Interpreting Indices

The Congressional District Health Dashboard presents metrics on our site in a variety of formats: rates, percentages, averages, and indices. In this blog, we’ll explore what an index is and how to interpret one.

An index is a measure which combines two or more data points into a single number to capture more information than can be captured in a single data point. Developing an index often involves mathematical operations such as dividing two pieces of information or multiplying components by weights (for example 0.5), to adjust how much influence each data point contributes to the final score. For example, a commonly used environmental index to communicate the health risks of air pollutant concentration in a given place is the Air Quality Index (AQI). This index combines data about air pollutant concentration together with the health impacts of those pollutants. Researchers then apply a formula to translate those two pieces of information into a single score between 1 and 400, which can be used to compare air pollution-related health risk across places.

Indices are useful because they allow users to easily compare between groups, evaluate health and population interventions, or efficiently allocate resources to areas with greatest need. But while indices can be helpful, they can also be difficult to understand and interpret because they combine multiple pieces of information. On the Dashboard, four metrics are represented as indices: Income Inequality, Neighborhood Racial/Ethnic Segregation, Racial/Ethnic Diversity, and Lead Exposure Risk Index.

We’ll take you through an example of how to understand one of the Dashboard’s index metrics: the Lead Exposure Risk Index. We define this metric as an “Index (1-10) reflecting poverty-adjusted risk of housing-based lead exposure.” This tells you that the possible range of values for this index are 1 (lowest risk) to 10 (highest risk), and that information on poverty and housing-based lead exposure are used to construct this index. (You can find additional information about how the index is calculated in the Metric Background.)

Lead Exposure Risk Index combines the percentage of housing that has a potential for elevated lead risk (based on the age of the building) with the percentage of people who live in poverty in that area. Both poverty and housing age are major predictors of lead exposure, so this index provides more information on vulnerable populations than just either of these components alone. Old housing is more likely to have lead-based paint, and residents with lower incomes may struggle with the cost of maintaining older houses, leaving them more vulnerable to the health impacts of poor housing quality (which includes lead exposure). An index value that represents these factors together can help leaders efficiently determine where to direct lead abatement resources. It’s important to note that the lead risk in a given place could be influenced more by housing age or more by resident poverty; a single index value does not tell us which factor has greater influence.

The Lead Exposure Risk Index also uses a ranking system in the calculation. This means that values provide a picture of how the area in question compares to other areas, rather than an absolute measure of the lead risk in that area. Not all indices use ranking, though - it’s important to understand how each index is calculated to interpret it correctly.

Consider South Dakota’s At-large district. We can see (below) one census tract with a Lead Exposure Risk index of 10, and one with an index of 3. What does that mean about the people living in each tract? Not every person living in the tract with a value of 10 is at high risk of lead exposure, but the average person’s risk in this tract is higher than the average person’s risk in a tract with a lower value. A value of 10 means that out of all tracts in the country, this one is in the top 10% for lead risk. Compare this to a tract with a Lead Exposure Risk index value of 3, which would be in the bottom 30% of all tracts for lead risk. This comparison can help communities target lead abatement strategies where they are needed most, considering both housing age and poverty status.

It can be simpler to identify where the greatest need lies when we compare across areas using single numbers.

There are several limitations to indices. Each index is calculated differently, so each requires careful attention to interpret correctly. Another factor to consider is that the creators of an index decide which component indicators to include, and how much importance to give each one in the calculation – decisions that are somewhat subjective. Because of this, each index should be assessed for validity to ensure it accurately measures what it intends to measure. This can be done by comparing other data sources or measurement methods to verify that index values reflect the intended reality. For example, one might compare lead exposure risk index values to average blood lead levels from electronic health records; if correlated, this would suggest the index truly reflects lead exposure.

Here are some general tips to help you interpret indices:

• Confirm minimum and maximum values of the index. Determine if higher or lower index values represent “better” or “worse” outcomes.

• Check the definition of the index to understand what it measures and what data are used to create the index. On the Dashboard, you can find this information by clicking on the “More about this metric” button when you hover over the question mark next to the metric name, or in the technical document.

• Determine if the index uses a ranking calculation. If values are ranked, the number only represents where the congressional district or tract falls compared to other congressional districts or tracts in that time period. It can’t be used to track absolute changes over time.

Now that you’ve learned more about index construction and how to interpret this data, we hope you feel empowered to use the Dashboard’s indices in your work to improve health and wellbeing.