Community Mental Health Journal
Volume 2, Number3, Fall, 1966
RELATIONSHIP VARIABLES IN
DAVID J. VAIL, M.D., R. JOSEPH LUCERO, M.A., ANYJAMES R. BOEN, ProD.*
A total of 77 socioeconomic and demographic variables and 46 indices of bio-psychosocial dysfunction were gathered on each of Minnesota's 87 counties. All of these were expressed in rates per 10,000 population or other standard figures. Various analyses were performed on the data. It was demonstrated that half of the variance in state hospital load was associated with socioeconomic variables. Poor counties showed a higher incidence and prevalence of major mental illness than did wealthier counties. Practical planning can be carried out for future mental health facilities by utilizing population and socioeconomic indices. PROBLEM Present trends in the mental health field point to an increasing emphasis on localized services. P l a n n i n g for future facilities must take this into account. A survey of the literature revealed that geographical and political subdivision studies have been limited to r u r a l - u r b a n differences and census tract differences i n large cities. Though these studies are of great value, the fact remains that the key administrative unit in most states is the county. A host of studies have demonstrated a relationship between state mental hospital loads and various indices of low socioeconomic status. Again, n o studies have been made of the socioeconomic status of counties and their relationship to mental illness.
This study is an attempt to begin to fill this gap. It is hoped that the results of this study will be of use to p l a n n e r s in the mental health f i e l d - - especially at the com, m u n i t y level. METHOD A total of 77 socioeconomic and demographic variables on each of Minnesota's 87 counties were extracted from the 1950 United States census (education, total labor force, etc.). Those variables which were not already expressed in comparative terms (median income, etc.) were transformed into rates per 10,000 population. The 77 variables were intercorrelated. Seven variables were extracted using the following criteria as guidelines: (a) The variables were minimally related to each other; (b) showed definite relationships with a number of other factors and (e) could be readily obtained in any particular year from other than census data. These were: no piped water, total owner occupied housing units, total of males completing the eighth grade and less, per cent housing units using wood, coke or coal for heating (relating positively to poverty) and median gross rent per month, per cent of families having telephones, per cent of families having TV sets (relating negatively to poverty). Next, the counties were intercorrelated over the 77 variables and a Q type cluster analysis was performed. Four types of counties emerged: (a) urban, (b) suburban and minor industrial, (c) poor rural and (d) rich rural. Because of the fact that counties containing state hospitals always have higher rates than other counties in the receiving area and the fact that these higher rates stem from causes other than the one investigated in this study, these counties were eliminated from the study. Since
*Dr. Vail is Medical Director and Mr. Lucero Mental Health Research Consultant in the Division of Medical Services of the Minnesota Department of Public Welfare, St. Paul; Dr. Boen, partially supported by VRA-RT-2, is an Assistant Professor of Biostatistics, School of Public Health and the Department of Physical Medicine and Rehabilitation, University of Minnesota. This study was supported in large part by a research grant from the Minnesota Division of Medical Services. The authors are greatly indebted to Mary Bilek, Byron W. Brown, Ph.D., Arthur J. Gallese, Ph.D., and Eleanor Steelsmith for various types of assistance. 211
COMMUNITY MENTAL HEALTH
there are seven major state mental hospitals in Minnesota, the final number of counties was 80. Ten major areas of bio-psycho-sociM dysfunction were identified. These were: major mental illness, alcoholism, mental retardation and epilepsy, major crime, Aid to Dependent Children, Aid to the Blind, Aid to Crippled Children, Aid to Disabled, Old Age Assistance and General Relief. These in turn were broken down into 46 subcategories. They were averaged over a six year period, rates calculated per 10,000 population, and were intercorrelated over the 87 counties. This was done in order to determine the single measure of mental illness that (a) best represented mental illness and (b) that had the highest and most relationships with the other factors. This measure turned out to be identical to the defined area; major mental illness. It consists of first admissions to state hospitals (incidence), readmissions and inhospital population (prevalence) and all discharges. Significances of differences between means were calculated for the 10 bio-psycho-social dysfunction areas between the different types of counties. Multiple correlation coefficients were computed between all possible combinations of the seven socioeconomic indices and major mental illness for counties not containing state hospitals. Finally, the multiple correlation coefficients were repeated leaving out voluntary patients. This was done on the assumption that voluntary patients might come from higher socioeconomic backgrounds than committed patients and their exclusion would lead to a higher relationship between the socioeconomic level of counties and major mental illness. RESULTS F o l l o w i n g are the results for the different types of counties. A l l differences were significant b e y o n d the .01 confidence level. Major mental illness: P > Alcoholism: P = Mental Retardation & Epilepsy: P : Major crime: P = Aid to Dependent Children: P > Aid to the Blind: P > Aid to Crippled Children: P > Aid to Disabled: P > Old Age Assistance: P > General Relief: P > inbetween,
U = S -----R. U = S -----R. U : S : R. U > S = R. U : S = R. U : S = R. U : S = R. R > U : S. R > U : S. R (U = S variance high)
Code: P : Poor rural; U = Urban; S -- Suburban and minor industrial; R = Rich rural.
The m u l t i p l e correlation comcient between m a j o r mental illness and all seven socioeconomic variables equalled .70. Another way of stating this is that 49 p e r cent of the variance in m a j o r mental illness was accounted for b y the socioeconomic status of the various counties. It was predicted f r o m the above p l u s the fact that voluntary admissions are heaviest in the rich r u r a l areas that this relationship would be higher if v o l u n t a r y patients were removed from the p o p u l a t i o n being studied. The prediction was born out slightly in that the multip l e correlation coefficient rose to .73 with 53 p e r cent of the variance accounted for when v o l u n t a r y patients were dropped. This rise, however, is only of theoretic imp o r t and has no practical significance. DISCUSSION Mental illness m a y be a necessary but is certainly not a sufficient condition to account for hospitalization in a state mental hospital. There are certainly other sources of variance but it must be said at this point that socioeconomic factors are of great practical importance. The findings of this study indicate that planning for the future should involve estimates of the socioeconomic level of counties. These estimates, combined with p o p u l a t i o n concentration figures, can lead to practical p l a n n i n g for future mental health facilities. Investigators have been in disagreement on the question of the relationship between r u r a l - u r b a n status and m a j o r mental illness. Some say that incidence is higher in urban areas, other claim the opposite. Is it conceivable that both schools are p a r t l y r i g h t ? It m a y be that urban areas are more wealthy in some sections of the country and r u r a l areas m o r e wealthy in other sections. Thus, the socioeconomic level of the area would be the factor related to m a j o r mental illness---not urban or rural status.