Explore the mortality rate maps

In a January 2021 article, the Breast Cancer Research Foundation reported that that while the rate of new cases of breast cancer between White and Black women are more similar, the death rate among Black women diagnosed with breast cancer is 40 percent higher than the death rate for White women. Because of lack of health care and preventive measures on top of social, economic, and behavioral factors, the gap between new breast cancer cases and outcomes is complex. The first version of this lesson analyzed data available in 2013. Although the disparity between Black women and other races and ethnicities is narrowing, Black women continue to experience the highest proportion of deaths due to breast cancer.

Chart showing female breast cancer mortality over time

For this lesson, you are a GIS analyst for a research advocacy group whose mission is to close the racial inequity gap of breast cancer mortality rates in the United States. Your first goal in this lesson is to confirm there is a racial disparity gap for breast cancer mortality rates. To do so, you'll map available data on breast cancer mortality rates for Black and White women. In some parts of the United States, mortality data has been suppressed, hidden, or simply unavailable by the data provider. Data can be suppressed due to high rates of unreported numbers or privacy concerns. Your goal is to create a map you can share with partnering advocacy organizations and elected officials to raise awareness and advocate for additional resources to address this important public health need.

Note:

For this lesson, you will only analyze two racial groups. It is considered best practice to consider data for all racial and ethnic groups in a given population when conducting a racial equity analysis. You are encouraged to use the workflow of this lesson to analyze the disparity between other racial and ethnic groups on your own.

Map breast cancer mortality

To view the data, you'll create a map of your area of interest in ArcMap. Then, you'll add mortality data for Black and White women and compare it.

  1. Open ArcMap and click Cancel in the Getting Started window.

    A blank map document opens.

  2. On the ribbon, click the arrow next to Add Data and choose Add Basemap.

    Add Basemap

  3. In the Basemap window, choose Light Gray Canvas and click Add.

    Light Gray Canvas basemap

    The Light Gray Canvas basemap is added to the map. Next, you'll add your data.

  4. Click the Add Data drop-down arrow and choose Add Data from ArcGIS Online.
  5. In the Add Data window, search for breast cancer owner:Learn_ArcGIS. For Breast Cancer Mortality Rate by Race 2018, click Add.

    Add breast cancer data.

    The Breast Cancer Mortality Rate by Race 2018 layer appears on the map.

  6. On the map, zoom in to the contiguous United States.

    The default symbology shows the overall mortality rate for breast cancer in the United States. Mortality rate is the number of deaths per 100,000 people and is calculated using the formula Mortality Rate = (Cancer Deaths / Population) × 100,000.

    Counties symbolized in darker pink colors have higher mortality rates, and lighter pinks represent lower mortality rates. Counties on this map that aren't filled in show where data has been suppressed by the provider. Data is suppressed when there are too few cases in each county to maintain individual privacy.

    First, you'll explore the data.

  7. In the Table Of Contents, right-click the MortalityRate2018 layer. In the menu, click Open Attribute Table.

    Open Attribute Table

    The attribute table shows all the attribute data contained in the layer. Scroll right until you see the mortality rate columns. Notice that there are a lot of zeros; this is the placeholder value for the suppressed data.

    Next, you'll change the symbology to view and compare the available data.

  8. Close the attribute table.
  9. In the Table Of Contents, double-click the MortalityRate2018 layer. The Layer Properties window appears. If necessary, click the Symbology tab.

    Symbology tab

  10. Under Fields, for Value, choose Black Mortality Rate.

    Value set for Black Mortality Rate.

    This is the mortality data for Black women. All the suppressed data zeros have their own category by default, but these don't need to be shown.

  11. Under Symbol, double-click the first color symbol box.

    First symbol

  12. In the Symbol Selector window, click Fill Color and choose No Color.

    Symbol Fill Color set to No Color

  13. Click OK in both windows.

    Reported county data for Black women

    The counties that have reported data for Black women are shown in different shades. Most of the available data is for counties and county equivalents in the east and southeast of the country. In all other areas, there are large gaps in the data. Counties on this map that aren't filled in show where data has been suppressed by the provider.

  14. If necessary, in the Table Of Contents, expand the Breast Cancer Mortality Data layer's legend.

    Layer legend

    The legend indicates that from 2014 through 2018, the breast cancer mortality rates for Black women were as high as 58.5 average annual deaths per 100,000 Black women in a single county (Adams County, Mississippi).

    Before you change the symbology to show the breast cancer mortality rate for White women, you'll create a copy of the layer that has been symbolized for the mortality rate for Black women so that you can more easily compare the two layers.

  15. Right-click MortalityRate2018 and choose Copy.
  16. Right-click Layers and choose Paste Layer(s).

    An identical layer is added to the Table Of Contents. The copy is not a new file that's been saved to your computer, but a layer that exists only in the map document. When you close the project, the layer data won't be saved.

  17. Click the copy layer's name twice to edit it. Type Black Mortality Rate, and uncheck the layer to turn it off.

    Change layer name.

  18. Click the original layer's name twice to rename it as White Mortality Rate.

    Change name of original MortalityRate2018 layer.

  19. Double-click White Mortality Rate, and on the Symbology tab, change Value to White Mortality.

    To better visually compare the layers, you'll change the color ramp.

  20. For Color Ramp, choose a light purple to dark purple ramp.
  21. Change the fill color for the zero category to No Color .

    Purple ramp

  22. Click OK.

    Reported county data for White women

    While both maps for breast cancer mortality rates for White and Black women do not have available data for every county in the United States, more data is available for White women. The legend also indicates that the range of mortality rates for White women is lower, with rates up to 49.8, than the range for Black women, with rates up to 58.5.

    By comparing the rates of breast cancer deaths for White and Black women, there is strong indication that a higher proportion of Black women are affected by breast cancer mortality compared to White women.

  23. On the ribbon, click Save. Name the map Breast Cancer Mortality.

Classify data

Next, you'll look at the classification statistics for both Black and White women to determine whether the information proves that there is a gap in breast cancer mortality rates.

  1. In the Table of Contents, double-click the White Mortality Rate layer.

    The default classification is Natural Breaks (Jenks). Jenks breaks the data into classes using a combination of statistical measures (mean, median, and quantiles) and gaps that exist naturally in the data. Since no two datasets are the same, this creates unique classes. To better compare the mortality rates, you'll want to use a more uniform classification technique and exclude all the counties with suppressed data.

  2. Under Classification, click Classify.

    Classify button

    The Classification window appears.

  3. Change Method to Quantile and change Classes to 4.
  4. Under Data Exclusion, click Exclusion.

    Exclusion button

    The Data Exclusion Properties window appears. Excluding data allows you to specify the values you don't want symbolized on the map. Next, you'll create a query to exclude all the null values from the suppressed data.

  5. Double-click the values in the query wizard to build the expression “White_Mortality” = 0.

    Data Exclusion Properties window

  6. Click OK twice, and in the Layer Properties window, click OK to show the changes.
  7. Double-click the White Mortality Rate layer and click Classify.

    The Classification window appears. Under Classification Statistics are statistics on all the data points in the layer.

    White classification statistics

    Quantiles are a data classification method that separates data values into four categories with equal numbers of observations. Now that the suppressed data has been removed, the available data is accurately categorized. The classification statistics indicate there are 1,581 counties with data for White women, which is about 49 percent of all 3,220 United States counties and county equivalents. Additionally, the statistics indicate that the average breast cancer mortality rate for White women is about 21 per 100,000.

  8. Close the classification and symbology windows and turn off the White Mortality Rate layer.
  9. Turn on then double-click the Black Mortality Rate layer.
  10. In the Layer Properties window, click Classify. Then change Method to Quantile and change Classes to 4.
  11. Click Exclusion and create the query “Black MortalityRate_Num” = 0. Click OK three times.
  12. Double-click the Black Mortality Rate layer and click Classify.

    Black classification statistics

    For Black Mortality Rate, there is data for 341 counties (just over 10 percent of all counties and county equivalents). The mean mortality rate for Black women is 28.88, a rate nearly 8 deaths higher than for White women. Compared to the available data for 2015, the average breast cancer mortality rate disparity between Black and White women was nearly 10 deaths per 100,000. The disparity in 2012 was about 9 deaths per 100,000. While the disparity has been decreasing, it continues to persist.

  13. Close the Classification and Layer Properties windows, and on the ribbon, click Save.

While there is more data available for White women, the range of the data values you mapped suggests the article's claims are true: Black women have higher breast cancer mortality rates than White women. Next, you will map the degree to which breast cancer mortality rates differ between Black and White women.


Map the mortality rate difference

Previously, you looked at counties that had data for both groups of women. Next, you'll map the difference in breast cancer mortality rate between the two groups.

Identify dual-data counties

To quantitatively compare mortality rates for Black and White women, you need to know what counties have data for both groups. You'll find these counties by creating an SQL query to select counties that meet the criteria.

  1. If necessary, open your Breast Cancer Mortality map document.
  2. In the Table Of Contents, right-click the Black Mortality Rate layer and open the attribute table.
  3. Click the Table Options button and choose Select By Attributes.

    Select By Attributes

    The Select By Attributes window appears with a wizard, similar to the window you built the exclusion query in earlier. To perform analysis, you want only counties that have mortality data for both Black and White women.

  4. Click attributes and operators to build the query "White_Mortality" > 0 AND "BlackMortalityRate_Num" > 0. Click Apply.

    Select By Attributes query

  5. Click Show Selected Records to see your data.

    Show Selected Records

    Data for both Black and White women has been provided by 318 counties and county equivalents. Next, you'll export these counties into a new layer.

  6. In the Table Of Contents, right-click the layer, point to Data and choose Export Data.

    Export selected data.

  7. In the Export Data window, for Output feature class, type Dual_Data_Counties and save it in the default geodatabase.
  8. Click OK. In the pop-up that asks to add the exported data to the map, click Yes.

    The new layer draws on the map.

  9. Close the Attribute Table and turn off all layers except Dual_Data_Counties.

    Dual_Data_Counties layer

    These are the counties for which you can run comparisons.

  10. Save the map.

Calculate mortality rate difference

  1. Right-click the Dual_Data_Counties layer and click Open Attribute Table.

    To calculate the difference in mortality rates, you'll create a column in the table.

  2. Click Table Options and choose Add Field.

    Add Field

    The Add Field window appears.

    In the Add Field window, you can specify how the field's information is stored and displayed. Type specifies how the data will be saved. Choosing Short Integer allocates the least amount of storage to the field and only accepts integer values up to five digits. To store larger numbers or numbers with fractional values, use Long Integer, Float, or Double as the type. Aliases are alternative names given to fields as a more user-friendly description of the content.

  3. In the Add Field window, name your new field Mortality_Rate_Difference. Leave Type set to Short Integer. For Alias, type Mortality Rate Difference.

    New field properties

  4. Click OK.
  5. In the attribute table, scroll to the end of the table to confirm that the Mortality Rate Difference field has been added.

    Mortality Rate Difference

    Because it's a new field, the data values are null, or not yet defined. Next, you'll calculate data values based on the data in other columns. For this analysis, you're interested in the difference between Black female mortality rates and White female mortality rates. To find the difference, you'll subtract White female mortality rates from Black female mortality rates using the Field Calculator.

  6. Right-click the Mortality Rate Difference heading and choose Field Calculator.

    Field Calculator

    A window may appear, warning you that you are about to perform a calculation outside of an edit session. This warning means that once you have calculated the data, you can't undo it. Because you're calculating a new field, you're not overwriting any old data, so you will not need to undo your calculation.

  7. In the Field Calculator window warning, click Yes.
  8. In the Field Calculator window, build the expression [BlackMortalityRate_Num] – [White_Mortality].

    Field Calculator expression

  9. Click OK.

    Data values are added to the Mortality Rate Difference column in your table. Most of the numbers in this column are positive, meaning that the mortality rate for Black females is higher than for White females. Scattered through the data, though, are zeros and negative numbers. In these counties, the mortality rate is the same or higher for White females.

    Now that you have calculated the differences, you'll map them.

  10. Close the attribute table and save the map.

Symbolize the values

Next, you'll symbolize the differences you just calculated to visualize them on the map. The Breast Cancer Data layer currently shows all of its features using a single symbol. You'll display the features using the Mortality Rate Difference field to show where in the United States the gap exists.

  1. In the Table Of Contents, double-click the Dual_Data_Counties layer. If necessary, click the Symbology tab. Under Show, click Quantities.

    Graduated colors

  2. Under Fields, for Value, choose Mortality Rate Difference.

    When you calculated this field, you determined that negative numbers belonged to counties that had higher mortality rates for white women, zeros showed an equal mortality rate, and positive numbers were counties that had a higher mortality rate for Black women. You'll reduce the number of classes to three to show these categories on the map.

  3. Under Classification, for Classes, choose 3. Click Classify.

    The Classification window appears.

  4. Under Break Values, click the first break value and change it to -1. Change the second break value to 0 and leave the third unchanged.

    Break Values

    The blue lines on the bar chart represent the breaks you have set. By changing the breaks, you now have three categories that represent your three categories of interest.

  5. Click OK.

    The three categories are shown with new breaks, but the monochrome color ramp will make them difficult to distinguish on the map.

  6. In the Layer Properties window, double-click the Label for the first symbol.

    First symbol

    The Symbol Selector window appears.

  7. Click Fill Color and choose Blue Gray Dust.
    Tip:

    To view a color name, point to the color.

    Blue Gray Dust

  8. For the second symbol, change the Fill color to Yucca Yellow. For the third symbol, change the Fill color to Tudor Rose Dust.
  9. In the Layer Properties window, in the Label column, click the label and type Mortality Rate higher for White women. Change the label for 0 to Mortality Rate equal between White and Black women, and change the label for the third symbol to Mortality Rate higher for Black women.

    Symbol range labels

  10. Click OK.

    The map now shows the counties symbolized with the new colors.

    Mortality Rate Difference map

    The majority of counties that have reported data have higher breast cancer mortality rates for Black women compared to White women. This map shows where the variation in mortality rates occurs, demonstrating that the racial disparity in breast cancer mortality rates is a national problem.

  11. Save the map.

You've analyzed data showing breast cancer mortality rates for Black and White women and symbolized it to show the large scope of the problem. Next, you will map the breast cancer mortality rates as a rate ratio and identify hot spots of high and low racial disparities.


Map the mortality rate ratio

Previously, you mapped the differences in breast cancer mortality rates for Black and White women. Next, you'll determine how wide the mortality rate gap is in each county. You'll calculate this gap using a rate ratio, which compares rates (of mortality, in this case) in two groups that differ by demographic characteristics (race, in this case). The rate for the primary group of interest (Black women) is divided by the rate for a comparison group (White women). Using this statistic, you'll perform a hot spot analysis to map where the mortality rates are highest. Based on your results, your cancer research advocacy group will know in which areas of the country to focus its campaign.

Calculate the mortality rate ratio

Now that you've seen the difference in mortality rates, you'll calculate another statistic, the rate ratio. The ratio gives the likelihood of an outcome for a specific group. In other words, a rate ratio of 5 in a specific county would mean that a Black woman with breast cancer in that county is five times more likely to die of the disease than a White woman.

First, you'll calculate the ratio of deaths due to breast cancer between Black women and White women using the following formula:

Rate ratio = Rate for Black women / Rate for White women

Then, you'll use this calculation in a hot spot analysis. Using the rate ratio identifies counties in which the mortality rate for Black women is significantly higher, instead of counties where the mortality rate is high for both Black and White women.

  1. If necessary, open your Breast Cancer Mortality map document in ArcMap.

    To perform your calculation, you need information about mortality rates. The Mortality Rate Difference layer you used earlier in the lesson contains mortality rate data. You want to use these attributes, but don't want to alter the layer you've made. To keep this layer and edit one with the same data, you'll make a copy of it.

  2. In the Table Of Contents, right-click the Dual_Data_Counties layer and click Copy.
  3. Right-click Layers and choose Paste Layer(s).

    A copy of the layer is added to the Table Of Contents. The copy is not a new file that's been saved to your computer, but a layer that exists only in the map document.

  4. Turn off the original Dual_Data_Counties layer. Rename the copy Mortality Rate Ratio.

    For this map, the mortality rates for Black women are divided by the rates for White women. This value will tell you how much more likely Black women are to die from breast cancer than White women are. You'll add a new field to the attribute table and calculate the values in the field to show the rate ratio.

  5. In the Table Of Contents, right-click the Mortality Rate Ratio layer and choose Attribute Table.
  6. Click Table Options and choose Add Field.
  7. In the Add Field window, enter the following:
    • For Name, type Mortality_Rate_Ratio.
    • For Type, choose Double.
    • For Alias, type Mortality Rate Ratio.

    Add Field

    Note:

    The Double type allows the field to store decimal values.

  8. Click OK.
  9. Right-click the new Mortality Rate Ratio field and choose Field Calculator. If necessary, click Yes in the warning window that appears.
  10. In the Field Calculator window, create the expression [BlackMortalityRate_Num] / [White_Mortality] and click OK.

    The rate ratio calculations are added to the new field.

  11. Right-click the Mortality Rate Ratio header and choose Sort Descending.

    Sort the table.

    Most of the counties have values greater than 1, which means the mortality rate is higher for Black women. Counties with values less than or equal to 1 are those in which the mortality rate is the same or higher for White women as for Black women.

  12. Close the attribute table.

    Next, you'll symbolize the values you calculated to visualize where the rates are higher or lower.

Symbolize the values

To symbolize the difference, you'll use graduated symbology in which the size of a county's circle relates to the magnitude of the value you're mapping. Graduated symbols on a map change sizes according to the value of the attribute they represent. For instance, a county with a higher rate ratio would be symbolized with a larger shape than a county with a smaller rate ratio. You want to make sure that the counties in which Black women have lower mortality rates are clearly distinguished from all others. To symbolize this distinction, you'll use a different color.

  1. In the Table Of Contents, double-click the Mortality Rate Ratio layer. If necessary, in the Layer Properties window, click the Symbology tab.
  2. Click Quantities and choose Graduated symbols.

    Graduated symbols

  3. In the Fields group, for Value, choose Mortality Rate Ratio.

    Graduated symbology

  4. Under Classification, click Classify.

    The Classify window appears. Currently, the data is broken into three classes based on the Natural Breaks (Jenks) method. This classification method doesn't automatically separate the data into the three categories of mortality rates you want to symbolize.

  5. Change the number of classes to 5.

    In the previous section, you had three categories because you symbolized the distribution of data into the three kinds of mortality rates: greater for Black women, equal, and greater for White women. This time, you'll symbolize the range of mortality rates, so you'll break the data into five classes. Remember, values less than or equal to 1 are counties in which the mortality rate is the same or higher for White women as for Black women.

  6. For Break Values, change the values to 11.52, 2.5, and 3.5.

    Break Values for mortality rate ratio

  7. Click OK.
  8. In the Symbology window, click the symbol under Template.

    Template symbol

  9. In the Symbol Selector window, click Circle 2.

    Circle 2 symbol

  10. Change the Color to Tudor Rose Dust.
  11. Click OK.

    Symbol Selector window

    The counties that you've symbolized still have a background under the circle symbol. The background is unnecessary and potentially distracting.

  12. In the Symbology window, click the symbol under Background.
  13. Change the Fill Color and Outline Color to No Color and click OK.

    Fill Color and Outline Color settings

  14. In the Symbology window, change the values for Symbol Size to 9 to 20.

    Symbol Size

  15. In the Symbology window, double-click the first symbol.
  16. Under Current Symbol, click Color. Choose Blue Gray Dust.

    Symbol sizes

    Last, you'll change the labels that go with your graduated symbols. Each label has an excessive number of decimal places, which takes up space.

  17. Under Symbol, click Label and choose Format Labels.

    Format Labels

  18. For Rounding, change the Number of decimal places parameter to 1. Click OK.

    Number of decimal places

    The labels change to include only a single decimal place.

  19. Click OK to finish applying all the new symbology.

    Mortality Rate Ratio map

  20. In the Table of Contents, under Mortality Rate Ratio, click the heading name twice and type Rate ratio between Black and White women.

    Changed heading

  21. Save the map.

    This map shows the rate ratio between average mortality rates for Black and White women for the years 2014 to 2018 for each county in the United States. Larger circles are counties where the breast cancer mortality rate among Black women is higher than the mortality rate for White women. Values under 1 (shown in blue) are counties where the breast cancer mortality rate among White women is equal to or greater than the mortality rate among Black women.

    This part of your investigation shows that the vast majority of counties with available breast cancer mortality rate data have higher mortality rates for Black women compared to White women. Calculating rate ratios was not only informative, it was a necessary step to finding hot spots. Next, you'll perform hot spot analysis to find statistically significant clusters of rate ratio values. Knowing where the clusters are concentrated can aid in your advocacy organization's efforts to raise awareness and increase resources to address racial disparity.

Perform hot spot analysis

Based on your rate ratio map, it appears that most counties with the highest rates are located in the south central and eastern coastal parts of the United States. To confirm this, you'll run a hot spot analysis.

Hot spot analysis finds statistically significant clusters of high and low values. By running the hot spot analysis on the rate ratio values, you will identify hot spots where the mortality rate ratios are high and spatially near one another, and any cold spots that show spatial clusters of low mortality rate ratios. The output from the Hot Spot Analysis tool provides a statistically confident measurement that there is clustering of high or low values. A hot spot with a 99 percent confidence level, for example, means there is only one chance out of 100 that a tight spatial cluster of high values is due to random chance. Identifying a spatial cluster of high values that is statistically significant provides confidence that the clustering is not the result of some random process, but rather suggests other factors may be contributing to the higher concentration of disparities (such as differences in environment, local land use policy, access to quality health care, genetics, life style choices, and so on).

First, you'll ensure there are no null values in your data. Null values can make the analysis results inaccurate.

  1. In the Table Of Contents, double-click the Mortality Rate Ratio layer. In the Layer Properties window, on the Definition Query tab, click Query Builder.

    Query Builder

    Query Builder is like the Field Calculator you used earlier. You'll build an expression the same way, but instead of doing a calculation, the query selects the specified results.

  2. In the Query Builder window, build the expression Mortality_Rate_Ratio IS NOT NULL.

    Query Builder expression

  3. Click OK in the Query Builder window and click OK to close the Layer Properties window.

    If there were any null values in your data, they have been removed by this query.

  4. On the ribbon, click Geoprocessing and choose ArcToolbox.

    ArcToolbox button

  5. In the ArcToolbox pane, click Spatial Statistics Tools to expand the toolbox.
  6. Expand the Mapping Clusters toolset and double-click the Optimized Hot Spot Analysis tool.

    Optimized Hot Spot Analysis tool

    There are two hot spot tools to choose from. Optimized Hot Spot Analysis works better in this case because it processes your data to choose appropriate parameter settings.

    You'll run the tool using your Mortality Rate Ratio layer. What this creates is statistically significant clusters of high and low values. Using the Getis-Ord Gi* statistic (pronounced G-i-star), the Hot Spot Analysis tool identifies statistically significant hot and cold spot areas. Features are binned by confidence level (the Gi_Bin field) so that hot spots that are statistically significant at the 99 percent confidence level are bright red and have a Gi_Bin value of 3. Similarly, hot spots that are statistically significant at the 95 percent confidence level are medium red and have a Gi_Bin value of 2. Gi_Bin values range from 3 to -3, where -3 is associated with statistically significant cold spots at the 99 percent confidence level (the darkest blue color on the map).

  7. In the Optimized Hot Spot Analysis window, set Input Features to Mortality Rate Ratio and set Analysis Field to Mortality_Rate_Ratio.
  8. For Output Features, type Optimized_Hot_Spots and save it in the default geodatabase.

    Optimized Hot Spot Analysis parameters

  9. Click OK.

    Once the analysis finishes running, the result layer is automatically added to the map as a layer.

    Hot Spot Analysis map

  10. In the Table of Contents, turn off the Mortality Rate Ratio layer. If necessary, expand the Optimized_Hot_Spots layer to see the layer legend.

    Optimized_Hot_Spots layer legend

    For the counties with available data on breast cancer mortality, your analysis identified clusters of disproportionately high mortality rates among Black women compared to the mortality rate of White women. There is a hot spot clustered in the south central region of the United States, similar to the patterns identified in the Mortality Rate Ratio map. But the cluster around North Carolina and South Carolina was not easily visible in previous maps. There is also a distinct cold spot cluster in the northeast, indicating there was less of a racial disparity in the breast cancer mortality rate between Black women and White women in this region.

This map confirms that there is clustering of breast cancer mortality rate disparities between Black and White women. Although you were only working with limited reported data for breast cancer mortality rates, you were able to analyze valuable information that will aid your organization's efforts to reduce breast cancer mortality rates.

You can find more lessons in the Learn ArcGIS Lesson Gallery.