"An Exploratory Study on the Dependents of

Mental Illness"

Josh CusatiTed Weil

The Mental Illness Rate is…..

The Mental Illness Rate is defined as:The number of adults who have experienced some form of serious mental illness and/or serious psychological distress within a time period – in the case of this model, a year.

Serious mental illness includes anxiety disorders, mood disorders, schizophrenia and other non-affective psychoses.

Serious psychological distress is associated with mental health problems that are not as severe as those characterized by SMI, but have a negative impact on a person’s functioning.

ObjectiveTo determine the factors or variables that drive the mental illness rate in the United States, and then through an econometric model, estimate the magnitude of the influence these variables have in their contributions to mental illness.

To provide mental health professionals with statistically sound evidence of the environmental, behavioral and economic factors that are driving mental illness rates, and their associated elasticities.

Past Research• The National Survey on Drug Use and Health

Report (The Substance Abuse and Mental Health Services Administration)• Conducted annually• Biased towards the factors that are believed to

contribute to, or be a predictor of, mental illness• No actual econometric models developed however

• Alcohol and Mental Health Factsheet (The Institute of Alcohol Studies)• Published annually• Biased towards the factors that are believed to

contribute to, or be a predictor of, mental illness• No actual econometric models developed

however

Hypotheses• H1: The mental illness rate is

dependent on personal income.

• H2: The mental illness rate is dependent on climate factors.

• H3: The mental illness rate is dependent on illicit drug use.

• H4: The mental illness rate is dependent on the unemployment rate.

MethodologyCollected demographic data from a variety of sources like the CDC, SAMHSA, and the US Census Bureau.

US Obesity RateAnnual Rate of Unemploymen

tDivorce Rate Illicit Drug Use

Rate

Public High School

Graduation Rate

% of Cigarette Smokers

Average Home Prices

US Average Household

Income (inflation adjusted)

Average Length of

UnemploymentPer Capita

Personal Income

College Graduation Rate (Bachelors in 6

years)Crime Rate Population

Percentage of Population with

Health Insurance

Mean Max Temperature (degrees F)

Mean Temperature (degrees F)

Mean Min Temperature (degress F)

Average Pecipitation

(inches)US Obesity Rate US Birth Rate

per 100 Women

Independent Variables

Dependent VariableRate of Reported Serious Psychological Distress in Past Year

Econometric Model

Using WinORS, we performed an OLS Regression on the data set. Our data was formatted for a Pooled Cross Sectional Study, as was regressed accordingly. The model was optimized to satisfy all four major assumptions of OLS Regression, and to provide a statistically significant solution that maintained constant variance and normality, while removing multicollinearity and auto-correlation.

Data & Results

Unexplained VarianceAs a requirement of a pooled cross section study we created dummy variables across pools to determine if there was any unexplained variance across pools.We chose 2006 as our base period. 4 of the 5 years that we compared to 2006 had unexplained variance and remained in our solution. The years and their associated elasticities are tabulated below.

2001 Effect 2002 Effect 2003 Effect 2004 Effect

-0.11683 -0.07811 -0.06908 -0.04377

Explanation of Seasonal Effect

The birth of Massive Payment Delinquencies, in all consumer debt level categories in late 2004 and early 2005, sparked the start of a America’s real estate market and overall economic financial crisis. The theory being that people dug themselves into debt in prior years, and everything came to a head at the beginning on 2005 when people could no longer borrow against equity in their homes to cover this debt. This pop of the bubble led to upside down equity. This debt burden and upside down equity led to an increased level of mental illness in those years. Had we had this information earlier, we might have considered including average credit score, or average debt, or even average days delinquent on debt as independent variables and we may have eliminated this yearly effect.

Conclusions3 of our 4 hypothesis were proven to be correct in that Climate Conditions, Personal Income, and Unemployment remained in the solution.Interestingly Illicit Drug was not correct. It has been commonly assumed to be correlated and it may remain so, however the drug use would be the result of mental illness and not vice versa.

Implications of ElasticitiesMean Max

Temperature (degrees F)

Mean Temperature (degrees F)

Obesity Rate (percent of population)

US Percentage of Cigarette

Smokers

Annual Average

Unemployment Rate

US Divorce Rate per

1000 population

Average Precipitation (inches)

Population

0.34898 -0.26732 -0.17586 0.16413 0.10495 0.08361 0.02873 -0.01037

LifestyleAs might be expected smoking addiction, unemployment, and divorce all contribute negatively to mental health. The obesity rate and population growth however, have an unexpectedly inverse relationship to the mental health rate.

Climate ConditionsHuman psychology prefers a narrow, optimal temperature range. The lower the max temperature, and the greater the overall mean temperature, the more positive effect on mental health. Populations that live in regions that experience less precipitation will also experience fewer incidents of mental illness or psychological distress

Finally….We proved that:

Money can indeed buy happiness

Per Capita Personal Income

Elasticity

-0.4057