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Minnesota’s counties ranked on length and quality of life

Carver County remains the healthiest county in Minnesota, and Mahnomen County remains the least healthy, according to the 2019 County Health Rankings report released Tuesday by the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute.

The two counties have held those rankings for several years now.

The differences between them in health outcomes are stark. The premature death rate, for example, is more than four times higher in Mahnomen County than in Carver County (15,700 versus 3,600 years of potential life lost before age 75 for every 100,000 residents).

And the percentage of residents who are in poor or fair health is more than twice as high in Mahnomen County than in Carver County (21 percent versus 10 percent).


As in past years, Minnesota has shown a clear north-south divide in its county rankings. Most of the unhealthiest counties are in the north, while most of the healthiest are in the south.

The 10 healthiest counties in this year’s report are (in order) Carver, Washington, Nicollet, Scott, Wright, Wabasha, Dodge, Lac qui Parie, Sherburne and Stevens. The 10 unhealthiest are (again, in order) Mahnomen, Cass, Wadena, Normal, Beltrami, Aitkin, Clearwater, Grant, Pine and Mille Lacs.

The two most populous of Minnesota’s 87 counties, Hennepin and Ramsey, ranked 41 and 64, respectively.

Overall rankings in health outcomes
County Health Rankings and Roadmaps
Overall rankings in health outcomes

Measures of future health

Those rankings are based on two types of measures: how long people live and how healthy people feel while alive.

The report also ranks the counties by health factors that affect their residents’ future health. These measures include health behaviors (such as alcohol-related deaths, access to exercise opportunities, and teen births), clinical care (such as percentage of residents without health insurance and ratio of primary care providers to residents), social and economic factors (such as unemployment rate and percentage of children living in poverty), and physical environment factors (such as average commuting-to-work time and reported drinking water violations).

The counties with the highest overall health-factor rankings are Carver, Washington, Olmsted, Scott, Stevens, Nicollet, Dakota, Rock, Brown and Douglas. Those with the lowest rankings are Mahnomen, Beltrami, Pine, Clearwater, Cass, Mille Lacs, Kanabec, Koochiching and Wadena.

Hennepin County is ranked 27th and Ramsey County is ranked 54thin this category.

In all, more than 30 measures are included in this year’s County Health Rankings report.

Disparities across counties

As the report points out, our length and quality of life are based not only on where we live, but also on our racial and ethnic background.

The authors of the report compared the health outcomes measures in Minnesota by race/ethnicity as well as by county. That comparison revealed the following troubling disparities:

  • American Indians/Alaska Natives are less healthy than those living in the bottom ranked county [Mahnomen].

  • Asians/Pacific Islanders are most similar in health to those living in the healthiest quartile of counties.

  • Blacks are most similar in health to those living in the least healthy quartile of counties.

  • Hispanics are most similar in health to those living in the least healthy quartile of counties.

  • Whites are most similar in health to those living in the middle 50% of counties.

“Our country has achieved significant health improvements over the past century,” the report points out. “We have benefited from progress in automobile safety, better workplace standards, good schools and medical clinics, and reductions in smoking and infectious diseases. But when you look closer, there are significant differences in health outcomes according to where we live, how much money we make, or how we are treated.”

“The data show that, in counties everywhere, not everyone has benefited in the same way from these health improvements,” the report continues. “There are fewer opportunities and resources for better health among groups that have been historically marginalized, including people of color, people living in poverty, people with physical or mental disabilities, LGBTQ persons, and women.”

Housing’s impact on health

This year, the County Health Rankings report took a particularly close look at housing costs.

“There is a strong and growing evidence base linking stable and affordable housing to health,” the report says. “As housing costs have outpaced local incomes, households not only struggle to acquire and maintain adequate shelter, but also face difficult trade-offs in meeting other basic needs,” such as healthful food and medical care.

The report notes that 11 percent of Minnesota’s households spend more than half of their income on housing costs. That severe housing cost burden ranges from 5 percent to 15 percent of households across Minnesota counties and from 10 percent to 25 percent among households headed by different racial and ethnic groups.

Overall rankings in health factors
County Health Rankings and Roadmaps
Overall rankings in health factors
But the burden is especially high among the 12 percent of Minnesota households with children living in poverty. Among those households, 45 percent spend more than half of their income on housing.

“Our homes are inextricably tied to our health,” says Dr. Richard Besser, president and CEO of the Robert Wood Johnson Foundation, in a released statement. “It’s unacceptable that so many individuals and families face barriers to health because of what they have to spend on housing. This leaves them with fewer dollars to keep their families healthy.”

“We are all healthier and stronger together when everyone has access to safe and affordable housing, regardless of the color of their skin or how much money they make,” he adds.

FMI: You can explore the scores of each Minnesota county in detail and compare them to other counties in the state at the County Health Rankings report’s interactive website.

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Comments (1)

  1. Submitted by Paul Udstrand on 03/21/2019 - 12:39 pm.

    These results appear to be drawn from a meta-statistical analysis that relies on a huge number of different data sets. There’s a lot of “modeling” going on here to compensate for small samples. It’s a lot of work, I’m not saying it’s garbage, but it might not be super reliable. This is NOT peer reviewed, and it’s complexity makes it very difficult to evaluate.

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