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Average Number of Poor Mental Health Days (Over the Last 30 Days)

Current Value

4.2

2022

Definition

Reason for Ranking

Self-reported health status is a general measure of health-related quality of life (HRQoL) in a population. Measuring HRQoL helps characterize the experience of people with disabilities and people living with chronic conditions in a population. Self-reported health status is a widely used measure of health-related quality of life. In addition to measuring how long people live, it is important to also include measures that consider how well people live. Further, reports of days of unwell mental health is a reliable estimate of recent health.

Reliability for the healthy days measures in the Behavioral Risk Factor Surveillance System is high.[1] In addition, a study examining the validity of healthy days as a summary measure for county health status found that counties with more unhealthy days were likely to have higher unemployment, poverty, percentage of adults who did not complete high school, mortality rates, and prevalence of disability than counties with fewer unhealthy days.[2] Self-reported health outcomes differ by race and ethnicity, in part, because cultural differences in reporting patterns due to different definitions of health may exist.[3] It is important to be aware of these differences when comparing across population groups.

Measure Methods

Poor Mental Health Days is an average

Poor mental health days measures the average number of mentally unhealthy days reported in past 30 days.

Poor Mental Health Days estimates are age-adjusted

Age is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. We report an age-adjusted rate in order to fairly compare counties with differing age structures.

The method for calculating Poor Mental Health Days has changed

Prior to the 2016 County Health Rankings, the CDC’s BRFSS provided the Rankings with county-level estimates that were constructed from seven years of responses from participants who used a landline phone. Beginning with the 2016 Rankings, the CDC provided single-year modeled county-level estimates that included both landline and cell phone users. Beginning with the 2021 Rankings, the CDC has updated their modeling procedure for producing small-area estimates. All of these changes were implemented in order to provide users with the most accurate estimates of health in their community as possible.

Poor Mental Health Days estimates are created using statistical modeling

Statistical modeling is used to obtain more informed and reliable estimates than survey data alone can provide. Modeling generates more stable estimates for places with small numbers of residents or survey responses. Our Poor Mental Health Days estimates are produced from one year of survey data and are created using complex statistical modeling. For more technical information on PLACES modeling using BRFSS data, please see their methodology.[4]

There are also drawbacks to using modeled data. The smaller the population or sample size of a county, the more the estimates are derived from the model itself and the less they are based on survey responses. Models make assumptions about statistical relationships that may not hold in all cases. Finally, there is no perfect model and each model generally has limitations specific to their methods.

Numerator

The numerator is the number of days respondents reported to the question "Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?"

Denominator

The denominator is the total number of adult respondents in a county.

Track Progress

This measure could be used to track progress, but only after considering its substantial limitations. Methodological changes in the Behavioral Risk Factor Surveillance System, which are discussed above and were implemented in the 2016 Rankings, make comparisons with estimates prior to that release year difficult. Additional changes to the methodology to create the estimates were implemented in the 2021 Rankings, further making comparisons with estimates prior to that release year difficult. Finally, current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress, especially in small geographic areas.

Modeled estimates have specific drawbacks with regard to their usefulness in tracking progress in communities. Modeled data are not particularly good at incorporating the effects of local conditions, such as health promotion policies or unique population characteristics, into their estimates. Counties trying to measure the effects of programs and policies on the data should use great caution when using modeled estimates. In order to better understand and validate modeled estimates, confirming this data with additional sources of data at the local level is particularly valuable.

Digging Deeper

Age
Gender
Race
Education
Income
Subcounty Area

The PLACES Project provides county-, city-, census tract-, and zip code-level small-area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including Poor Mental Health Days, across the United States.

For larger counties, you can access county- or MSA-specific data from the CDC at http://www.cdc.gov/brfss/smart/smart_data.htm. However, using this data requires somewhat advanced analytic capabilities. In many states, you can access county-level BRFSS estimates, and in some cases you can stratify those estimates by age, gender, education, income, or race. You can find BRFSS resources for most states in our Find More Data section.

References

[1] Andresen EM, Catlin TK, Wyrwich KW, Jackson-Thompson J. Retest reliability of surveillance questions on health related quality of life. Journal of Epidemiology & Community Health. 2003; 57:339-343.
[2] Jia H, Muennig P, Lubetkin EI, Gold MR. Predicting geographical variations in behavioural risk factors: An analysis of physical and mental healthy days. Journal of Epidemiology & Community Health. 2004; 58:150-155.
[3] Bombak AE. Self-rated health and public health: a critical perspective. Frontiers in Public Health. 2013; 1:15.
[4] PLACES Project. Centers for Disease Control and Prevention. Accessed March 9, 2021. https://www.cdc.gov/places.

<h3>Reason for Ranking</h3>

<section>
<p>Self-reported health status is a general measure of health-related quality of life (HRQoL) in a population. Measuring HRQoL helps characterize the experience of people with disabilities and people living with chronic conditions in a population. Self-reported health status is a widely used measure of health-related quality of life. In addition to measuring how long people live, it is important to also include measures that consider how well people live. Further, reports of days of unwell mental health is a reliable estimate of recent health.</p>

<p>Reliability for the healthy days measures in the Behavioral Risk Factor Surveillance System is high.[1] In addition, a study examining the validity of healthy days as a summary measure for county health status found that counties with more unhealthy days were likely to have higher unemployment, poverty, percentage of adults who did not complete high school, mortality rates, and prevalence of disability than counties with fewer unhealthy days.[2] Self-reported health outcomes differ by race and ethnicity, in part, because cultural differences in reporting patterns due to different definitions of health may exist.[3] It is important to be aware of these differences when comparing across population groups.</p>
</section>

<h3>Measure Methods</h3>

<section>
<h4>Poor Mental Health Days is an average</h4>

<p>Poor mental health days measures the average number of mentally unhealthy days reported in past 30 days.</p>

<h4>Poor Mental Health Days estimates are age-adjusted</h4>

<p>Age is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. We report an age-adjusted rate in order to fairly compare counties with differing age structures.</p>

<h4>The method for calculating Poor Mental Health Days has changed</h4>

<p>Prior to the 2016 County Health Rankings, the CDC’s BRFSS provided the Rankings with county-level estimates that were constructed from seven years of responses from participants who used a landline phone. Beginning with the 2016 Rankings, the CDC provided single-year modeled county-level estimates that included both landline and cell phone users. Beginning with the 2021 Rankings, the CDC has updated their modeling procedure for producing small-area estimates. All of these changes were implemented in order to provide users with the most accurate estimates of health in their community as possible.</p>

<h4>Poor Mental Health Days estimates are created using statistical modeling</h4>

<p>Statistical modeling is used to obtain more informed and reliable estimates than survey data alone can provide. Modeling generates more stable estimates for places with small numbers of residents or survey responses. Our Poor Mental Health Days estimates are produced from one year of survey data and are created using complex statistical modeling. For more technical information on PLACES modeling using BRFSS data, please see their methodology.[<a href="https://www.cdc.gov/places/methodology/index.html" rel="noopener" target="_blank">4</a>]</p>

<p>There are also drawbacks to using modeled data. The smaller the population or sample size of a county, the more the estimates are derived from the model itself and the less they are based on survey responses. Models make assumptions about statistical relationships that may not hold in all cases. Finally, there is no perfect model and each model generally has limitations specific to their methods.</p>

<h4>Numerator</h4>

<p>The numerator is the number of days respondents reported to the question "Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?"</p>

<h4>Denominator</h4>

<p>The denominator is the total number of adult respondents in a county.</p>
</section>

<h3>Track Progress</h3>

<section>
<p>This measure could be used to track progress, but only after considering its substantial limitations. Methodological changes in the Behavioral Risk Factor Surveillance System, which are discussed above and were implemented in the 2016 Rankings, make comparisons with estimates prior to that release year difficult. Additional changes to the methodology to create the estimates were implemented in the 2021 Rankings, further making comparisons with estimates prior to that release year difficult. Finally, current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress, especially in small geographic areas.</p>

<p>Modeled estimates have specific drawbacks with regard to their usefulness in tracking progress in communities. Modeled data are not particularly good at incorporating the effects of local conditions, such as health promotion policies or unique population characteristics, into their estimates. Counties trying to measure the effects of programs and policies on the data should use great caution when using modeled estimates. In order to better understand and validate modeled estimates, confirming this data with additional sources of data at the local level is particularly valuable.</p>
</section>

<h3>Digging Deeper</h3>

<table>
	<tbody>
		<tr>
			<td>Age</td>
			<td><img alt="positive trend" height="24px" src="https://www.countyhealthrankings.org/modules/custom/chrr_rankings/js/app/static/images/table-positive.png" width="24px"></td>
		</tr>
		<tr>
			<td>Gender</td>
			<td><img alt="positive trend" height="24px" src="https://www.countyhealthrankings.org/modules/custom/chrr_rankings/js/app/static/images/table-positive.png" width="24px"></td>
		</tr>
		<tr>
			<td>Race</td>
			<td><img alt="positive trend" height="24px" src="https://www.countyhealthrankings.org/modules/custom/chrr_rankings/js/app/static/images/table-positive.png" width="24px"></td>
		</tr>
		<tr>
			<td>Education</td>
			<td><img alt="positive trend" height="24px" src="https://www.countyhealthrankings.org/modules/custom/chrr_rankings/js/app/static/images/table-positive.png" width="24px"></td>
		</tr>
		<tr>
			<td>Income</td>
			<td><img alt="positive trend" height="24px" src="https://www.countyhealthrankings.org/modules/custom/chrr_rankings/js/app/static/images/table-positive.png" width="24px"></td>
		</tr>
		<tr>
			<td>Subcounty Area</td>
			<td><img alt="positive trend" height="24px" src="https://www.countyhealthrankings.org/modules/custom/chrr_rankings/js/app/static/images/table-positive.png" width="24px"></td>
		</tr>
	</tbody>
</table>

<section>
<p>The&nbsp;<a href="https://www.cdc.gov/places/index.html" rel="noopener" target="_blank">PLACES Project</a>&nbsp;provides county-, city-, census tract-, and zip code-level small-area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including Poor Mental Health Days, across the United States.&nbsp;</p>

<p>For larger counties, you can access county- or MSA-specific data from the CDC at&nbsp;<a href="http://www.cdc.gov/brfss/smart/smart_data.htm" rel="noopener" target="_blank">http://www.cdc.gov/brfss/smart/smart_data.htm</a>. However, using this data requires somewhat advanced analytic capabilities. In many states, you can access county-level BRFSS estimates, and in some cases you can stratify those estimates by age, gender, education, income, or race.&nbsp;You can find BRFSS resources for most states in our&nbsp;<a href="https://www.countyhealthrankings.org/explore-health-rankings/use-the-data/go-beyond-the-snapshot/find-more-data" rel="noopener" target="_blank">Find More Data</a>&nbsp;section.</p>
</section>

<h3>References</h3>

<section>
<p>[1] Andresen EM, Catlin TK, Wyrwich KW, Jackson-Thompson J. Retest reliability of surveillance questions on health related quality of life. Journal of Epidemiology &amp; Community Health. 2003; 57:339-343.<br>
[2] Jia H, Muennig P, Lubetkin EI, Gold MR. Predicting geographical variations in behavioural risk factors: An analysis of physical and mental healthy days. Journal of Epidemiology &amp; Community Health. 2004; 58:150-155.<br>
[3]&nbsp;Bombak AE. Self-rated health and public health: a critical perspective.&nbsp;Frontiers in Public Health. 2013; 1:15.&nbsp;<br>
[4] PLACES Project. Centers for Disease Control and Prevention. Accessed March 9, 2021.&nbsp;<a href="https://www.cdc.gov/places/index.html">https://www.cdc.gov/places</a>.</p>
</section>

Line Bar

Story Behind the Curve

Mental health continues to be significant concern which impacts the lives of many in Brown County. Gaps in mental health impacts the whole individual and subsequently the health of our community. The Brown County Community Health Assessment from 2020 highlighted a few key points related to the impacts of poor mental health:

11% of adults in Brown County reported 14 or more days of poor mental health each month.
1365 hospitalizations in Brown County due to mental health concerns.
40 deaths by suicide in Brown County.
People with depression tend to smoke more, are typically less physically active, and are 3 times less likely to be compliant with recommended medical treatment plans.
Additionally, people who are depressed are 4 times more likely to have cardiovascular disease.
Youth are also impacted by mental health concerns, with 31% of 10th graders surveyed feeling so sad or hopeless that they stop usual activities, 15% considering suicide, and more than half reporting problems with anxiety.

Supports

National launch of the 988 suicide prevention number.
Local resources and supports on www.myconnectionNEW.org
Strength of the Mental Health Taskforce carrying into this work
Connections for Mental Wellness and affiliated agencies' support in advancing this work
Mental Health First Aid trainings available in Brown County
Strong history of engagement by the Brown County Coalition for Suicide Prevention
School-based Mental Health Collaborative and implementation of robust system around bringing mental health services to youth in school.
Collaborative spirit around partnering to build mental health systems in Brown County.

Limiting Factors

Provider capacity and staffing shortages, both in Wisconsin and locally.
Access to providers of color and culturally responsive services in Brown County is limited, especially for advanced mental health services, mirroring state and national trends.
Need for onsite services that can't always be met due to capacity constraints which restrict systems development.
Insurance and cost barriers to receiving services.
Clinical requirements to hire on directly as schools can be restrictive, alongside other procedural barriers to equitable access.
Lack of sustainable funding for mental health services and positions.

Partners

Organizations currently engaging in this important work in Brown County include:

Bellin Health
Advocate Aurora
Hospital Sisters Health Systems
Prevea Health
N.E.W. Community Clinic
Connections for Mental Wellness
School-Based Mental Health partners
Many additional community organizations and partners support this work through ongoing programming and initiatives
Green Bay Packers

Opportunities for further partnership:

The Veterans' Administration
Oneida Nation
Additional mental health service providers
Insurance companies/payees

What Works

Potential strategies to consider for implementation:

Collaborative data sharing agreements to facilitate initiatives.
Discuss adoption of agreed upon measures to understand mental health and screening rates in Brown County.
Utilize data/analytics to understand and drive decision-making, stratifying data whenever possible to uncover disparities.
Leverage the use of electronic medical records (EMRs) for collaboration.
Partner with statewide organizations to more deeply understand health disparities (Data sharing platforms such as WHA/WCHQ)
Building partnerships with the Veterans Administration, jail and prison systems, and other local clinical partners to build strong mental health systems and further establish "no wrong door" approach.
Share advocacy opportunities as they come up across various networks.
Increase depression screening rates as a coordinated initiative for clinical partners in Brown County.
School-based "health in all policies" approach to mental health days for students without needing a doctor's note.

Strategy

In 2022 and beyond, the organizations leading this strategy work are committed to:

Engaging in enhanced data collection and alignment discussions to reflect timely, local, and accurate mental health screening rates in Brown County.
Exploring data sharing agreements to understand and drive decision-making.
Building partnerships with the Veterans Administration and other local clinical partners not historically included in data collection methodologies.
Leverage the use of electronic medical records (EMRs) for collaboration.
Partner with statewide organizations to more deeply understand health disparities (including data-sharing platforms such as WHA/WCHQ)
Develop action plans which capture shared aims and change ideas.

Clear Impact Suite is an easy-to-use, web-based software platform that helps your staff collaborate with external stakeholders and community partners by utilizing the combination of data collection, performance reporting, and program planning.