 95% confidence intervals do not overlap,
there is a statistically significant difference.
Vitalnet uses several methods to
calculate confidence intervals.
The output table documents which method was used.
Confidence level
-
The likelihood that the true value of a variable is within a
confidence interval.
For example, for confidence intervals at the 95% level, we
are statistically 95% certain that the actual value of the variable is
within the interval.
CSV format
-
Also called comma-separated-value format.
A type of computer output that is readily
imported into other software, especially spreadsheet software.
Each output item is separated by a comma from surrounding items,
and each output text item is surrounded by "double quotes".
Vitalnet produces CSV output.
A comma-separated-value file has "csv" extension.
Data mining
-
Finding unexpected relationships in a data set.
Similar to exploratory data analysis.
Vitalnet is excellent at data mining.
Of course, keep in mind that the more you look,
the more unusual events you will find, just by chance.
Data warehouse
-
Software system, such as Vitalnet, making large complex databases
readily available for querying and analysis.
dBASE III format
-
A widely used file format derived from the database software of the same name.
Files in this format may be readily imported
into almost any data analysis, graphing, mapping,
or other presentation software.
Has dbf extension.
Suppressed cells are represented as the number "-1".
Vitalnet produces dBASE III output.
Death rate
-
Deaths per 100,000 population.
May be used to compare
the burden of disease between different groups.
Also called crude death rate.
Denominator
-
The number on the bottom of a fraction.
Population data are often referred to as "denominator data",
as they are used as denominators to calculate population-based rates.
E codes
-
"External" causes of death such as injuries and poisonings.
E codes range from ICD 800 to ICD 999.
Export
-
Produce output that can be read into other computer programs.
Vitalnet produces ASCII text,
comma-separated-value (csv extension), HTML (htm extension),
and dBASE III (dbf extension) files for export.
Filter variable
-
A variable solely used to filter which records are included in the output.
For example, for a single table with race rows and sex columns, age is
a filter variable.
Footer
-
Last part of a Vitalnet table.
Lists other details of the analysis, such as the date and time
produced, and the data sources.
Each table is assigned a unique ID, listed in the footer,
to assist you in keeping track of and organizing analyses.
Header
-
First part of a Vitalnet table.
Lists basic parameters you selected to define the table, such
as years analyzed.
HSA
-
Health Service Area.
Groupings of California counties. There are 14 HSA's.
ICD
-
International Classification of Diseases.
A widely used system of classifying diseases and injuries.
Each disease or set of diseases has an ICD code or ICD group assigned to it.
Vitalnet uses the ICD-9 system, which has been in use since 1979,
and the ICD-10 system, which started with 1999 mortality data.
ICD-9 code
-
A single ICD number representing a
single disease or injury (for example, ICD 250 for diabetes).
Note - ICD codes for HIV / AIDS first came into use in 1987.
ICD-9 group
-
A range of continuous ICD-9 codes (for example,
ICD 10-18 for tuberculosis).
ICD-9 set
-
One or more ICD-9 groups or codes combined,
for example ICD 174 (female breast cancer)
and ICD 180 (cervical cancer).
Import
-
Read information into a computer program.
ASCII text, CSV files, HTML, and dBASE III files
from Vitalnet are easily imported into word processing,
spreadsheet, data analysis, mapping, graphing, and other
presentation software programs.
Leading causes for ICD-9
-
The ten causes of death with the highest number of deaths, out of
a standard National Center for Health Statistics list of 38
rankable causes.
Vitalnet makes
it easy to select and rank the 38 cause list.
Least-squares
-
A standard method for fitting the best straight line
to a set of points.
Produces a 1 -intercept and a slope defining the least-squares line.
Mean age of death
-
If the ages of death were 50, 51, and 58,
the mean age of death is (50 + 51 + 58) / 3 = 53
Multiple age groups
-
One age group for each row or column of a table.
Example: Birth-19, 20-39, 40-59, 60-99+.
Multiple cause mortality data
-
Data which include all causes of death listed
on the death certificate.
Contrast with underlying cause mortality data.
Multiple causes of death
-
All diseases or injuries which led directly to death,
or all circumstances of the accident or violence
which produced the fatal injury.
NCHS
-
National Center for Health Statistics. US health statistics agency.
Part of the CDC.
Place of occurrence mortality data
-
Data compiled by the location where
the death occurred, without regard to
the place of residence of the deceased.
Vitalnet does not currently analyze for place of occurrence.
Place of residence mortality data
-
Data compiled by the usual place of residence of the deceased,
without regard to the location where the death occurred.
Vitalnet analyzes mortality data by place of residence.
Population
-
The number of people living in an area.
Primary Statistic
-
The basic type of numerical result displayed in a table, chart, or map.
For example,
death rate.
Rows
-
Horizontal lines in a Vitalnet table,
such as a row for each race group.
Row sort settings
-
Vitalnet rows may be sorted in ascending or descending order.
Set
-
A combination of one or more things.
For example, several areas may be combined into an area set.
Single age group
-
Only one age group (30-49, for example) is selected.
A single age group is used for tables
that do not have age columns or age rows.
Standardized mortality ratio (SMR)
-
The ratio of the expected number of
deaths in a population to the observed number of deaths.
The expected number of deaths is derived from applying
a standard set of rates (usually state or national rates) to the population.
SMRs are useful for assessing whether the
mortality in a population is higher than expected.
Statistic
-
See "Primary Statistic".
Table
-
A set of results produced by Vitalnet.
A table has several parts:
- Header - lists basic analysis settings
- Data section - numerical results
- Horizontal bar graphs - graphical representation of the data
- Footer - lists other analysis settings
Underlying cause
-
The disease or injury that initiated the train of events
leading directly to death, or the circumstances of
the accident or violence which produced the fatal injury.
A single underlying cause is assigned to each death.
Underlying cause mortality data
-
Data which include only the underlying
cause of death listed on the death certificate.
Contrast with multiple cause
mortality data.
UNIX
-
A widely used computer operating system. Vitalnet can run on UNIX,
either remotely, locally, or by client-server.
Unknown Values
-
Unknowns are automatically inserted into a Vitalnet table.
For example, a separate row (or column) for unknown race.
The rate is assigned as zero for an unknown category,
since there is no population denominator to use.
Some fields, such as sex for certain data sets, are never
unknown, so unknowns are left off the table.
When, such as for age-adjusted rates,
the unknown variable (age) is different from the
rows or columns, the number of unknowns
for age is shown below the table.
Windows
-
A widely used set of PC operating systems,
including Windows 3.1, 95, 98, NT, 2000, and XP.
Vitalnet will run under any version of Windows.
World Wide Web (WWW)
-
A widely used part of the internet that may
be easily accessed with a web browser.
Vitalnet runs on the WWW.
Years of potential life lost (YPLL)
-
Sum of the years of life lost by persons who suffered early deaths.
Early death is usually defined as death occurring
before the age of 65 (the YPLL age limit).
For example, death at age 40 (40.5) results in 24.5 YPLL to age 65.
YPLL is a widely used measure of premature mortality.
YPLL age limit
-
The age used for calculating YPLL.
The most common age limit is 65,
but other age limits may be selected from within Vitalnet.
YPLL rate
-
YPLL per 100,000 population in the appropriate age category.
For example, the YPLL rate up to age 65 is calculated as follows:
(YPLL up to age 65) / (population for age group 0-64).
YPLL rate is not commonly used.
Copyright 1998-2006 by Expert Health Data Programming, Inc.