Uninsured Rates, Behavior, and Mental Health

Mental health is a vital aspect of well-being that affects millions of people in the U.S. However, not everyone has the same access to mental health care or the same risk factors for mental health problems. In this analysis, we will explore how different variables at the county level, such as drug overdoses, excessive drinking, employment status, and health insurance coverage, are related to the rates of poor mental health days among the population.

According to Healthy People 2030, one of the main goals for improving public health is to prevent and treat mental and behavioral health disorders, as well as to enhance the quality of life for those who are affected by them. Mental health disorders are among the leading causes of disability in the U.S., and having health insurance can increase the likelihood of using mental health services (Miller et al., 2016). However, there are disparities in the use and adequacy of mental health care based on race, ethnicity, and socioeconomic factors  (Jimenez et al., 2013; Tsai et al., 2014). Moreover, there is an urgent need to address the co-occurrence of mental health and drug use disorders in the U.S., which can have serious consequences for individuals and communities (Mokdad et al., 2018, Weigel et al., 2019).

The data in this analysis can help determine if there are significant associations between the rates of poor mental health days and the rates of drug overdoses, excessive drinking, employment status, and health insurance coverage at the county level. These findings can help illustrate the complex interactions between various factors that affect mental health outcomes so that policymakers and health care providers can design and implement interventions to improve mental health care access and quality for everyone.

This data is also available as an Excel spreadsheet.

Uninsured Rates, Behavior, and Mental Health Demographics 2023 (Excel) (376.67 KB)

This data represents the most current publicly available information sources that are commonly used to study health care trends. The data is derived from the data sources listed below and combined to support the analysis.

County Health Rankings (data released 2023)

A blank entry indicates unreported data. A value of zero is a defined value and does not represent unreported data.

State: The abbreviation of the state.

Population Size: The total number of individuals residing in the county. The data is derived from the Census Population Estimates from 2021.

Population Type: The population type is determined based on the population size of a specific county. The population types include metro, nonmetro cities, and nonmetro towns. These types are adapted from the rural-urban commuting area codes (RUCA) and core-based statistical areas (CBSA) definitions of rural and urban. The population types for counties are defined as follows:

  • Metro - A population of 50,000 or more
  • Nonmetro cities - A population between 2,500 and less than 50,000
  • Nonmetro towns - A population of less than 2,500

County: The name of the county. County names are listed as provided on the U.S. Census Bureau's list of 2020 FIPS Codes for Counties and County Equivalent Entities.

Percent Uninsured: The estimated number of individuals in the county under age 65 without health insurance. Data is from the Small Area Health Insurance Estimates (SAHIE) Program from 2020.

Median Household Income:  Median annual household income. Data is from the Small Area Income and Poverty Estimates (SAIPE) in 2021.

Percent Unemployment: The estimated percentage of individuals in the county who had no employment, were available for work, and had made specific efforts to find employment. The number is from the annual U.S. Bureau of Labor Statistics’ Labor Force Data in 2021.

Percent Excessive Drinking: The percentage of adults that report either binge drinking, defined as consuming more than four (women) or five (men) alcoholic beverages on a single occasion in the past 30 days, or heavy drinking, defined as drinking more than one (women) or two (men) drinks per day on average. The reporting period for this measure is 2020. Data is from the Behavioral Risk Factor Surveillance System (BRFSS) survey.

Drug Overdose Deaths:  The number of deaths due to drug poisoning per 100,000 population from the Centers for Disease Control and Prevention (CDC) WONDER mortality data from 2018 through 2020.

Teen Births: Number of births per 1,000 female population ages 15-19. The reporting period for this measure is 2014 through 2020 from the National Center for Health Statistics - Natality files.

Avg Poor Mental Health Days per Month:  The average number of days a county's adult respondents report that their mental health was poor in the past 30 days. Age-adjusted data is from the BRFSS survey from 2020.

Population Type: An indication of whether a county is predominantly metropolitan, micropolitan, or rural. 


Tutorial Video

In this tutorial video, we look at the Uninsured Rates, Behavior, and Mental Health dashboard. The video guides you through how to use Tableau data analysis to compare the rates of poor mental health days by rates of drug overdose, excessive drinking, employment status, and health insurance coverage at the county level.

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National Rural Health Resource Center

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