Determining Risk Status in a Primary Care Setting Catherine Ingram Fogel
Nurses working in primary health care settings often care for large numbers of clients in brief periods of time. Both clients and nurses express frustration toward the care provided in these circumstances. Development of a screening tool to identify high-risk clients could assist nurses in targeting interventions to these individuals; in turn, this has the potential for increasing nurse and client satisfaction. A general procedure for identifying persons at risk in primary health care settings is described. The procedure is illustrated with a specific population of clients--incarcerated women. Factors found important in assessing risk were social characteristics such as education, situational factors such as sentence length, and indicators of psychological distress such as depression. These broad categories may serve as a basis for the development of screening tools for a variety of populations. Copyright © 1992 by W.B. Saunders Company
URSES WORKING in primary care settings with large numbers of clients can experience frustration. They may feel dissatisfied with the care they provide and wonder if they are meeting the health care needs of those clients at greatest risk for health problems. One strategy for addressing these concerns is the development of a screening tool for high-risk status. If at-risk individuals can be identified, they can be targeted for additional nursing interventions. This may increase both client and nurse satisfaction with the care provided. This article describes the basis for developing a tool to screen high-risk clients with data readily accessible to the nurse. One type of primary health care setting that includes many high-risk clients is the correctional institution. Nurses conducting sick call and working in outpatient clinics in prisons often have large numbers of clients. Derro (1978), studying male inmates to determine the type and frequency of health problems they experienced during incarceration, reported the mean number of visits per inmate per year was 8.2. This rate of clinical usage was two to three times higher than the reported national rate of use of outpatient facilities by the general population (U.S. Department of Health, Education, and Welfare [USDHEW], 1975a; 1975b). Demers and Walsh (1981) studied a county jail population over a 2-month period and confirmed the high utilization rates of health care services found by Derro. Female prisoners in their study averaged 24.7 visits during the 2-month pe-
N
140
riod for an annual visit rate of 148.4. This rate grossly exceeded the 2.6 average United States' rate for adult females reported by the National Ambulatory Medical Care Survey (USDHEW, 1975a) and was three times greater than the reported rate for male prisoners in the same facility. More recently, in a sample of 55 incarcerated women, Fogel (1988) found the mean number of sick call visits to be 12.5 for 6 months for an annual visit rate of 25. This rate also far exceeds the reported 1980 outpatient visit rate for females in the general population in the United States. Nurses providing care to the inmates reported feeling overwhelmed by the volume of clients seen and were frustrated with the care they were able to provide. The nurses believed that some of the inmates seen were not actively ill and wasted time that could have been spent with those who actually were sick. On the other hand, inmates believed that their health problems were not addressed adequately, and they found the health care system to be unresponsive to their concerns. Furthermore, From the Department of Health of Women and Children, School of Nursing, University of North Carolina at Chapel Hill, NC. Catherine lngram Fogel, PhD, RNC: Associate Professor, University of North Carolina at Chapel Hill, NC. Address reprint requests to Catherine lngram Fogel, PhD, RNC, Associate Professor, 526 Carrington CB #7460, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7460. Copyright © 1992 by W.B. Saunders Company. 0897-189719210503-000755.0010 Applied Nursing Research, Vol. 5, No, 3 (August), 1992: pp. 140-145
141
ASSESSING RISK STATUS
over one quarter of them indicated that they did not seek treatment for health problems because they were dissatisfied with the health care they received (Fogel, 1988). One strategy for addressing the problem of frequent sick-call visits resulting in nurse and client dissatisfaction is early identification of those inmates who are at risk for ongoing health problems. When these individuals are identified, nurses can offer varied interventions to improve the quality of care delivered, which increases satisfaction with care. As a first step in developing a screening instrument, longitudinal data available from a larger study (Fogel, 1988) to document the health problems of incarcerated women were analyzed to find factors that could identify subjects who experienced increased health problems over a 6-month period of incarceration. The following research questions were addressed: (1) What factors can be used to identify those individuals who will experience increased physical symptoms during a period of incarceration? and (2) How well does the chosen set of variables identify those individuals?
METHOD Setting and Sample The data were collected at a major correctional facility for women located in a southern state. The prison, a maximum security institution, is one of the largest of its kind in the United States. At the time of the study, it housed approximately 600 women. The sample included 55 newly incarcerated, nonpregnant women with sentences of 2 years or more; these women were observed for 6 months. Almost two thirds of the sample were nonwhite (3.7% [n = 2] American Indian and 61.8% [n = 34] black). The majority of subjects had not graduated from high school (mean grade completed was 10.6) and were in their late twenties (mean age was 28.5 years). This was the first incarceration experience for 52.8% (n = 29) of the subjects. The typical subject in this study was a single headof-household who possessed few, if any, job skills. She usually had been in a low paying job or unemployed before her incarceration. The sample was demographically similar to national (Feinman, 1986; Glick & Neto, 1977) and statewide (Grossman, 1983) studies of female prisoners. Subjects were incarcerated for serious crime such as murder
or robbery and for pretty offenses such as forgery or welfare fraud.
Measurement of Variables Of critical importance in developing a technique for identifying clients at risk is the selection of the variables used to distinguish between risk and no risk. In this analysis, three types of variables were used: key social characteristics, situational variables, and indicators of psychological distress.
Social Characteristics Race, age, and socioeconomic status are key social characteristics that may affect an individual's exposure and vulnerability to stress (Pearlin, Lieberman, Menaghan, &Mullan, 1981). Age is strongly related to health (Verbrugge, 1983). Children and young adults experience more acute illnesses, whereas older persons experience more chronic disease. Older people generally are expected to be in poorer health. In this study, age was measured as the subject's age at her last birthday. Race is a common predictor of health status, with members of nonwhite races (blacks and native Americans) traditionally having higher levels of illness than whites (Cassel, 1976; Milio, 1986). Race was measured here as white and nonwhite. Throughout the life cycle, social divisions affect morbidity, mortality and health behaviors (Marmot, Kogerunos, & Elston, 1987). Poorer people are at higher risk for virtually all acute and chronic illnesses (Milio, 1986). Whether social class is measured by income, education, or occupation, those at the bottom have higher rates of disability and disease. In this analysis, education was used as a measure of socioeconomic status because income data are not a reliable measure for the incarcerated population. To determine educational level, respondents were asked to indicate the last grade they had completed.
Situational Variables An important component of screening for risk is the client's life circumstances. In the analysis reported in this article, the dominant life situation was recent incarceration. The selection of situational variables was based on the assumption that incarceration is a stressful life event. The Jones (1976) finding that prisons are most dangerous for new inmates suggests that prior incarceration experiences might have a protective effect against the
CATHERINE INGRAM FOGEL
142
stressor of imprisonment. Prior incarceration was measured here as whether or not the subject had ever been in prison before. Jones (1976) found that prisons were also dangerous for those inmates who had been confined for a long time and for those whose prospect of release was uncertain. These findings suggest that those inmates who receive long prison sentences might experience more stress than those who expect to serve less time in prison. Thus, length of sentence was selected as another situational stressor and was measured in number of years. Type of crime is related to sentence length in that inmates convicted of severe and violent crimes receive longer sentences. Type of crime committed was defined as serious or violent crimes as opposed to petty offenses.
Indicators of Psychological Distress The potential impact of prior psychological health status on present health status has long been recognized (Tausig, 1986). Two indicators of psychological health status at entry to prison were selected: depression and anxiety. The Center for Epidemiologic Study Depression Scale (CES-D) was used to measure levels of depressive symptoms (Radliff, 1977). The scale consists of 20 symptoms, rated on a 4-point Likert scale from 0 ("rarely or not at all") to 3 ("most of the time"). Respondents are asked how often they have experienced each of the symptoms in the past week. The sum of responses is the total score for the respondents. Reported Cronbach's alpha coefficients for this scale range from .84 to .90 (Lin, Dean, & Ensel, 1986). The alpha coefficient for this sample was .92. The scale has been found to be acceptable for use with diverse ethnic populations and community studies (Roberts, I980). The State Scale of the Speilberger State-Trait Anxiety Inventory (Speilberger, 1983) was used to measure state anxiety at the time of incarceration. The scale consists of 20 items; respondents are asked to rate the intensity of their present feelings of apprehension, tension, worry, and nervousness on a scale from 1 ("not at all") to 4 ("very much so"). The responses are summed. Alpha coefficients between .90 and .92 have been reported for this inventory (Speilberger, 1983). Alpha coefficient for this sample was .92. Evidence of the construct validity of this instrument was found in two
studies investigating the effects of stress on state anxiety (Speilberger, 1983).
Outcome Variable A Symptoms Checklist drawn from an interview schedule used to determine health service (Anderson & Anderson, 1967) was used to measure frequency of occurrence of symptoms commonly encountered in health care. These symptoms are physical complaints for which users of outpatient services most commonly seek health care. Subjects were asked at the time of incarceration and again 6 months' later which of 20 common physical symptoms they had experienced in the previous month. The total number of symptoms reported at entry to prison was subtracted from the total number of symptoms reported after 6 months in prison to obtain a symptom change score. Subjects were then divided into two groups: those who had experienced an increase in symptoms and those who had experienced either no increase or a decrease in the number of symptoms. Procedure Data were obtained through chart review and personal interview. Potential subjects were contacted during their first week of incarceration, the study was explained, and informed consent was obtained. The initial interview, lasting between 45 minutes and 2 hours, was conducted at this time. Data on key social characteristics, situational variables, and indicators of psychological distress, and number of physical symptoms present during the previous week were obtained in this first interview. A follow-up interview, approximately 1 hour in length, was conducted in 6 months. Data on the number of physical symptoms present in the week before the interview were obtained with the follow-up interview. Because of the low literacy of this population, all instruments were read to the subjects and unfamiliar words were defined for them. To ensure confidentiality for the inmates, all interviews were conducted in a private room. At no time were correctional officers, other prison personnel, or other inmates present. Discriminant function analysis was used to answer the research questions. This statistical procedure makes it possible to determine if significant group differences exist, identify what independent or predictor variables are related to the criterion
143
ASSESSING RISK STATUS
variable, and determine how well a set of variables will predict group membership (Edens, 1987; Stevens, 1986). RESULTS AND DISCUSSION
Descriptive statistics on the study variables are shown in Table 1. The mean number of symptoms reported by the subjects at entry to prison was 5.3. After 6 months in prison, they experienced 4.6 symptoms. This difference was not statistically significant. Subjects reported changes in number of symptoms ranging from a decrease of 13 to an increase of 6. The mean symptom change score was - 0 . 7 9 (SD = 3.62) or an average decrease in number of symptoms of slightly less than 1. No change in number of symptoms or a decrease in the number of symptoms was reported by 61.8% of the subjects, whereas 38.2% reported an increased number of physical symptoms after 6 months of imprisonment. When discriminant function analysis was performed, six predictor variables were found to maximally separate those subjects who reported increased symptoms and those who did not. The set of variables that identified those at high risk were depression scale score, type of crime committed, anxiety scale score, sentence length, prior incarceration experience, and educational level. Race and age were not discriminating factors. Persons who were at risk for increased symptoms included patients who had higher depression scores, longer sentences, more education, a previous incarceration experience, and a history of petty criminal offenses (Table 2). In terms of relative importance in prediction, a subject's depression scale score had the most predictive weight, whereas education had the least. The canonical correlation coefficient Table 1. Descriptive Statistics for Variables Variable (N = 55)
Mean
Standard Deviation
Age Education Race Sentence length Crime Prior incarceration CES-D STAI-S Change
28.5 10.63 0.35 13.2 0.84 0.42 24.76 47.58 0.38
8.84 2.18 0.48 13.41 0.37 0.49 14.84 14.53 0.49
Table 2. Group Mean for the Predictor Variables Group Mean Variable
Ia
lib
Depression (CES-D) Crime e Sentence length Anxiety (STAI-S) Prior incarceration Education
23.818 1.848 10.758 49.516 1.364 10.394
25.762 1.714 17.095 44.286 1.476 11.048
a Subjects with no change in physical symptoms or decrease in physical symptoms. b Subjects/with increase in physical symptoms. ¢ Variable is coded such that 1 = petty offenses and 2 = serious/violent offense.
was .4787 (×2(6) = 12.754, p = .05), indicating a satisfactory measure of association (Table 3). Table 4 shows the ability of the predictor variables to correctly classify cases in the two groups, those with increased symptoms and those with the same number or fewer symptoms at 6 months. The set of variables correctly classified 75.93% of the cases but underpredicted the number of subjects who experienced increased symptoms in 33.3% of cases. IMPLICATIONS
This study identified a set of client factors that correctional health care nurses can use to discriminate between those clients who will experience increased physical symptoms during incarceration and those who will not. Some of the data, such as educational level, are readily available to nurses. Other information, such as sentence length, type of crime, and prior incarceration, is obtained easily Table 3. Standardized Canonical Discriminant Function Coefficients Predictave of Group Membership Variable (N = 55) Depression (CES-D) Crime Sentence length Anxiety (STAI-S) Prior incarceration Education
Coefficient 0.8865 0.7243 0.6651 0.6122 0.4540 0.3391
Canonical correlation is .4787; Wilk's lambda is .7708. X2 (6) = 12.75, p ~< .05.
CATHERINE INGRAM FOGEL
144
Table 4. Ability of Variable Set to Predict Subjects With and Without Increased Symptoms Actual Group (N = 55)
Number of Cases
Predicted Group
Membership
No change 1 (0)
33
Change b (1)
21
27 (81.8%) 7 (33.3%) 0 (0.0%)
6 (18.2%) 14 (66.7%) 1 (100.0%)
Ungrouped cases
1
Percentage of Cases correctly classified is 75.93%. a Subjects with no change in physical symptoms or decrease in physical symptoms. b Subjects with increase in physical symptoms.
from the inmate or prison records. Administration of the CES-D and STAI-S scales could be incorporated into intake medical history procedures or other prison diagnostic routines. The combined set of factors then could be used as a screening tool for risk of increased health problems while in prison. Subjects potentially at risk could be identified, and a notation would be made on their medical charts. If these at-risk women attended sick call more frequently than was the norm for the prison, nurses could more quickly identify them and administer appropriate interventions. An even more effective strategy might be to intervene before inmates made numerous visits to sick call. More in-depth physical screening examinations might be performed on those with high-risk status, and crisis intervention groups might be formed to facilitate the inmates' adaptation to the prison environment. Using a prison setting to illustrate the development of a screening technique to be used in busy
primary health care settings is unusual, yet the screening process probably is applicable to many types of populations. Using a framework that incorporates individual social attributes, present life circumstances, and previous psychological health status ensures a multidimensional approach to client assessment. In this framework, both individual and social structural attributes can be accounted for. The categories of factors suggested are broad enough so that nurses can incorporate variables relevant to their population. What is critical to the ultimate usefulness of this screening method is the selection of factors to distinguish between high and low risk. Reviewing the literature to determine which characteristics have been suggested as risk factors for particular populations may assist in the selection of relevant variables. Nurses should also carefully examine their clinical observations and hunches to identify those patient characteristics they believe to be relevant. Once a set of client characteristics has been selected for the screening tool, the characteristics' relevance for screening must be established. An outcome variable that is easily measured and relevant to the practice site must also be identified. Finally, nurses must collect and analyze data to evaluate the usefulness of the set of factors chosen. Nurses in primary health care and their clients frequently express frustration at the quality of care provided and received in such settings. Development of a tool for identifying high-risk clients in prison who would benefit from increased interventions was reported. The procedure for identifying factors found important in assessing risk was described and suggestions for application to other settings made.
REFERENCES
Anderson, R., & Anderson, O.W. (1967). Decade of health service. Chicago, IL: University of Chicago Press. Cassel, J.C. (1976). The contribution of the social environment to host resistence. American Journal of Epidemiology, 104, 47-48. Demers, R., & Walsh, K. (1981). Use of medical services during a 2-month period in the Seattle-King County (Washington) jail. Public Health Reports, 96, 452-457. Derro, R. (1978). Health problems in a city-county workhouse. Public Health Reports, 93, 379-385. Edens, G.E. (1987). Discriminant analysis. Nursing Research, 36, 257-261.
Feinman, C. (1986). Women in the criminal justice system (2nd ed.). New York, NY: Praeger. Fogel, C.I. (1988). Health status of incarcerated women. Dissertation Abstracts International, 49, 1582A. Glick, R., & Neto, V. (1977). National study of women's correctional programs. Washington, DC: National Institute of Law Enforcement and Criminal Justice. Grossman, J. (1983). Comparison of male and female inmates under the department's custody: As of June 1983. Report prepared for the State of New York Department of Correctional Services. Jones, D. (1976). Health risk of imprisonment. Lexington, MA: DC Health & Company.
ASSESSING RISK STATUS
Lin, N., Dean, A., & Ensel, W. (1986). Social support, life events and depression. Orlando, FL: Academic. Marmot, M.G., Kogerunos, M., & Elston, P. (1987). Social/economic status and disease. Annual Review of Public Health, 8, 11-35. Milio, N. (1986). Promoting health through public policy. Ottawa, Canada: Canadian Public Health Association. Pearlin, L.I., Lieberman, M., Menaghan, E., &Mullan, J. (1981). The stress process. Journal of Heahh and Social Behavior, 22, 337-356. Radliff, L.S. (1977). The CES-D scale: A self-report depression scale for use in the general population. Applied Psychological Measurement, 1, 385-401. Roberts, R. (1980). Relationship of the CES-D in different ethnic contexts. Psychiatry Research, 2, 125. Speilberger, C.D. (1983). Manual for the state-trait anxiety inventory. Palo Alto, CA: Consulting Psychologist Press. Stevens, J. (1986). Applied Multivariate Statistics for the Social Sciences. Hillsdale, NJ: Lawrence Erlbarium Associates.
145
Tausig, M. (1986). Prior history of illness in the basic model. In N. Lin, A. Dean, & W.M. Ensel. (Eds.), Social support, life events and depression (pp. 71-95). New York, NY: Academic. U.S. Department of Health, Education and Welfare. (1975a). The national ambulatory medical care surL,ey: 1973 summary United States May 1973-April 1974. (DHEW Publication No. HRA 76-1772). Washington, DC: U.S. Government Printing Office. U.S. Department of Health, Education and Welfare. (1975b). Current estimates from the health interview survey, United States-1974. (DHEW Publication No. 76-1527). Washington. DC: U.S. Government Printing Office. U.S. Department of Commerce, Bureau of Census. (1990). StatisticalAbstJ'act of the United States 1990. Washington, DC: U.S. Government Printing Office. Verbmgge, L.M. (1983). Multiple roles and physical health of men and women. Journal of Health and Social Behavior, 24, 16-30.
Nurses working in primary health care settings often care for large numbers of clients in brief periods of time. Both clients and nurses express frustration toward the care provided in these circumstances. Development of a screening tool to identify high-risk clients could assist nurses in targeting interventions to these individuals; in turn, this has the potential for increasing nurse and client satisfaction. A general procedure for identifying persons at risk in primary health care settings is described. The procedure is illustrated with a specific population of clients--incarcerated women. Factors found important in assessing risk were social characteristics such as education, situational factors such as sentence length, and indicators of psychological distress such as depression. These broad categories may serve as a basis for the development of screening tools for a variety of populations. Copyright © 1992 by W.B. Saunders Company
URSES WORKING in primary care settings with large numbers of clients can experience frustration. They may feel dissatisfied with the care they provide and wonder if they are meeting the health care needs of those clients at greatest risk for health problems. One strategy for addressing these concerns is the development of a screening tool for high-risk status. If at-risk individuals can be identified, they can be targeted for additional nursing interventions. This may increase both client and nurse satisfaction with the care provided. This article describes the basis for developing a tool to screen high-risk clients with data readily accessible to the nurse. One type of primary health care setting that includes many high-risk clients is the correctional institution. Nurses conducting sick call and working in outpatient clinics in prisons often have large numbers of clients. Derro (1978), studying male inmates to determine the type and frequency of health problems they experienced during incarceration, reported the mean number of visits per inmate per year was 8.2. This rate of clinical usage was two to three times higher than the reported national rate of use of outpatient facilities by the general population (U.S. Department of Health, Education, and Welfare [USDHEW], 1975a; 1975b). Demers and Walsh (1981) studied a county jail population over a 2-month period and confirmed the high utilization rates of health care services found by Derro. Female prisoners in their study averaged 24.7 visits during the 2-month pe-
N
140
riod for an annual visit rate of 148.4. This rate grossly exceeded the 2.6 average United States' rate for adult females reported by the National Ambulatory Medical Care Survey (USDHEW, 1975a) and was three times greater than the reported rate for male prisoners in the same facility. More recently, in a sample of 55 incarcerated women, Fogel (1988) found the mean number of sick call visits to be 12.5 for 6 months for an annual visit rate of 25. This rate also far exceeds the reported 1980 outpatient visit rate for females in the general population in the United States. Nurses providing care to the inmates reported feeling overwhelmed by the volume of clients seen and were frustrated with the care they were able to provide. The nurses believed that some of the inmates seen were not actively ill and wasted time that could have been spent with those who actually were sick. On the other hand, inmates believed that their health problems were not addressed adequately, and they found the health care system to be unresponsive to their concerns. Furthermore, From the Department of Health of Women and Children, School of Nursing, University of North Carolina at Chapel Hill, NC. Catherine lngram Fogel, PhD, RNC: Associate Professor, University of North Carolina at Chapel Hill, NC. Address reprint requests to Catherine lngram Fogel, PhD, RNC, Associate Professor, 526 Carrington CB #7460, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7460. Copyright © 1992 by W.B. Saunders Company. 0897-189719210503-000755.0010 Applied Nursing Research, Vol. 5, No, 3 (August), 1992: pp. 140-145
141
ASSESSING RISK STATUS
over one quarter of them indicated that they did not seek treatment for health problems because they were dissatisfied with the health care they received (Fogel, 1988). One strategy for addressing the problem of frequent sick-call visits resulting in nurse and client dissatisfaction is early identification of those inmates who are at risk for ongoing health problems. When these individuals are identified, nurses can offer varied interventions to improve the quality of care delivered, which increases satisfaction with care. As a first step in developing a screening instrument, longitudinal data available from a larger study (Fogel, 1988) to document the health problems of incarcerated women were analyzed to find factors that could identify subjects who experienced increased health problems over a 6-month period of incarceration. The following research questions were addressed: (1) What factors can be used to identify those individuals who will experience increased physical symptoms during a period of incarceration? and (2) How well does the chosen set of variables identify those individuals?
METHOD Setting and Sample The data were collected at a major correctional facility for women located in a southern state. The prison, a maximum security institution, is one of the largest of its kind in the United States. At the time of the study, it housed approximately 600 women. The sample included 55 newly incarcerated, nonpregnant women with sentences of 2 years or more; these women were observed for 6 months. Almost two thirds of the sample were nonwhite (3.7% [n = 2] American Indian and 61.8% [n = 34] black). The majority of subjects had not graduated from high school (mean grade completed was 10.6) and were in their late twenties (mean age was 28.5 years). This was the first incarceration experience for 52.8% (n = 29) of the subjects. The typical subject in this study was a single headof-household who possessed few, if any, job skills. She usually had been in a low paying job or unemployed before her incarceration. The sample was demographically similar to national (Feinman, 1986; Glick & Neto, 1977) and statewide (Grossman, 1983) studies of female prisoners. Subjects were incarcerated for serious crime such as murder
or robbery and for pretty offenses such as forgery or welfare fraud.
Measurement of Variables Of critical importance in developing a technique for identifying clients at risk is the selection of the variables used to distinguish between risk and no risk. In this analysis, three types of variables were used: key social characteristics, situational variables, and indicators of psychological distress.
Social Characteristics Race, age, and socioeconomic status are key social characteristics that may affect an individual's exposure and vulnerability to stress (Pearlin, Lieberman, Menaghan, &Mullan, 1981). Age is strongly related to health (Verbrugge, 1983). Children and young adults experience more acute illnesses, whereas older persons experience more chronic disease. Older people generally are expected to be in poorer health. In this study, age was measured as the subject's age at her last birthday. Race is a common predictor of health status, with members of nonwhite races (blacks and native Americans) traditionally having higher levels of illness than whites (Cassel, 1976; Milio, 1986). Race was measured here as white and nonwhite. Throughout the life cycle, social divisions affect morbidity, mortality and health behaviors (Marmot, Kogerunos, & Elston, 1987). Poorer people are at higher risk for virtually all acute and chronic illnesses (Milio, 1986). Whether social class is measured by income, education, or occupation, those at the bottom have higher rates of disability and disease. In this analysis, education was used as a measure of socioeconomic status because income data are not a reliable measure for the incarcerated population. To determine educational level, respondents were asked to indicate the last grade they had completed.
Situational Variables An important component of screening for risk is the client's life circumstances. In the analysis reported in this article, the dominant life situation was recent incarceration. The selection of situational variables was based on the assumption that incarceration is a stressful life event. The Jones (1976) finding that prisons are most dangerous for new inmates suggests that prior incarceration experiences might have a protective effect against the
CATHERINE INGRAM FOGEL
142
stressor of imprisonment. Prior incarceration was measured here as whether or not the subject had ever been in prison before. Jones (1976) found that prisons were also dangerous for those inmates who had been confined for a long time and for those whose prospect of release was uncertain. These findings suggest that those inmates who receive long prison sentences might experience more stress than those who expect to serve less time in prison. Thus, length of sentence was selected as another situational stressor and was measured in number of years. Type of crime is related to sentence length in that inmates convicted of severe and violent crimes receive longer sentences. Type of crime committed was defined as serious or violent crimes as opposed to petty offenses.
Indicators of Psychological Distress The potential impact of prior psychological health status on present health status has long been recognized (Tausig, 1986). Two indicators of psychological health status at entry to prison were selected: depression and anxiety. The Center for Epidemiologic Study Depression Scale (CES-D) was used to measure levels of depressive symptoms (Radliff, 1977). The scale consists of 20 symptoms, rated on a 4-point Likert scale from 0 ("rarely or not at all") to 3 ("most of the time"). Respondents are asked how often they have experienced each of the symptoms in the past week. The sum of responses is the total score for the respondents. Reported Cronbach's alpha coefficients for this scale range from .84 to .90 (Lin, Dean, & Ensel, 1986). The alpha coefficient for this sample was .92. The scale has been found to be acceptable for use with diverse ethnic populations and community studies (Roberts, I980). The State Scale of the Speilberger State-Trait Anxiety Inventory (Speilberger, 1983) was used to measure state anxiety at the time of incarceration. The scale consists of 20 items; respondents are asked to rate the intensity of their present feelings of apprehension, tension, worry, and nervousness on a scale from 1 ("not at all") to 4 ("very much so"). The responses are summed. Alpha coefficients between .90 and .92 have been reported for this inventory (Speilberger, 1983). Alpha coefficient for this sample was .92. Evidence of the construct validity of this instrument was found in two
studies investigating the effects of stress on state anxiety (Speilberger, 1983).
Outcome Variable A Symptoms Checklist drawn from an interview schedule used to determine health service (Anderson & Anderson, 1967) was used to measure frequency of occurrence of symptoms commonly encountered in health care. These symptoms are physical complaints for which users of outpatient services most commonly seek health care. Subjects were asked at the time of incarceration and again 6 months' later which of 20 common physical symptoms they had experienced in the previous month. The total number of symptoms reported at entry to prison was subtracted from the total number of symptoms reported after 6 months in prison to obtain a symptom change score. Subjects were then divided into two groups: those who had experienced an increase in symptoms and those who had experienced either no increase or a decrease in the number of symptoms. Procedure Data were obtained through chart review and personal interview. Potential subjects were contacted during their first week of incarceration, the study was explained, and informed consent was obtained. The initial interview, lasting between 45 minutes and 2 hours, was conducted at this time. Data on key social characteristics, situational variables, and indicators of psychological distress, and number of physical symptoms present during the previous week were obtained in this first interview. A follow-up interview, approximately 1 hour in length, was conducted in 6 months. Data on the number of physical symptoms present in the week before the interview were obtained with the follow-up interview. Because of the low literacy of this population, all instruments were read to the subjects and unfamiliar words were defined for them. To ensure confidentiality for the inmates, all interviews were conducted in a private room. At no time were correctional officers, other prison personnel, or other inmates present. Discriminant function analysis was used to answer the research questions. This statistical procedure makes it possible to determine if significant group differences exist, identify what independent or predictor variables are related to the criterion
143
ASSESSING RISK STATUS
variable, and determine how well a set of variables will predict group membership (Edens, 1987; Stevens, 1986). RESULTS AND DISCUSSION
Descriptive statistics on the study variables are shown in Table 1. The mean number of symptoms reported by the subjects at entry to prison was 5.3. After 6 months in prison, they experienced 4.6 symptoms. This difference was not statistically significant. Subjects reported changes in number of symptoms ranging from a decrease of 13 to an increase of 6. The mean symptom change score was - 0 . 7 9 (SD = 3.62) or an average decrease in number of symptoms of slightly less than 1. No change in number of symptoms or a decrease in the number of symptoms was reported by 61.8% of the subjects, whereas 38.2% reported an increased number of physical symptoms after 6 months of imprisonment. When discriminant function analysis was performed, six predictor variables were found to maximally separate those subjects who reported increased symptoms and those who did not. The set of variables that identified those at high risk were depression scale score, type of crime committed, anxiety scale score, sentence length, prior incarceration experience, and educational level. Race and age were not discriminating factors. Persons who were at risk for increased symptoms included patients who had higher depression scores, longer sentences, more education, a previous incarceration experience, and a history of petty criminal offenses (Table 2). In terms of relative importance in prediction, a subject's depression scale score had the most predictive weight, whereas education had the least. The canonical correlation coefficient Table 1. Descriptive Statistics for Variables Variable (N = 55)
Mean
Standard Deviation
Age Education Race Sentence length Crime Prior incarceration CES-D STAI-S Change
28.5 10.63 0.35 13.2 0.84 0.42 24.76 47.58 0.38
8.84 2.18 0.48 13.41 0.37 0.49 14.84 14.53 0.49
Table 2. Group Mean for the Predictor Variables Group Mean Variable
Ia
lib
Depression (CES-D) Crime e Sentence length Anxiety (STAI-S) Prior incarceration Education
23.818 1.848 10.758 49.516 1.364 10.394
25.762 1.714 17.095 44.286 1.476 11.048
a Subjects with no change in physical symptoms or decrease in physical symptoms. b Subjects/with increase in physical symptoms. ¢ Variable is coded such that 1 = petty offenses and 2 = serious/violent offense.
was .4787 (×2(6) = 12.754, p = .05), indicating a satisfactory measure of association (Table 3). Table 4 shows the ability of the predictor variables to correctly classify cases in the two groups, those with increased symptoms and those with the same number or fewer symptoms at 6 months. The set of variables correctly classified 75.93% of the cases but underpredicted the number of subjects who experienced increased symptoms in 33.3% of cases. IMPLICATIONS
This study identified a set of client factors that correctional health care nurses can use to discriminate between those clients who will experience increased physical symptoms during incarceration and those who will not. Some of the data, such as educational level, are readily available to nurses. Other information, such as sentence length, type of crime, and prior incarceration, is obtained easily Table 3. Standardized Canonical Discriminant Function Coefficients Predictave of Group Membership Variable (N = 55) Depression (CES-D) Crime Sentence length Anxiety (STAI-S) Prior incarceration Education
Coefficient 0.8865 0.7243 0.6651 0.6122 0.4540 0.3391
Canonical correlation is .4787; Wilk's lambda is .7708. X2 (6) = 12.75, p ~< .05.
CATHERINE INGRAM FOGEL
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Table 4. Ability of Variable Set to Predict Subjects With and Without Increased Symptoms Actual Group (N = 55)
Number of Cases
Predicted Group
Membership
No change 1 (0)
33
Change b (1)
21
27 (81.8%) 7 (33.3%) 0 (0.0%)
6 (18.2%) 14 (66.7%) 1 (100.0%)
Ungrouped cases
1
Percentage of Cases correctly classified is 75.93%. a Subjects with no change in physical symptoms or decrease in physical symptoms. b Subjects with increase in physical symptoms.
from the inmate or prison records. Administration of the CES-D and STAI-S scales could be incorporated into intake medical history procedures or other prison diagnostic routines. The combined set of factors then could be used as a screening tool for risk of increased health problems while in prison. Subjects potentially at risk could be identified, and a notation would be made on their medical charts. If these at-risk women attended sick call more frequently than was the norm for the prison, nurses could more quickly identify them and administer appropriate interventions. An even more effective strategy might be to intervene before inmates made numerous visits to sick call. More in-depth physical screening examinations might be performed on those with high-risk status, and crisis intervention groups might be formed to facilitate the inmates' adaptation to the prison environment. Using a prison setting to illustrate the development of a screening technique to be used in busy
primary health care settings is unusual, yet the screening process probably is applicable to many types of populations. Using a framework that incorporates individual social attributes, present life circumstances, and previous psychological health status ensures a multidimensional approach to client assessment. In this framework, both individual and social structural attributes can be accounted for. The categories of factors suggested are broad enough so that nurses can incorporate variables relevant to their population. What is critical to the ultimate usefulness of this screening method is the selection of factors to distinguish between high and low risk. Reviewing the literature to determine which characteristics have been suggested as risk factors for particular populations may assist in the selection of relevant variables. Nurses should also carefully examine their clinical observations and hunches to identify those patient characteristics they believe to be relevant. Once a set of client characteristics has been selected for the screening tool, the characteristics' relevance for screening must be established. An outcome variable that is easily measured and relevant to the practice site must also be identified. Finally, nurses must collect and analyze data to evaluate the usefulness of the set of factors chosen. Nurses in primary health care and their clients frequently express frustration at the quality of care provided and received in such settings. Development of a tool for identifying high-risk clients in prison who would benefit from increased interventions was reported. The procedure for identifying factors found important in assessing risk was described and suggestions for application to other settings made.
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Feinman, C. (1986). Women in the criminal justice system (2nd ed.). New York, NY: Praeger. Fogel, C.I. (1988). Health status of incarcerated women. Dissertation Abstracts International, 49, 1582A. Glick, R., & Neto, V. (1977). National study of women's correctional programs. Washington, DC: National Institute of Law Enforcement and Criminal Justice. Grossman, J. (1983). Comparison of male and female inmates under the department's custody: As of June 1983. Report prepared for the State of New York Department of Correctional Services. Jones, D. (1976). Health risk of imprisonment. Lexington, MA: DC Health & Company.
ASSESSING RISK STATUS
Lin, N., Dean, A., & Ensel, W. (1986). Social support, life events and depression. Orlando, FL: Academic. Marmot, M.G., Kogerunos, M., & Elston, P. (1987). Social/economic status and disease. Annual Review of Public Health, 8, 11-35. Milio, N. (1986). Promoting health through public policy. Ottawa, Canada: Canadian Public Health Association. Pearlin, L.I., Lieberman, M., Menaghan, E., &Mullan, J. (1981). The stress process. Journal of Heahh and Social Behavior, 22, 337-356. Radliff, L.S. (1977). The CES-D scale: A self-report depression scale for use in the general population. Applied Psychological Measurement, 1, 385-401. Roberts, R. (1980). Relationship of the CES-D in different ethnic contexts. Psychiatry Research, 2, 125. Speilberger, C.D. (1983). Manual for the state-trait anxiety inventory. Palo Alto, CA: Consulting Psychologist Press. Stevens, J. (1986). Applied Multivariate Statistics for the Social Sciences. Hillsdale, NJ: Lawrence Erlbarium Associates.
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