*Equal contribution. Author names listed alphabetically.

Introduction

Mental Health Awareness and the Need for Mental Health Professionals

Since 2020, there has been a significant surge in mental health awareness in the United States, with a growing emphasis on understanding and addressing emotional well-being. This heightened awareness has led to an increased demand for mental health professionals, fueled by reduced stigma surrounding seeking care. To effectively tackle these issues, it becomes crucial to improve access to mental health professionals, as doing so can help lower the number of untreated mental health cases while simultaneously promoting greater access to mental health treatments.

In this report, we analyze the relationship between access to mental health care, as measured by the number of available mental health professionals per capita, and people’s mental health status, as measured by their self-reported number of poor mental health days. In doing so, we also focus on two crucial factors that may impact the relationship.

Impact of Socioeconomic Status

According to Social Psychiatry and Psychiatric Epidemiology, instances of poor mental health have been associated with socioeconomic status (Boe et al., 2012). Socioeconomic status is defined as “the position of an individual or group determined by a combination of social and economic factors, such as income, amount and kind of education, type and prestige of occupation, place of residence, and—in some societies or parts of society—ethnic origin or religious background” (American Psychology Association, 2010). Studies have reported association between lower socioeconomic status and increased amounts of negative mental health events. It is also known that “exposure to stressful life events is related to mental health problems including depression, anxiety and substance abuse. The total number of stressors experienced by an individual may have a direct impact on mental health” (Businelle et al., 2014).

In this study, we specifically focus on two socioeconomic factors: median household income and the education level, as measured by the percentage of college graduates, aggregated over each county in the U.S. Other socioeconomic factors, such as median wages, housing prices, racial and gender composition, unemployment rate, and income inequality, may further impact the mental health variables; we leave these variables for future work.

Impact of the Affordable Care Act

In 2010, the Affordable Care Act (ACA) was implemented in the United States. The ACA has three primary goals: “make affordable health insurance available to more people, expand the medicaid program, and support innovative medical care delivery methods designed to lower the costs of health care generally” (HealthCare.gov).

The most notable changes the ACA made to healthcare was its expansion of coverage to decrease the amount of the uninsured in the United States. Its expansion also included additional mental health services, trying to address the precedent of siloed mental health care versus physical health care coverage. According to Baumgartner et al. (2020), “the ACA guarantees access to mental health services within individual, small-group (fully insured), and Medicaid expansion plans by mandating that they cover 10 essential health benefits, including mental health and prescription drugs.” Furthermore, the ACA requires all plans (including large-group) to cover preventive services like mental health screenings at no cost. Finally, insurance companies cannot deny coverage for preexisting medical conditions, cannot cancel insurance in the event of sickness, and cannot cap the amount of care received annually or during a patient’s lifetime.

The ACA has been considered controversial in American politics. While the implementation of the ACA is at the federal level, individual states can choose to reject the additional coverage that the ACA offers its residents. According to Kantarjian et al. (2014), at least 30 million of the 50 million previously uninsured US citizens were set to gain health insurance coverage, although those living in states that rejected the ACA did not ultimately get the coverage.

As of 2023, a total of 12 states have not implemented (either rejected or not implemented) the ACA. Wisconsin, Wyoming, Kansas, Tennessee, Texas, Mississippi, Alabama, Georgia, South Carolina, and Florida rejected the ACA; South Dakota and North Carolina have adopted but not implemented it (Kaiser Family Foundation, 2023). In our analysis, we focus on the binary variable of whether each state has implemented the ACA.

Topic Question and Hypothesis

The key questions and hypotheses in this report are summarized as follows.

  1. Main Question
    • How does the number of mental health providers affect the number of self-reported poor mental health days?
  2. Impact of Socioeconomic Status
    1. How does socioeconomic status impact the number of poor mental health days?
    2. How does socioeconomic status impact the relationship between mental health providers and the number of poor mental health days?
  3. Impact of the Affordable Care Act
    1. Is there a relationship between ACA implementation and access to mental health care, in terms of both the availability of mental health providers and whether or not people are insured?
    2. How does ACA implementation affect the relationship between the number of mental health providers per population and the number of poor mental health days?

We answer these questions with a combination of exploratory analysis, linear modeling, and predictive modeling.

Data and Key Variables

Throughout our analysis, we use the 2023 version of the county health rankings data for the purpose of assessing the overall health status of counties across the United States. This dataset collects various health and socieconomic variables for each county in the United States and report aggregate statistics per county. This means that all of our analysis uses data aggregated at the county level, rather than the individual or state level. The following is a glossary of the key variables we consider from the dataset.

Exploratory Data Analysis

Each of our exploratory data analysis (EDA) result corresponds to a question.

Main Question

How does the number of mental health providers affect the number of self-reported poor mental health days?

We show the relationship between poor mental health days versus the number of mental health providers as a scatterplot, along with a linear fit. Each point in the plot represents one of 3,082 counties in the U.S.

The plot demonstrates an inversely proportional relationship between the amount of health providers per capita and the amount of self-recorded poor mental health days. This is expected, as better access to mental health care should ideally decrease poor mental health days, although the relationship also appears quite weak.

Impact of Socioeconomic Status

How does socioeconomic status impact the number of poor mental health days?

We show a county-level representation of the relationship between poor mental health days and the percentage of college graduates. As before, each point in the plot represents a county in the U.S. The color of each point further indicates the percentage of current college graduates.

We see that there is a clear inversely proportional relationship between the two variables. As the mean education level increases, we expect people to experience less number of poor mental health days experiences, and the plot confirms this hypothesis. One explanation for this is that the education level correlates with many factors related to the quality of living (income; access to a stable job, housing, and healthcare; and more). This trend is expected to be similar if we substitute the education level with the median household income.

How does socioeconomic status impact the relationship between mental health providers and the number of poor mental health days?

In this plot, we redraw the relationship between poor mental health days and the number of mental health providers at the county level, but we additionally show the percentage of college graduates as the colors of each point.

There is an observed shift in gradient color representing the increase in percentage of college graduates as mental health providers per 100,000 people increases.

Impact of the Affordable Care Act

Is there a relationship between ACA implementation and access to mental health providers (in select states)?

We plot four histograms on the distribution of mental health providers per 100K population, across two ACA and two non-ACA states. ACA states are shown in Blue (New York and Louisiana), while non-ACA states are shown in Red (Florida and Alabama).

This shows that non-ACA states have more counties that have few mental health providers per capita.

Is there a relationship between ACA implementation and access to mental health providers (across all states)?

We now plot histograms on the distribution of mental health providers per 100K population, across all counties in ACA and non-ACA states. ACA-implements states are shown in Blue (New York and Louisiana), while non-implemented states are shown in Red (Florida and Alabama).

This plot shows that non-ACA states as a whole have an overall lower amount of mental health providers than ACA states.

Is there a relationship between ACA implementation and the percentage of uninsured people?

Here, we draw violin and box plots displaying the percentage of uninsured people in ACA vs non-ACA states.

This visualization shows that there are fewer uninsured individuals in ACA states than non-ACA states. The majority of ACA states have less than 10% uninsured individuals while non-ACA states are closer to 15% of its populations being uninsured. The outlier represented in the non-ACA states is Presidio county (TX), having about 37% of its population being uninsured, possibly due to outside factors not accounted for in this research.

How does ACA implementation affect the relationship between the number of mental health providers per population and the number of poor mental health days (in select states)?

We show the number of mental health providers per 100K population versus poor mental health days, specifically in the two ACA states (New York and Louisiana) and non-ACA (Florida and Alabama) states. We include a linear fit corresponding to mental health professionals versus poor mental health days in these states.

We see that, in non-ACA states, the number of available mental health providers per 100,000 people has a clear negative association with the number of poor mental health days. For ACA states, the relationship is more nuanced. In New York, the number of poor mental health days decreases as the number of mental health providers increases; however, in Louisiana, the number of poor mental health days seems to remain constant regardless of the number of mental health providers per population.

How does ACA implementation affect the relationship between the number of mental health providers per population and the number of poor mental health days (in all states)?

We now show the number of mental health providers per 100K population versus poor mental health days, across all counties in ACA and non-ACA states. As before, we include a linear fit corresponding to mental health professionals versus poor mental health days in these states.

Again, we see that, in non-ACA states, the number of available mental health providers per 100,000 people has a clear negative association with the number of poor mental health days. Having more mental health providers in non-ACA states may lead to an improved mental health status of the residents.

For ACA states, somewhat surprisingly, the relationship appears insignificant. One possible explanation is that a majority of residents in these states do have some access to mental health care, such that it is not as crucial of a factor than other quality-of-life variables.

Linear Modeling

In this section, we formally examine the linear relationships of the predictors and the outcomes examined in our EDA. We use a simple linear regression model that makes a rather restrictive assumption that the underlying relationship is linear. While far from perfect, the model allows certain inferential conclusions to be made regarding the relationships.

Basic Linear Model

## 
## Call:
## lm(formula = PoorMentalHealthDays ~ LogMentalHealthProviders, 
##     data = county_health_data_clean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.83806 -0.40267  0.01791  0.40873  2.13766 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.05318    0.05442  92.847  < 2e-16 ***
## LogMentalHealthProviders -0.10514    0.02559  -4.108  4.1e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6105 on 2892 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.005801,   Adjusted R-squared:  0.005458 
## F-statistic: 16.88 on 1 and 2892 DF,  p-value: 4.102e-05

This model examines the relationship between the number of mental health providers and the average number of recorded poor mental health days on a county based level. According to the model, the relationship is statistically significant (p-value < 0.001). The model states that a 10-fold increase in the number of available mental health care providers would result in the average number of poor mental health days recorded to decrease by .105 days. (Note that we use data from 2,892 U.S. counties, after deleting 188 counties due to lack of data.)

Poor Mental Health Days vs. (log)Mental Health Providers, controlling for socioeconomic factors

## 
## Call:
## lm(formula = PoorMentalHealthDays ~ LogMentalHealthProviders + 
##     SomeCollege + MedianHouseholdIncome, data = county_health_data_clean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.82331 -0.31256  0.02943  0.33186  1.53220 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.268e+00  5.791e-02 108.236  < 2e-16 ***
## LogMentalHealthProviders  1.565e-01  2.293e-02   6.824 1.08e-11 ***
## SomeCollege              -2.019e+00  1.114e-01 -18.131  < 2e-16 ***
## MedianHouseholdIncome    -9.740e-06  8.019e-07 -12.146  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5131 on 2890 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.2981, Adjusted R-squared:  0.2974 
## F-statistic: 409.2 on 3 and 2890 DF,  p-value: < 2.2e-16

This model examines the relationship between the (log) number of mental health providers and the average number of recorded poor mental health days on a county, while controlling for the college graduation percentage and the median household income. The model says that a 10-fold increase in the number of available mental health care providers would actually result in the average number of poor mental health days recorded to increase by 1.56 days (p-value < 0.001). This is somewhat surprising, and it reveals how the relationship may be more complex than a simple linear fit.

Poor Mental Health Days vs. (log)Mental Health Providers, controlling for ACA implementation

Here, we use a linear model with an interaction term for ACA implementation: \[ \mathrm{PoorMentalHealthDays} = \alpha_0 + \alpha_1 \cdot \mathrm{ACA} + \beta_0 \log_{10}(\mathrm{MentalHealthProviders}) + \beta_1 \cdot \log_{10}(\mathrm{MentalHealthProviders}) \cdot \mathrm{ACA}. \]

In the case where \(ACA=0\) (not implemented), \(\alpha_0\) is the intercept and \(\beta_0\) is the slope. * In the case where \(ACA=1\) (implemented), \(\alpha_0 + \alpha_1\) is the intercept and \(\beta_0 + \beta_1\) is the slope.

## 
## Call:
## lm(formula = PoorMentalHealthDays ~ LogMentalHealthProviders * 
##     ACA, data = county_health_data_clean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.87124 -0.39080  0.02236  0.39040  2.09571 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              5.49504    0.08585  64.009  < 2e-16
## LogMentalHealthProviders                -0.36292    0.04414  -8.222 2.99e-16
## ACAImplemented                          -0.64756    0.11341  -5.710 1.24e-08
## LogMentalHealthProviders:ACAImplemented  0.36395    0.05535   6.576 5.72e-11
##                                            
## (Intercept)                             ***
## LogMentalHealthProviders                ***
## ACAImplemented                          ***
## LogMentalHealthProviders:ACAImplemented ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6051 on 2890 degrees of freedom
##   (188 observations deleted due to missingness)
## Multiple R-squared:  0.02396,    Adjusted R-squared:  0.02295 
## F-statistic: 23.65 on 3 and 2890 DF,  p-value: 4.038e-15

This model examines the relationship between the number of mental health providers and the average number of recorded poor mental health days considering whether or not a state has implemented the ACA. The model displays an increased need for mental health providers in non-ACA states, as there is a significant relationship between the two variables: a 10-fold increase of mental healthcare providers may lead to a decrease in poor mental health days recorded by 0.363 days. The interaction term also models how the relationship becomes essentially uncorrelated for counties in ACA states.

Poor Mental Health Days vs. (log)Mental Health Providers, controlling for socioeconomic factors and only considering Non-ACA Implemented States

## 
## Call:
## lm(formula = PoorMentalHealthDays ~ LogMentalHealthProviders + 
##     SomeCollege + MedianHouseholdIncome, data = county_health_data_non_aca)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.71314 -0.26054  0.03652  0.29359  1.16945 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.221e+00  7.784e-02  79.924  < 2e-16 ***
## LogMentalHealthProviders -1.128e-01  3.606e-02  -3.129   0.0018 ** 
## SomeCollege              -1.532e+00  1.628e-01  -9.408  < 2e-16 ***
## MedianHouseholdIncome    -6.107e-06  1.301e-06  -4.694 3.05e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.432 on 1016 degrees of freedom
##   (100 observations deleted due to missingness)
## Multiple R-squared:  0.2666, Adjusted R-squared:  0.2644 
## F-statistic: 123.1 on 3 and 1016 DF,  p-value: < 2.2e-16

Given that the relationship is significant only with non-ACA states, we will now further examine the relationship while controlling for the socioeconomic variables. This model examines the relationship between the number of mental health providers and the average number of recorded poor mental health days in non-ACA states only, while controlling for the college graduation percentage and the median household income. This model shows that a 10-fold increase on the number of mental health providers results in a decrease in the number of poor mental health days recorded by 1.13 days, emphasizing that in non-ACA states an increase in available mental health care providers may result in the overall decrease in recorded poor mental health days.

Predictive Modeling via Random Forests

We now fit a random forest model using several predictors in the dataset. We first remove counties that have any missing entry in any of the predictors. The following shows the list of predictors used as well as the number of missing entries for each.

##             PoorMentalHealthDays                   LifeExpectancy 
##                                1                               17 
##          LongCommuteDrivingAlone               DrivingAlonetoWork 
##                                0                                0 
##            SevereHousingProblems               SocialAssociations 
##                                0                                0 
## ChildreninSingleParentHouseholds                 IncomeInequality 
##                                0                                6 
##                     Unemployment                      SomeCollege 
##                                0                                0 
##             HighSchoolCompletion            MentalHealthProviders 
##                                0                              187 
##                        Uninsured                       TeenBirths 
##                                0                              129 
##    SexuallyTransmittedInfections                ExcessiveDrinking 
##                               89                                1 
##    AccesstoExerciseOpportunities             FoodEnvironmentIndex 
##                               50                               32 
##                            State           PoorPhysicalHealthDays 
##                                0                                1 
##                              ACA            MedianHouseholdIncome 
##                                0                                1

After removing the missing rows, we are left with the following number of counties.

## [1] 2749
## 
## Call:
##  randomForest(formula = PoorMentalHealthDays ~ LongCommuteDrivingAlone +      SevereHousingProblems + SocialAssociations + ChildreninSingleParentHouseholds +      IncomeInequality + Unemployment + SomeCollege + MentalHealthProviders +      Uninsured + TeenBirths + ExcessiveDrinking + AccesstoExerciseOpportunities +      FoodEnvironmentIndex + ACA + MedianHouseholdIncome, data = county_health_data_RF,      importance = TRUE, ntree = 500) 
##                Type of random forest: regression
##                      Number of trees: 500
## No. of variables tried at each split: 5
## 
##           Mean of squared residuals: 0.1554119
##                     % Var explained: 57.34

Note that we did not include predictors capturing the physical health status. This would increase the % variance explained further. In our models, we focus on variables that are directly related to mental health and/or socioeconomic status.

#variable importance
vip(rf_model) + theme_minimal() +
  labs(title = "Variable Importance Plot, Random Forest",
       y = "Importance (% increase in mean squared error)")

The number of mental health providers per capita has the following variable importance.

## [1] 22.78494

The variable is not considered more important than some of the other variables we considered. This makes sense because variables such as excessive drinking, unemployment, and socioeconomic status can be more direct predictors of mental health problems than the availability of mental health providers. Nevertheless, the number of mental health providers is still useful for accurately predicting the number of poor mental health days, having a substantial variable importance as measured by the increase in MSE.

One of the variables that was expected to be seen in this variable importance plot is the uninsured variable. Being uninsured is likely correlated with low socioeconomic status; furthermore, we previously saw that states that did not implement ACA tend to have higher number of poor mental health days. This suggest that some individuals can be mentally unhealthy and would like to report their mental health, however they are uninsured. In such cases, the implementation of more affordable health insurance may be beneficial. This also suggests that, even though ACA is not the most important variable considered, it may still be necessary.

Discussion

Conclusion

  • Both our exploratory analysis and our simple linear model analysis demonstrate that there is a significant relationship between the number of mental health providers and the average number of poor mental health days in counties.
    • As the number of mental health providers per 100,000 people increases, the number of poor mental health days decreases.
    • We find that counties with higher percentage of college graduates have more mental heal providers per 100K people and less reported poor mental health days.
  • Counties in states that implemented ACA tend to have a higher number of mental health providers per 100,000 people and a lower percentage of uninsured people.
  • In states that did not implement ACA, the number of poor mental health days decreases as the number of mental health providers per 100K people increases, even after controlling for socioeconomic variables (college graduate % and median household income).

Limitations

  • There were 187 counties that did not input data for the number of poor mental health providers present, while other counties left multiple variables uncompleted.
  • Race and gender were two variables that we were unable to explore and examine how they had an effect on the data used.
  • Because the number of poor mental health days is self-reported, the outcome measure may not be as consistent as needed, as the number can be affected by personal and cultural backgrounds.
  • The linear model analyses rely on strong assumptions, and they should be interpreted with extra caution.

Future Work

  • A longitudinal study following up on the impact of access to mental health care, especially among states that implement alternatives to ACA (if any), can may further clarify the answer to our main question.
  • Having other relevant variables, such as the number of individuals living in a single household, could also help predict improvements that should be made to the ACA public policy.
  • For socioeconomic factors, the analysis may benefit from see more socioeconomic variables be included, such as race and gender. These variables may allow us to give more accurate predictions along with being able to build and explore other models.