Using Multi-Year Data: Tips and Cautions

The Dashboard displays multiple years of data for many of its metrics, and users may see changes over time in some of them. These changes can be caused by many factors, including real changes in the outcome of interest, as well as changes in population demographics, changes in data collection methods, or others. Furthermore, it may be hard to interpret year-to-year changes for many reasons, including overlapping confidence intervals*. Because it is hard to know what drives differences from year to year in individual datasets, and how to interpret those changes, it is important to understand the underlying dataset from which the metric was derived. Users should keep the characteristics of the relevant underlying dataset (described below) in mind when interpreting changes in specific measures over time.

The Dashboard team does not recommend using multi-year data to evaluate the impact of local public health programs or polices over time. In most cases, the underlying datasets cannot pick up the effects of local initiatives on a year-to-year basis. This is due to time gaps between when data are collected and when they are made available, statistical methods used to create some estimates, and because some estimates are based on grouped years of data.

Data sources not listed on this page do not provide multi-year data, and so are not included.

For more information on any of these data sources and associated metrics please review our Technical Documents and Metrics Background. Please contact [email protected] with any questions.

Underlying datasets on which multi-year data are based:

American Community Survey (ACS)

ACS data are used to calculate eleven of the Dashboard’s metrics. The Dashboard presents five-year estimates, representing the combination of data collected over a five-year period. For example, a 2017 estimate actually is a pooling, or combination, of information collected by the US Census Bureau from 2013, 2014, 2015, 2016 and 2017. For further explanation and examples please refer to the U.S. Census Bureau’s publication, “Understanding and Using ACS Single-Year and Multiyear Estimates.”

Dashboard measures derived from the ACS dataset:

  • Children in poverty

  • Broadband connection

  • High school completion

  • Housing cost, excessive

  • Housing with potential lead risk

  • Income inequality

  • Lead exposure risk index

  • Neighborhood racial/ethnic segregation

  • Racial/ethnic diversity

  • Unemployment – annual, neighborhood-level 

  • Uninsured

Local Area Unemployment Statistics, U.S. Bureau of Labor Statistics (BLS)

Data from the BLS are used to produce the Unemployment – current, city-level metric. These data are derived through monthly surveys conducted by the BLS. These surveys gather information from different people each month. As such the BLS survey is meant to capture unemployment for a given month in a given city and does not necessarily measure how unemployment has changed for a specific group of people over time.

Dashboard measures derived from the BLS dataset:

  • Unemployment – current, city-level

National Vital Statistics System (NVSS)**

Data from NVSS are used to calculate eight of the Dashboard’s metrics. Similar to ACS metrics, NVSS estimates use multiple years of data – three years, to be exact.

Dashboard measures derived from the NVSS dataset:

  • Breast cancer deaths

  • Cardiovascular disease deaths

  • Colorectal cancer deaths

  • Low birthweight

  • Opioid overdose deaths

  • Premature deaths (all causes)

  • Prenatal care

  • Teen births

**New Jersey State Health Assessment Data (NJSHAD) are used in lieu of NVSS data for a subset of ten New Jersey cities.

Uniform Crime Reporting (UCR) Data

The way in which multi-year UCR data is reported has varied over time.  The quality of data within a city can also vary over time.  Please use particular caution when comparing UCR data across years.

Dashboard measures derived from the Uniform Crime Reporting dataset:

  • Violent Crime

Food Access Research Atlas, Economic Research Service, United States Department of Agriculture (USDA Food Atlas)

Grocery store locations are updated for each year of data, while population counts come from the 2010 Census for all years of data available. In 2015 this dataset did not distinguish between 0 values at the tract level and missing values. Their methods were later updated, and 2019 missing values are designated as NA. Due to this, caution is needed when comparing 2015 data to future years, and when interpreting 0 values at the tract-level.

Dashboard measures derived from the USDA Food Atlas dataset:

  • Limited Access to Healthy Food

EPA Community Multiscale Air Quality Modeling System (CMAQ)

CMAQ data are produced by a network of air quality censors throughout Dashboard cities. The number of censors vary across cities, and some cities have a low number of censors. As such, multi-year EPA CMAQ data should not be used to evaluate the impact of local public health programs or policies, similar to all datasets and metrics on the Dashboard.

Dashboard measure derived from the EPA CMAQ dataset:

  • Air Pollution

PLACES Project (formerly 500 Cities Project)

Data from the CDC’s PLACES Project (formerly 500 Cities Project) are used for 10 of the Dashboard’s metrics. These estimates are calculated differently from most other measures on the Dashboard and therefore have different multi-year data use caveats. According to the PLACES Project, “the current modeling procedure does not support using the estimates to track changes at the local level over time.” The Dashboard added PLACES data to the website on March 1, 2021. These data represent an update to the 500 Cities data, with some small methodological changes. One change is that PLACES no longer releases estimates for the portion of census tracts that is located within city boundaries. For this reason, users should use additional caution when comparing PLACES data to 500 Cities data from previous years. For more information please visit the PLACES Project’s Using the Data webpage. Note that data pre-2018 will be unavailable for cities included in PLACES, but not in 500 Cities. Dashboard measures derived from the PLACES (formerly 500 Cities) dataset:

  • Binge drinking

  • Dental care

  • Diabetes

  • Frequent mental distress

  • Frequent physical distress 

  • High blood pressure

  • Obesity

  • Physical inactivity

  • Preventive services, 65+

  • Routine checkup

  • Smoking

*TIP – Interested in digging deeper into the Dashboard’s confidence interval information? Confidence intervals for our estimates are available through our API and in our city and tract downloadable files.

Last updated: November 1, 2021