The Story of Health Insurance Coverage in Gary, Indiana

• Multi-year data are now available on the Dashboard for 29 metrics!

• Explore an example using changes in insurance coverage in Gary, IN from 2013 to 2017

This June, the Dashboard added multiple years of data for 29 metrics. Some metrics, such as those from the American Community Survey, have up to 5 years of data – from 2013 to 2017.  Providing multi-year data helps cities track progress to improve factors that influence health in their communities such as cardiovascular disease, air pollution, and health insurance coverage. The data is quite eye opening when you look at the percentage of uninsured. All 500 cities across the Dashboard had a reduction in the percentage of uninsured between 2013 and 2017. To dig a little deeper, we took a look at the uninsured metric for Gary, Indiana.

Trends over Time

The uninsured metric in the City Health Dashboard captures lack of health insurance among people aged 0-64 years. As shown below, the percentage of uninsured residents in Gary decreased from 22.2% in 2013 to 15.6% in 2017.

Gary, a city of 77,000 in NW Indiana on the Lake Michigan coast, is part of a group of cities that were built by immigrant and migrant manufacturing middle-class communities. Many cities across this region share postindustrial challenges related to loss of workforce, poverty, and poor infrastructure development. Additionally, rural areas and small towns have higher percent uninsured than larger cities.  Access to affordable high quality health insurance can be critical in helping cities like Gary support its residents and improve health outcomes.

These downward trends in uninsurance are encouraging, and also consistent with state-level and national changes over time. In Indiana, subsidies for ACA and Medicaid expansion have increased the number of people eligible for free or low cost insurance and reduced percent uninsured. The decrease in uninsured percent is also seen at the national level, where states with expanded Medicaid have fewer uninsured residents.

With the Dashboard, we can also see how the patterns of insurance coverage change across different neighborhoods within Gary from 2013-2017. Census tracts in the north of the city along the shore of Lake Michigan experienced the greatest improvements in insurance coverage. Using additional data from the Dashboard, we also know that these neighborhoods saw reductions in child poverty and unemployment across the same years. However, some neighborhoods experienced an increase in percent uninsured, such as those in the center of the city. This hyper-local data demonstrate how city-level averages can sometimes hide important geographic differences.

Digging Deeper

All race/ethnicity groups had improved coverage from 2013 to 2017, with Gary’s Asian (20.2% to 8.6%) and Hispanic (24.5% to 14.7%) populations experiencing the largest increases in insurance coverage. Users can also explore changes in insurance coverage across age groups¹, which can be especially interesting given the national publicity around young adults being able to stay on their parents’ insurance plans until age 26.

Cities can use the Dashboard to look at trends and dig deeper into the other links in how where you live matters to how well you live. The Dashboard supports local leaders in their work to:

• Identify and prioritize health issues facing a city or neighborhood

• Identify demographic groups that were historically and are currently at greater risk

• Develop and implement effective policies and programs

Enjoy exploring the data across years and metrics! Additionally, don’t forget to check out our enhanced Take Action page, Metric Comparison Maps, and improved data access page – including an API that allows you to pull data directly from the site!

Learning More

¹ To understand this comparison you have to know a little about the underlying data source data. For example, it is important to note that the definition of “children” changes from 2013 (0-17) to 2017 (0-18). This change in the population definition could influence estimates over time. Details like this are important when analyzing multi-year data; be sure to consult our Using Multi-Year Data: Tips and Cautions page for guidance on understanding these data.