Expert Health Data Programming, Inc.

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Nature of the software - Vitalnet is a health data warehouse / data analysis system. Vitalnet includes both desktop (VitalPro) and internet-based (VitalWeb) systems. VitalPro and VitalWeb make analyzing data faster, easier, and more reliable. This user guide describes VitalWeb Ajax, which runs over the internet in your browser. For convenience, we will often refer to the software as simply VitalWeb.

Advice about user guide - We have made VitalWeb as self-explanatory as possible. You don't have to read this user guide. If you pay attention to the menus, you can fully use the software. However, we recommend you read the user guide because: (1) Many users learn better from a written text. (2) It is helpful to have an overview of what the software can do, to know it's capabilities, before using it. (3) The user guide explains many background concepts necessary for properly interpreting and using the data.

PDF and HTML versions - Viewing the PDF version of this user guide requires Adobe or other PDF reader. Viewing the HTML version requires any modern browser connected to the internet. The user guide is also designed to be printed.

Tutorial - This user guide incorporates a tutorial, to help you get started using the software. Tutorial sections are highlighted as shown in this example:

Organization - Here's how this user guide is organized:

· Chapter 1: Introduction - Introduces Texas VitalWeb Ajax (VitalWeb).
· Chapter 2: Understanding Table Results - Shows and explains typical VitalWeb tables.
· Chapter 3: Using the Interface - Describes and shows how to use VitalWeb menus.
· Chapter 4: Outlines of Menus - Lists VitalWeb menus and capabilities.
· Chapter 5: Basic Table Layout - Explains how "table variables" (eg, rows) specify table layout.
· Chapter 6: Data Variables - Documents and explains variables such as race and age.
· Chapter 7: Charts and Maps - Explains map and chart (bar, line, pie) settings.
· Chapter 8: Other Parameters - Explains remaining parameters (eg, cell suppression).
· Glossary - Defines terms related to mortality data and VitalWeb.

Citation for Vitalnet - Expert Health Data Programming, Inc., Texas VitalWeb Ajax: Data Warehouse Software for Analyzing Texas ICD-10 Underlying Cause Deaths. Bellevue, Washington. 1998-2019. Browse www.ehdp.com for more information about the software or to contact EHDP.

Acknowledgements - We gratefully acknowledge the suggestions and ideas we have received from Texas Department of State Health Services staff and other users. Please contact us at 888-709-5319 or at www.ehdp.com if you have suggestions or requests. We want to further improve VitalWeb to meet your needs.

References for Vitalnet map color palettes - • LW Pickle, M Mungiole, GK Jones, AA White, "Atlas of United States Mortality", National Center for Health Statistics, 1997. • CA Brewer, "Color Use Guidelines for Mapping and Visualization", in "Visualization in Modern Cartography", Elsevier, 1994. • ColorBrewer web site showing color palettes.

Produced - "Apr 1 2023"   "17:40:19"

Copyright 1998-2021 by Expert Health Data Programming, Inc. All rights reserved. DATA AWAREHOUSE, VITALNET, VITALWEB, and VITALPRO are trademarks of Expert Health Data Programming, Inc.

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· VitalWeb makes it easy to analyze Texas mortality data.

Fast - You get results in seconds or minutes. Depending on the analysis, alternate methods could easily require hours or days to complete. Or may be totally not practical.

Reliable - VitalWeb helps prevent mistakes. It automatically incorporates needed complex algorithms and relationships. In contrast, analyses with SAS, SPSS, and other generic stats software are error-prone (besides being quite tedious). There are many steps that can go wrong in trying to manually analyze data.

Flexible - A wide variety of tables, charts and maps may be produced. You may set table rows and columns however you want. A large number of table, map, chart, and data parameters may be selected and combined as needed. On the other hand, if you choose you can ignore most program options, as they are set to reasonable default values.

Efficient - VitalWeb goes beyond just being fast. For example, you can automatically make a whole series of tables with one keystroke (multi-tables), such as a separate table for each county. As another example, chart output is automatically produced whenever it makes sense, but NOT produced when it would look terrible or be invalid.

Data Awarehouse! - VitalWeb is modern and efficient. An input Texas death file (one year) is 110 MB. The VitalWeb death file is only 0.8 MB! The standard "data warehouse" file (same granularity) is 2,189 MB. VitalWeb data is "smart": Each field "knows" what it is (eg, range, ICD) and what it does.

Easy to use - All operations are menu-driven, so you don't need to do tedious scripting. And you don't need to know any special codes such as FIPS codes.

Has online help - If needed, context-sensitive help is available from each menu. In addition, you may select from a list of on-line help topics, providing advice on topics related to VitalWeb and data analysis. The on-line help complements the information in this user guide. Useful on-line reference materials are also provided, such as tables of standard populations used for age-adjustment.

Fully documented output - VitalWeb output helps prevent data misinterpretation. All output is fully documented, so (1) there is no confusion or question about the exact analysis that was done, and (2) the analysis can be exactly replicated if needed.

Makes charts and maps - VitalWeb automatically makes charts (bar, line, and pie) and maps. The charts and maps can be customized in a wide variety of ways, as explained later.

Integrates with other software - VitalWeb automatically displays HTML output in your desktop browser. And the HTML may be imported into word processing software. In addition, the following alternative formats are automatically made along with each analysis, to allow importing the results into other software tools: (1) ASCII text format, (2) delimited format for importing into spreadsheets, (3) dBASE III format for importing into statistical, GIS, mapping, graphing and other software, (4) PNG, PDF, SVG and GIS map formats.

Can be customized - Let us know if other capabilities you would like added. We want to further improve VitalWeb to meet your needs.

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· VitalWeb links and analyzes geographic, population, mortality, and ICD-10 data.

Geographic information - VitalWeb includes a database of Texas counties and regions, linked to the population and mortality data sets. Boundary files are included for making maps.

Population data - The Center for Health Statistics at DSHS provides 1980-1999 Texas population data. The Texas State Data Center provides Texas population data for 2000 and beyond. The Texas State Data Center periodically revises its estimates for years between censuses, but the changes are typically small. The population variables included within VitalWeb are age, county of residence, race, sex, and year. Population figures are used for calculating death rates. A separate Vitalnet interface, PopTrend, analyzes population data for demographic trends, and to obtain denominators for analyzing other data sets.

Mortality data - The Center for Health Statistics at the Texas Department of State Health Services provides all Texas mortality data. New data are normally loaded once a year, based on their schedule. Mortality variables within the software include age of deceased, cause of death, county of residence, race, sex, and year.

Place of residence - The software analyzes by place of residence of the deceased. For example, if a Dallas resident died in a motor vehicle collision in Houston, the software would classify the death under Dallas. Standard mortality reports usually use place of residence (the other system is "place of occurrence").

Underlying cause - The software 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. A separate program (MultiCod) analyzes multiple cause mortality data, with up to 20 causes for a deceased person.

ICD information - The ICD (International Classification of Diseases) is the standard system for classifying causes of injury, illness and death. The software includes a complete database of the ICD system, for selecting and combining causes of death to analyze. For example, you can easily determine the leading causes of death or select any combination of ICD codes for analysis. The ICD-10 system is used starting with 1999 mortality data. Mortality data for 1980 to 1998 use the ICD-9 system. VitalWeb operation is the same with ICD-9 or ICD-10 data, so there is no separate user guide for analyzing ICD-9 death data.

Data linking - Four data sets (geographic data, population data, mortality data, and ICD data) are linked with each other into the VitalWeb data warehouse.

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· Access VitalWeb on a PC connected to the internet.

Execution speed - VitalWeb is fast. Analyses that might otherwise take hours to days to set up and run are done in seconds or minutes.

The software execution speed depends on:

• The speed of your internet connection
• The load on the server running the analysis
• The specific analysis you are doing (greatest effect)

It typically takes a second or two to generate output.

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· Texas VitalWeb users must comply with confidentiality requirements.

Confidentiality policy - Your use of Texas VitalWeb indicates your agreement to the following conditions: You will not try to use Texas VitalWeb results nor let anyone else use Texas VitalWeb 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 Texas Department of State 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, Texas VitalWeb can suppress cells that have fewer than a user-defined number of death. Cell suppression can increase the confidentiality of written reports in some cases. Cell suppression is described more fully later.

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What's in this chapter - VitalWeb makes tables, charts, data files, and maps. Before jumping in and using VitalWeb (next chapter), it is helpful to get a good understanding of VitalWeb table results, and see some practical output table examples.

First, this chapter explains the four sections of a VitalWeb table:

• Header - Basic analysis parameters.
• Data section - Numerical results.
• Tabular chart - Optional simple graphic.
• Footnotes - Other analysis parameters.

As appropriate, a VitalWeb table may include a graphic chart (bar, line, or pie). Graphic charts (and when they are made) are explained and illustrated later in this user guide.

Second, this chapter shows examples of two Texas VitalWeb tables, to give you an idea of what is possible. You are given an opportunity to practice interpreting the sample tables.

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· Each table has four parts: header, data section, optional tabular chart, footnotes.


Header: Documents major parameters for the analysis. The table analyzes 1999 deaths for Galveston and Harris counties. Chronic lower respiratory disease (ICD J40-J47) is analyzed.

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 33 deaths in women age 40-59, 402 deaths in males, and a total of 839 deaths. Each result (such as 33, 402, and 839) is called a "cell".

Optional Tabular Chart: Represents the results in a text-based horizontal bar graph format. May be turned off by user via a menu option. Is automatically turned off if a graphical chart (bar, line, pie) is shown. In the above example, each 'X' (big X) symbol represents 26.6 deaths. The tabular chart clearly shows that the great majority of deaths in this analysis were in the 60-99+ age group, for both sexes.

Footnote: Documents additional details related to the analysis, such as data sources and when the table was produced. Usefully, each table is assigned a unique ID 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.

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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.

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Here is another table. Fill in the spaces below. Then, check your answers on the next page.

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Here is the second table again, and our interpretation of the results.

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What's in this chapter - Finally, you will get to use VitalWeb! 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.

Some advice about learning - As you use VitalWeb, we encourage you to explore and try things. Don't be shy. You cannot cause any damage by using the software. VitalWeb does a lot, and there are a lot of options, because analyzing data has many options. But running the software is pretty simple, once you know the basic way it works.

Open favorite browser - The preferred browsers are Firefox, Internet Explorer, Safari, and Opera. Chrome has a bug, does not display local files via Javascript, so Chrome fails to display time trend maps.

Access the software - 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:

VitalWeb Ajax runs in any relatively recent (say within past five years) browser. You will start at the Main Menu (shown on next page).

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· You will constantly return to the Main Menu.



After VitalWeb Ajax starts in the browser window, you are presented with the Main Menu, as shown above.

The Main Menu is the "command center" - You move to submenus to modify parameters, but return to the Main Menu to make a table (or map). The Main Menu lists all parameters currently selected. It provides an overview of the current analysis scenario.

Menu Level - Note the words "Main Menu" in the upper right corner. This is also "Menu Level 1". For each submenu, as shown on the next page, the "Menu Level" is indicated: 2, 3, etc.

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· Access a submenu by clicking on a linked item.



Selecting parameter to change - To change a parameter, click on the parameter (using your mouse).

Quick changes - Some parameters have a relatively simple menu. Try this:

More complex changes - For more complex parameters, a more complicated menu will appear and help you change the parameter. For example, to change the geographic selection, you would click on the underlined link "Areas". Do the following:

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· Each menu guides you in selecting parameters.



If you have carried out the tutorial steps, your screen will look similar to the above screen. If your screen looks different, go back and try again. It is very simple: Highlight an item on the Main Menu using the arrow keys. Then, press ENTER.

Menu with scrolling window - You are now on the "Area Sets" menu. This is a typical VitalWeb Ajax menu. It has a scrolling window with a list of items, "Area Sets" in this case. You may highlight one of the list items by using your mouse.

Action Buttons - To the right, the menu has a series of buttons, such as "Delete all sets". As you would expect, clicking a button carries out some action. Note that some buttons may be "greyed out" (temporarily inactive). For example, you cannot merge sets until you have highlighted some sets to merge.

Adding an area - Carry out the following steps to add an area:

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· You always return to the Main Menu after modifying a parameter.



Make sure you're back - You should now be back at the Main Menu, shown above. Note that "Geographic Areas" has been modified (it is now set to PHR 1). If you are not back at the Main Menu, get assistance from a co-worker familiar with using the software. Or (in almost all cases), simply click the "Close" button a few times.

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· Click "Make Table" on the Main Menu to make a table.

Check that you at the Main Menu - You should now be back at the Main Menu, as explained on the previous page. You have used the menus to select a few parameters, and are now ready to produce your first table!



Time required - The amount of time required depends on which parameters are selected, but is typically a few seconds.

Examining the table - The output will automatically appear in your browser. The bottom of the output (not shown above) contains links to spreadsheet, text, and dBASE III output. In addition, there may be links to bar, pie, or line chart output.

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· From the Main Menu, you may switch to designing a map, as shown below.



You've got the basics - You've navigated the Main Menu, used submenus to select parameters, made an output table, and viewed the output. That's about all there is to operating the software! It's a simple, fast, powerful analysis cycle. You should be getting the hang of VitalWeb by now. If not, talk with a coworker or contact us.

PNG, PDF, SVG and GIS maps - VitalWeb also makes a wide variety of map formats. And you can customize the maps in a wide variety of ways to meet your needs: add area labels, switch between boundary types (eg, regional boundaries), set the number of colors to use, select a color palette, set the range type (eg, "equal interval"), select time trend maps (map for each year range). These options are described and illustrated later in this user guide.

Make a map - Since tables and maps are so fundamentally different (eg, maps don't have rows, tables don't have colors), VitalWeb displays a simpler menu system (shown above) when designing a map. To make your first map:

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· VitalWeb lets you quickly carry out a wide variety of analyses.

Explore the menus - Once you get the hang of it, using VitalWeb is easy. If you have gotten this far, you understand the basic program operation.

Don't be afraid to experiment and try out different options. Once you have made a table (or map), look it over. If it meets your needs, you're done. If the output is not quite right, look over the parameters on the Main Menu, an click on the one you want to change. A submenu will lead you through the modification process. After setting all parameters as desired, return to the Main Menu and produce another table (or map) by clicking the "Make" button. Try it:

On-line help - VitalWeb Ajax has been designed to be as self-explanatory as possible. However, it also includes extensive on-line help. To access on-line help from any menu, click the "Help" link. A help screen will appear, with information related to the current menu. Try it:

Quitting VitalWeb Ajax - Simply dismiss the browser window (CTL-W).

Suggestions to avoid misinterpretation:

• Double-check header and footer to verify it was the analysis you intended.
• Double-check ICD codes to make sure they are the causes of interest.
• Acknowledge limitations in accuracy of the cause of death.
• Acknowledge possible changes in coding practices by certifiers.
• Acknowledge possible confounding variables (eg, socioeconomic status).
• Acknowledge the possibility of census miscounts.
• Acknowledge difficulty estimating population in years between censuses.
• Use age-adjusted rates to compare rates between different populations.
• Use fewer rows and columns for a simpler presentation.
• Use more rows and columns if more comparisons and detail are needed.
• Use confidence intervals to help determine statistical significance.
• Graphic charts make the data story easier for viewers to understand.
• If uncertainties remain, contact local data experts for advice.

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For additional practice, and to gain more understanding of how VitalWeb can speed and simplify data analysis, carry out the following sample analyses. If a variable is not listed (eg, Sex), then all values are selected (eg, Male and Female). Do the following:

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What other menus and options are available? - If you have done the examples in the previous chapter, you should have a good understanding of how to use VitalWeb menus to select parameters. However, you have just scratched the surface of the capabilities and power of the software. For your reference, the following pages list all of the Texas VitalWeb Ajax 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:

Choose Table/Map and Statistic:

  • Table or Map
  • Main Statistic

Design table layout (if making table):

  • Row Variable
  • Row Sorting
  • Column Variable
  • Multi-Tables

Modify data variables:

  • Age Groups
  • Area Sets (If Making Table)
  • Cause of Death
  • Race
  • Sex
  • Year Ranges

Modify other program settings:

  • Chart Settings (If Making Table)
  • Map Settings (If Making Map)
  • Secondary Statistics
  • Output HTML Settings
  • Various Other Settings

From Main Menu, carry out an action:

  • Produce Table and Charts
  • Produce Map
  • Exit VitalWeb Ajax

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· Using VitalWeb Ajax, explore menus for designing table layout.

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· Using VitalWeb Ajax, explore menus for modifying variables.

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· Using VitalWeb Ajax, explore other settings listed below.

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This checklist gives an overview of the main capabilities of VitalWeb Ajax, and the tasks you might normally do to analyze the data. You don't have to do everything listed below. Most of the parameters can be left at default values, with no problem. For example, you are always going to get a nice pie chart, even with the default settings. But it is helpful for you to be aware of what is available, for example in case you want a bigger pie chart.

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What's in this chapter - This chapter explains what we call "table variables". Table variables are the basic variables that determine the basic layout of an output table. VitalWeb allows great flexibility in designing your table. Also, the software prevents incompatibilities from occurring.

The table variables are:

  · Main Statistic
  · Row Variable
  · Row Sorting
  · Column Variable
  · Multi-Tables

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· The Main Statistic is the basic type of number in a table (or map).

What is a Main Statistic? - Every table (or map) has a Main Statistic. No exceptions. The Main Statistic (or outcome) is the basic type of data generated in the output. Each Main Statistic is defined in the glossary, and on-line help files give the full calculation methods.


Reliability indicator - As shown in the table above, a reliability indicator (either number of deaths or confidence interval) is displayed along with most Main Statistics. The number of deaths is normally displayed along with each rate and YPLL. Optionally, you may request that confidence intervals be displayed as the reliability indicator.

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· Table rows and columns may be set as needed, and rows can be sorted.

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 (or column) (not set to a variable)
• One row (or column) per selected age group
• One row (or column) per selected area set
• One row (or column) per selected sex
• One row (or column) per selected ICD set
• One row (or column) per selected race set
• One row (or column) per selected year range

Row sorting - Rows can optionally be sorted.

The VitalWeb default is "unsorted rows" (standard order). Examples of unsorted rows include:

• Area sets in alphabetical order, such as Bexar before El Paso
• ICD sets in alpha-numerical order, such as C34 before K74

From the "row sorting" menu, you can tell VitalWeb to sort table rows as follows:

• Rows sorted from high to low, by total data
• Rows sorted from low to high, by total data

Columns cannot be sorted, instead always appear in standard order.

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· You may automatically produce a series of tables.


Purpose of multi-tables - Suppose you want to make a separate table for each county. It would be quite 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. See the table series above for an example. 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 range

Multi-tables are very powerful and a big time-saver.

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What's in this chapter - This chapter lists and explains data variables you can modify using VitalWeb. The software allows great flexibility in selecting and modifying data variables. You can select variables into "ranges" (eg, 2009-2010) and "sets" (eg, "White + Black") just about any way needed.

Here are the data variables covered in this chapter:

· Geo Selection (Area Sets)

Categorical variables:
· Race
· Sex

Range variables:
· Age Groups
· Years

· Causes of Death (ICD Sets)

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· Every table has a geographic specification.

Geographical Areas - VitalWeb analyzes Texas data to the county level, and also easily allows pre-set and ad-hoc county groupings. Texas has 254 counties, organized into regions (a group of several counties), as follows:

• 8 Health Service Regions (HSR)
• 11 Public Health Regions (PHR) (Effective 3/1/93)
• 25 Metropolitan Statistical Areas (MSA) (and the Remainder)
• 24 Councils of Government (COG)
• 15 Immediate Border Counties (vs Remaining Counties)
• 32 La Paz Border Counties (vs Remaining Counties)

Area Sets - One or more geographic areas may be combined into an "area set", and compared with other sets. For example, Bell County and Travis County can be combined into an area set. VitalWeb makes it easy to combine areas into sets. Later, you split the set up. Or combine some sets to make a new set. Bottom line: VitalWeb makes it easy to compare results between different groupings ("sets") of geographic areas (counties and county groupings), or limit analysis to specific geographic areas.

Selection is quick and easy - All geographic operations are grouped together on one menu, to allow quick and easy selection of any combination of areas desired. A previous interface snapshot (page 3-3) shows the main geographic menu.

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Race Groups (Categorical)

You may select any combination of race groups to analyze, by combining races (eg, Black, White) 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.

For 2004 and previous years, each death is classified as White, Black, Hispanic, or Other, as follows: (1) If race is reported as Black, the category is "Black". (2) Of the remainder, if race is reported as Chinese, Japanese, Hawaiian, Filipino, Asian Indian, Korean, Samoan, Vietnamese, or Guamanian, the category is "Other". (3) Of the remainder, those said to be of Hispanic origin are counted as "Hispanic". (4) Of the remainder, if race reported as White, the category is "White". (5) All remaining are classified as "Other". (DSHS Definition)

For 2005 and later years, each death is classified as White, Black, Hispanic, or Other, as follows: (1) Those said to be of Hispanic origin are counted as "Hispanic". (2) Of the remainder, if race is reported as Black, the category is "Black". (3) Of the remainder, if race reported as White, the category is "White". (4) All remaining are classified as "Other". (DSHS Definition)

Sex (Categorical)

You may select Male, Female, or both sexes combined.

Age Groups (Range)

VitalWeb 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+.

VitalWeb 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. Once you have a standard grouping selected, you may combine them in just about any way desired.

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 75-99+ (75 and over).

Years (Range)

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. VitalWeb 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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· Mortality tables always include causes of death.

VitalWeb classifies causes of death with ICD codes. ICD stands for "International Classification of Diseases", the standard system for classifying causes of death.

ICD-10 codes - Each disease or condition has either: (1) a 3-digit ICD-10 code (one letter followed by two digits) (for example, ICD-10 C53 for cervical cancer), or (2) a 4-digit ICD code (for example, ICD-10 G71.0 for muscular dystrophy). As of 1999, mortality data use ICD-10.

ICD-9 codes - Data from 1980 to 1998 use the 9th revision of the ICD system (ICD-9). As with ICD-10, each disease or condition has a separate 3-digit or 4-digit ICD-9 code. However the codes look different, as the ICD-9 codes are all numbers (no letters). For example, ICD-9 250 for diabetes mellitus, and ICD-9 005.1 for botulism. Otherwise, ICD-9 works basically the same as ICD-10. Note that there are many changes in the categories, so ICD-9 and ICD-10 results cannot always be directly compared.

ICD groups and sets - An ICD group is one or more consecutive ICD codes (for example, C00-D48 for neoplasms). An ICD set is one or more ICD groups combined. For example, ICD C50 for breast cancer and ICD C53 for cervical cancer may be combined into an ICD set (C50 plus C53).

Number of ICD groups - 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), VitalWeb uses external cause codes (ICD-9 E-codes), standard for mortality analysis. At the broadest level, the ICD-10 system has 19 major disease categories. At the most detailed level, there are about 7,600 four-digit ICD-10 codes.

Selecting ICD groups - VitalPro allows you to easily select and combine ICD groups to meet your analysis needs. You may select ICD sets by:

(1) Entering the range (for example, enter C16 for stomach cancer, or B20-B24 for HIV/AIDS).

(2) Selecting from ICD "tree" menus, organized to allow you to narrow your focus without having to know the specific code.

(3) Selecting from one of several standard ICD lists of commonly used codes.

(4) Selecting one of the entire standard lists. For example, for easily calculating leading causes of death, select the NCHS list of potential leading causes.

VitalWeb automatically formats leading cause reports. This saves you the trouble of converting ICD codes such as "ICD E10-E14" and set numbers such as "Set #18" to descriptive terms such as "Diabetes" and "Heart Disease".

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What's in this chapter - This chapter explains about settings for VitalWeb charts and maps.

Charts are very helpful for scanning results for trends, and for better communicating findings to others. Plus, they make the data more attractive, less boring. VitalWeb makes three kinds of graphic charts. Each chart type is more or less best suited for a particular purpose:

Bar Charts

• Compare amounts of different things.
• For example, which race group has a higher birth rate?
• Or, which sex has more deaths from a particular cause?

Line Charts

• Show movement, trends, and comparisons.
• For example, which age group has a higher death rate?
• Or, is a particular rate changing (up or down) over time?

Pie Charts

• Show parts of a whole, a quick overall comparison.
• For example, what proportion of deaths occur in males?
• Or, what proportion of births are to teenage moms?

Maps are a special kind of graphical data display, to show geographic differences in rates and counts. VitalWeb makes attractive maps, in PNG, PDF, SVG and GIS formats.

The automatically produced charts and maps can be extensively customized, as explained in this chapter.

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• Background color behind the chart.
• Color palette to use for the bars.
• First color in palette to use for bars.

Below are shown the settings, with examples:

Background Color for Chart (20 options) White Grey 95 Lemon Chiffon Light Cyan

Color Palette for Bars (4 options) Bright Colors Subdued Colors Bright + Subdued Black + White

First Color in Palette to Use (10 options) A Forward D Reverse E Forward J Reverse

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Vertical or Horizontal Bars Vertical Bars Horizontal Stacked Bars? Stacked Bars Not Stacked

Height of Each Bar (9 options) 2 cm High 4 cm High Width of Each Bar (8 options) 0.2 cm Wide 0.6 cm Wide

Include Grid Lines? Include Grid Omit Grid Font Size for Chart Text (8 options) 10 pt Font 14 pt Font

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Eight ways to customize Vitalnet line charts:

Background Color for Chart (20 options) Alice Blue Light Yellow

Include Data Point Symbols? (2 options) Include Symbols Omit Symbols

Radius for Chart Symbols (10 options) 1.0 mm Radius 1.4 mm Radius

Line Chart Height (9 options) 4.0 cm (Not Shown) 7.0 cm (Not Shown)

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Width of Lines (3 options) Thin Lines Thick Lines

Include Grid Lines? (2 options) Include Grid Omit Grid

Colored Lines, or Black + White (2 options) Colored Lines Black + White Lines

Font Size for Chart Text (8 options) 10 pt Font (Not Shown) 12 pt Font (Not Shown)

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Three settings for customizing pie chart coloring:

• The background color behind the chart.
• The color palette to use for the pie chart.
• First color in palette to use (for slice #1).

Background Color for Chart (20 options) Cornsilk Background Grey Background Light Cyan

Color Palette for Pie Chart (3 options) Bright Colors Subdued Colors Mixed Colors

Slice #1 Color to Use (10 options) Color A for #1 I Forward F Reverse

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Four settings for customizing pie chart layout:

Slice #1 Clock Position (12 options) Slice #1 at Noon Slice #1 at 3:00 Slice #1 at 6:00

How to Label Pie Chart (9 options) Line + Label Label Only Use Legend

How to Display Slice Percents (3 options) Percent After Label Under Label Omit Percents

Pie Chart Radius (9 options) 3.0 cm (Not Shown) 4.0 cm (Not Shown) 5.0 cm (Not Shown)

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Color Combination (36 options) Orange-Red Grey Red-Blue

Number of Colors (8 options) 3 Colors 5 Colors 7 Colors

How to Set Ranges (3 options) Equal Ranges Equal Counts Natural Breaks

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Boundaries to Display (6 options) County Boundaries HSR Boundaries Border Counties

Cell Suppression (14 options) Suppression Off Suppress if < 10 Events Suppress if < 30 Events

Map File Format (PNG, PDF, SVG, GIS) PNG Map (Imports Best) SVG Map (Prints Best) GIS Map (Interesting)

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Color Palettes - VitalWeb uses 36 beautiful map color palettes. The palettes have been proven through published research, and used in professional atlases. Trying to hand-pick colors is tedious, and usually produces bad-looking results. VitalWeb color palettes save lots of time and guarantee successful results.


All palettes are OK for color printing. BW - OK for black and white printing? CB - OK for red-green color blindness?

Text Font for Map - 9 font options for displaying area labels:   [Serif Normal] [Sans-Serif Normal] [Monospace Normal] [Serif Bold] [Sans-Serif Bold] [Monospace Bold] [Serif Italic] [Sans-Serif Italic] [Monospace Italic]

Time Trend Maps - Vitalnet can make a series of maps, one for each selected year range. The map series is cycled within a special interface. The data ranges are the same for each map in the series.

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Vitalnet is smart about making output. Whenever you make a table, Vitalnet usually makes one or more accompanying charts. But it only produces the charts that make sense. If a chart would look terrible, or not be epidemiologically valid, the software does not make it.

Vitalnet avoids making misleading or useless charts. To prevent misinterpretation and embarassing results, Vitalnet intelligently decides when it appropriate to make a chart, as explained below:

Bar charts are only made if the following conditions are met:

• 1 to 20 rows (groups of bars).
• 1 to 10 columns (bars per group).
• No suppressed results (for stacked bar chart).

Line charts are only made if the following conditions are met:

• Range rows (such as age or year).
• No breaks in ranges (not 2000, 2002).
• Rows not sorted. No suppressed results.
• No more than 10 lines (10 columns).

Pie charts are only made if the following conditions are met:

• Cumulative data (counts, some rates).
• One set of numbers (one row or column).
• 2 to 9 pie slices. No suppressed results.

Time trend maps (that cycle from map to map) are only produced when all selected year ranges are the same width. For example, 1995-1996, 1997-1998, 1999-2000 is OK. But 1995-1996, 1997-1998, 1999 is not OK. Also, at least two year ranges are required. So if you just have 1995-1996 (a single range) selected, Vitalnet does not make a series of time trend maps.

Time trend analysis, when making a table with year rows, is only carried out when: 1) at least three year ranges are selected, 2) there are no gaps in the ranges, 3) the ranges are the same width, and 4) the rows are not sorted. When these conditions are met, the time trend analysis is epidemiologically valid.

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What's in this chapter - This chapter explains various other parameters, that are neither table variables nor data variables.

Out of the many other parameters, ere are the ones covered in this chapter:

  • Cell Confidence Level
  • Cell Suppression
  • Decimal Digits
  • Trend Confidence
  • CSV/TSV and DBF Output

There are some other parameters not specifically covered in this manual. For example, you can tell VitalPro to include or not include a unique ID on the output. These additional parameters are self-explanatory, and are usually kept at the default value anyway.

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Statistical reliability - In general, 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 formally 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%) implies greater confidence that the true value is included, so results in a larger interval than a lower confidence level (for example, 80%). Use the 95% confidence level (a somewhat arbitrary but very widely used standard) unless you have a good reason to do otherwise.

Methods for confidence intervals - VitalWeb 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 calculated using Student's t-distribution.

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VitalWeb 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. Line charts and pie charts are not included if any cells are suppressed, because the charts are too disrupted by the cell suppression.

Suppression of row / column totals - It's a little complicated. A row total will be suppressed if there is exactly one suppressed cell in the row, or if the row total is itself low enough to be suppressed. A row total will be displayed if there is more than one suppressed cell in the row, unless the row total is itself low enough to be suppressed. 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 probably 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".

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· VitalWeb automatically carries out trend analyses.

Purpose of trend analysis - When analyzing data with a year (or year range) for each row, you usually want to know: Is there a trend up or down? Is the rate increasing or decreasing?

When trend analysis is done - Trends are calculated if: (1) the "row variable" is year, (2) rows are not sorted, (3) at least three year groupings, (4) and year groupings have equal ranges, for example 2000-2001, 2002-2003, 2004-2005. Another example of equal ranges is 2010, 2011, 2012.

Least-squares line shows the trend - VitalWeb carries out a "least-squares" analysis for the 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 ("y-intercept"), and the amount of change per year range ("slope").

Confidence intervals show if significant trend - VitalWeb calculates a confidence interval for the slope. If the confidence interval doesn't include 0, there is a significant trend.

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· Results may be saved to a text, spreadsheet, or database file.

Saving to a second format - The output in your desktop browser always includes (at the bottom of the output) links to ASCII text (txt), spreadsheet (csv, tsv, or dif), and dBASE III (dbf) output. To view or download one of the second formats, simply click on the appropriate link, such as "dBASE III".

TXT file - Text output is pure ASCII text, for example as shown on the previous page.

CSV file - CSV (comma-separated-value) output has a comma between each output item, and each output text item is surrounded by "double quotes", as shown below. CSV format is ideal for importing into spreadsheet software, such as Excel. TSV (tab-separated-value) format is similar, but uses tabs instead of commas between items.

Browser differences - Internet Explorer automatically displays CSV output in Excel (if installed). Other browsers may be configured to automatically display CSV output in a spreadsheet program. Or you may need to right-click on the link, and select "Open" to open the file.



DBF file - Results may also be saved to a dBASE III database file (dbf extension). DBF 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 cannot be included in DBF 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) Only 128 columns may be saved to a dBASE III file. This rarely presents a problem, because unlimited rows are allowed.

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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 synthetic 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.

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 events (such as 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 may be 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, so 23-24 is more reliable than 13-34. If the 95% confidence intervals do not overlap, there is probably 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 - Comma-separated-value format. CSV files are readily imported into spreadsheet software. Each output item is separated by a comma from surrounding items, and each output text item is surrounded by "double quotes". A comma-separated-value file has "csv" extension. Similar to TSV format.

Data warehouse - A software system, such as Vitalnet, making large complex databases readily available for querying and analysis. A related term is "data mining", finding unexpected relationships in a data set, for further study. Data mining is 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.

dBASE III format - A widely used file format originally for the database software of the same name. Files in dBASE III format may be readily imported into almost any data analysis, graphing, mapping, or other presentation software. Uses dbf extension. Suppressed cells are represented as the number "-1".

Death rate - Deaths per 100,000 population. May be used to compare the burden of disease between different groups. Also called crude death rate.

DIF format - Data interchange format. DIF files are readily imported into spreadsheet software. The DIF format is too complex to explain in this glossary. Has "dif" extension.

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.

Export - Produce output that can be read into other computer programs. Vitalnet produces ASCII text (txt), comma-separated-value (csv), HTML (htm), and dBASE III (dbf) 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 10-19 is a filter variable.

FIPS Code - FIPS = "Federal Information Processing Standards". A five-digit number which uniquely identifies counties, territories, and certain other areas in the United States. States have two-digit FIPS codes.

Footer - Last part of a Vitalnet table. Lists less important details of the analysis, such as the date produced, and data sources. Also contains a unique ID to assist in keeping track of analyses.

Header - First part of a Vitalnet table. Lists key analysis parameters, such as years analyzed.

ICD - International Classification of Diseases. A widely used system for 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, for 1979-1998 mortality data, and the ICD-10 system, for 1999-present mortality data. An "ICD code" is a single ICD number representing a single disease or injury. For example, ICD E10 for insulin-dependent diabetes. An "ICD group" is a range of continuous ICD codes. For example, ICD E10-E14 for diabetes mellitus. An "ICD set" is one or more ICD groups or codes combined, for example ICD C50 (breast cancer) and ICD C53 (cervical cancer).

Import - Read information into a computer program. ASCII text, CSV, TSV, DIF, 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-10 - The ten causes of death with the highest number of deaths, out of a standard National Center for Health Statistics list of 51 rankable causes. Vitalnet makes it easy to select and rank the 51 cause list.

Least-squares - A standard method for fitting the best straight line to a set of points. Produces a Y-intercept and a slope defining the least-squares line.

Mean age of death - If the ages were 50, 51, and 58, the mean age of death is (50 + 51 + 58) / 3 = 53

Multiple age groups - One age group for each table row (or column). Example: 0-19, 20-59, 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.

Natural Breaks - Method for determining map ranges. Minimizes "squared deviations from class means".

NCHS - National Center for Health Statistics. US health statistics agency. Part of the CDC.

Place of occurrence mortality data - Data compiled by the location 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 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.

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 to the observed number of deaths. The expected number of deaths is derived by applying a standard set of rates (usually state or national rates) to a population. SMRs help assess whether the mortality in a population is higher than expected.

Statistic (Main Statistic) - The basic type of numerical result displayed in a table, chart, or map. For example, birth rate, death rate, population, pregnancy rate, etc.

Table - A set of results produced by Vitalnet. A table has several parts:
 
  1. Header - basic analysis settings
  2. Data section - numerical results
  3. Bar graphs - horizontal charts
  4. Footer - other analysis settings

Tabular chart - A section of a Vitalnet table. Gives an scaleable graphical representation of the data. May be omitted from the output table.

TSV format - Tab-separated-value format. TSV files are readily imported into spreadsheet software. Each output item is separated by a tab from surrounding items, and each output text item is surrounded by "double quotes". A tab-separated-value file has "tsv" extension. Similar to CSV format.

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.

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.

Vitalnet / VitalPro / VitalWeb - Vitalnet is data warehouse / data analysis software for analyzing health data sets. VitalPro is a Vitalnet system that runs directly on a PC, for example VitalPro for Win32. VitalWeb is a Vitalnet system that runs over the internet, for example VitalWeb Ajax.

Windows - Microsoft PC operating systems. VitalPro runs 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 die "early". 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 YPLL age limit is 65.

YPLL rate - YPLL per 100,000 population in the appropriate age category. The YPLL rate up to age 65 is calculated as follows: (YPLL up to age 65) / (population for age group 0-64). Not commonly used.