The nonspecific symptom screening method

Psychiatry and Primary Care Editor: Wayne

J. Katon,

M.D.

Recent epidemiologic studies have found that most patients with mental illness are seen exclusively in primary care medicine. These patients often present with medically unexplained somatic symptoms and utilize at least twice as many health care visits as controls. There has been an exponential growth in studies in this interface between primary care and psychiatry in the last 10 years. This special section, edited by Wayne J. Katon, M.D., will publish informative research articles that address primary care-psychiatric issues.

The Nonspecific Symptom Screening Method Detection of Nonpsychotic Morbidity Based on Nonspecific Symptoms Tirupati N. Srinivasan,

M.D., and Thanjavur

Abstract: Detection ofnonpsychotic morbidity in prima y cure patients presenting with nonspecific and somatic symptoms has been difficult because of several factors related to the patients, primary care clinicians, and working conditions of the overcrowded clinic. The available standardized screening questionnaires do not overcome many of these difficulties when used for routine clinical purposes. A screening method based only on nonspecific symptoms, which could be easily incorporated into the routine initial clinical work-up of a patient, was developed in this study and has been found to have good validity and reliability for screening nonpsychotic morbidity. The method of construction of the screen and its clinical applicability and limitations are discussed.

The rate of psychiatric morbidity in developing countries, ranging from 10.6% to 17.7% [l], is comparable to that in developed countries. With an estimated prevalence of l%-2% for psychotic illnesses in the community in countries like India [2], the problem of nonpsychotic illnesses is quite significant [3,4]. At the primary care level, morbidity has been observed to range from 14% to 63% [5-121, the majority comprising nonpsychotic illnesses. Nonpsychotic morbidity should not be ignored From the Department of Psychiatry, Sri Ramachandra Medical Colleee. Tamil Nadu Arasu Medical Sciences and Research Institute TTTkMARAI), Madras, India. Address reprint requests to: Tirupati N. Srinivasan, M.D., Assistant Professor of Psychiatry, Sri Ramachandra Medical College, TAMARAI Porur, Madras-600 116, India.

106 ISSN 0163~8343/91/$3.50

R. Suresh, M.B.B.S.,

D.P.M.

on the mistaken presumption that it is “mild” [13]. By the very fact of their high prevalence, these illnesses constitute a significant burden, not only to society, but also to the health care system. It has often been observed that patients with anxiety and affective disorders are high users of primary nonpsychiatric medical care facilities [14-171. A significant proportion (up to 50%) of these cases, however, remains undetected in general practitioners’ clinics and general hospital outpatient department services [6,10,13]. The failure of primary care personnel to identify such patients seems to arise mainly from their deficient knowledge about and ability to identify psychiatric phenomena, due largely to inadequacies in interview skills [18,19]. Added to this are the working conditions of primary care clinics, which are generally overcrowded and understaffed. The pressure of work and the length of time the doctor can afford to devote to individual consultations have been shown to affect the rate of reporting of psychiatric cases [6]. Another important factor influencing case detection is the nature of the complaints of patients with affective and anxiety disorders, which are often nonspecific and somatic in nature, e.g., body pain, weakness, fatigability, and insomnia [6,12, 20-231. Psychiatric phenomena are also reported in nonspecific forms such as unclear thinking [12] or forgetfulness. This nonspecific presentation General Hospital Psychiatry 13, 106-114, 1991 0 1991 Elsevier Science Publishing Co., Inc. 655 Avenue of the Americas, New York, NY 10010

The Nonspecific Symptom Screening Method

seems to be a universal phenomenon observed both at the community level [24-261 and in primary care clinical settings [6,10,13,20,21,23,2736]. These symptoms, which are also reported by the physically ill, are characterized by their longer duration (3 months and more) in the psychiatric population when compared to the physically ill [37]. In clinical practice, the physician tends to consider these nonspecific symptoms at their face value, often taking them as early indicators of some serious physical illness [6,32]. What follows is an array of nonspecific diagnoses and unjustified investigations and interventions. Only when the conditions fail to respond, and possibly after hospital work-ups prove negative, does the physician think in psychiatric terms [6]. Such a practice obviously can be an enormous burden on the health care delivery system as well as on the patient. A well-conducted, standardized psychiatric interview of all patients is the best way to detect psychiatric morbidity [38]. This obviously is not possible for routine clinical practice in primary care. Patients can be screened with an instrument like the General Health Questionnaire and its modifications [27,39-441, the Self Report Questionnaire [40], Goldberg et al.‘s 9-item method for screening anxiety and depressive disorders [41], and subsequently examined in detail. All methods of screening currently available, however, require the elicitation of specific emotional symptoms, in addition to somatic ones. Although the ability of the primary care worker to elicit and interpret emotional symptoms can be improved through training, this still may not overcome the patient’s tendency to present with nonspecific symptoms. Moreover, the process of completing these questionnaires in a busy clinic may prove too cumbersome for routine visits. Hence a screening method that could be easily incorporated into the clinical work-up and seeks to elicit only nonspecific symptoms could be useful in the primary care clinic in terms of both ease and speed of administration. In this study such a method was developed and standardized against a standard questionnaire (Self Report Questionnaire). The applicability of the method in different primary care settings is discussed.

Materials and Methods This study was conducted at a newly established public general hospital located on the outskirts of the city of Madras in southern India. At the

time of the study, the hospital provided only outpatient care, catered mainly to the semiurban and rural areas around the city, and was utilized by a patient population that was predominantly illiterate (about 65%), from the lowest economic stratum engaged in labor occupations (about 70%), and belonging to the Hindu faith (about 90%). Thus the clinical facility and the patients using it resembled any other primary health care facility in the country. The study was conducted in two stages. In the first stage of item selection, the nonspecific symptoms that could significantly differentiate the nonpsychotic, psychologically distressed patients from the physically ill were identified and incorporated into the Nonspecific Symptom Screen (NSS). In the second stage, the validity of the NSS method was established in comparison to the 20-item version of the Self Report Questionnaire (SRQ-20). The translated version of SRQ-20 in the Tamil language had been standardized at the study center in an earlier pilot study, and it had a high interrater reliability between the two authors (K coefficient = 0.85) and a high validity of 82% specificity and 90% sensitivity in identifying nonpsychotic, distressed patients at a cutoff score of 7. The method adopted for making the psychiatric and physical diagnoses was common to both stages of the study. Psychiatric diagnosis was made on the ICD-9 criteria [42] after conducting a standard psychiatric interview using the Indian Psychiatric Survey Schedule (ES) [43]. The IPSS is a structured interview schedule developed and standardized in India and has been used successfully in some of the major studies in India ~25,441. F or p ur p oses of the study, questions relating to nonspecific symptoms in the ES were excluded to maintain the blindness of the psychiatric interview. The ICD system was used instead of a more rigorous criterion, as we felt that this system is simpler to adopt for primary care work, and the results of the study would be more easily applicable to such settings. The following disorders were classified as nonpsychotic morbidity for the purposes of the study: the neuroses (ICD-300); personality disorders (ICD301); the psychophysiological illnesses (ICD-306, 316); psychalgia (ICD-307.8); adjustment disorders (ICD-308, 309); and depression NOS (ICD311). The physical diagnosis was made by the physician or surgeon, who was not aware of the psychiatric status of the patients. 107

T. N. Srinivasan

and T. R. Suresh

Table 1. Intergroup

comparison

of nonspecific

symptoms 2

Symptom

GpA/GpC

score

GpB/GpC

GpA/GpB

3.79” 2.52

4.35 2.55

0.65 0.63

2.52

3.02

0.99

1.65 3.74 2.74”

3.93 5.40” 5.39”

1.18 1.16 1.56

2.77”

5.24

1.75

3.06 4.14 2.03 2.73

6.11” 5.52 4.11” 4.55

1.90 0.26 1.02 1.43

1. Generalized

2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

body aches and pains Headache Pain in the chest Shortness of breath Fatigability Feeling weak Giddiness/ Dizziness Unable to work as before Insomnia Loss of appetite Forgetfulness

‘p < 0.01 to 0.001.

Patient Groups: GpA, pure psychiatric illness; GpB, physical + psychiatric illness; GpC, pure physical illness.

Patient Selection and Examination Stage 1. One hundred outpatients above the age of 15 years who registered as new cases at the general outpatient clinic were selected by a systematic random sampling. Three patients who were later found to have psychiatric disorders other than those studied were excluded and replaced by further samples without affecting the randomness of the selection procedure. The selected patients were first examined by one of the authors who evaluated for the presence of 11 nonspecific symptoms (see symptom list in Table l), which were scored as “present” only if they had been experienced for a period of at least 3 months. These 11 symptoms, many of which are featured in standard questionnaires like the GHQ and the SRQ, were chosen because they were the most common nonspecific symptoms reported by patients presenting to this facility. The terms used to elicit these symptoms were of common usage in this region (see Discussion) and hence no translation exercise was undertaken. After the nonspecific symptoms were scored, the other author made the psychiatric diagnosis, blind to the initial assessment. Diagnosis of phys108

ical illness was made by the physician/surgeon. The patients were divided into three groups, one with only psychiatric illness, another with only physical illness, and the third with both psychiatric and physical illnesses. These three groups were compared to one another with regard to the presence of each of the 11 nonspecific symptoms, using the two-tailed z-test for difference of proportions, taking the minimum level of significance required as 1%. Following this, a three-group discriminant function analysis was conducted using the Statistical Package for Social Sciences [45]. The symptoms that discriminated most significantly between cases and noncases were formed into the NSS method.

Stage 2. Two hundred outpatients were selected, as in the first stage, and were examined initially by one of the authors, who completed the NSS and SRQ-20. Psychiatric and physical diagnoses were made, and the patients were divided into two groups, one having psychiatric illness (with or without physical comorbidity), and the other having only physical illness. The validity of the nonspecific symptom screening method was measured using the formulas given in Appendix 1. The index of agreement between the NSS and the SRQ-20 in classifying cases and noncases was also determined. The validity of the NSS when there is low prevalence of psychiatric morbidity was also estimated, and the cutoff scores that could be adopted for clinical use were identified.

Results Stage 1 Of the 100 patients examined, 61 were identified as having a psychiatric disorder of the nonpsychotic type. Twenty of them had only psychiatric illness, and 41 had both psychiatric and physical illness (group B). The mean ages of the psychiatric group A (35.57 + SD 12.67 years) were not significantly different from those of the physically ill group C (31.35 + SD 11.14 years). There were significantly more females (N = 43) than males (N = 18) in the psychiatric group (x’ = 10.25, p < 0.001). Depression (neurotic depression and adjustment disorder with depression) was the most common diagnosis made (N = 45). There were 10 cases of anxiety, five of psychalgia and one of hyphochondriasis. Table 1 shows that seven of the 11 symptoms,

The Nonspecific Symptom Screening Method

Table 2. Discriminant

function

Symptom 1. 2. 3. 4. 5. 6.

Fat&ability Insomnia Forgetfulness Giddiness/Dizziness General aches and pains Feeling weak

analysis

Wilk’s lambda ~7value 0.61368 0.48488 0.45636 0.43862 0.42126 0.40963

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

i.e., generalized aches and pains, undue fatigabilgiddiness/dizziness, inability to ity, weakness, work as before, insomnia, and forgetfulness, were more prevalent in both the psychiatric groups A and B than in group C. The other four symptoms were either equally common in all groups (headache), or more in group B only (pain in the chest, shortness of breath, and loss of appetite). The discriminant function analysis given in Table 2 shows that six of the 11 symptoms discriminated significantly between the groups. The Wilk’s lambda value shows that these six symptoms explain nearly 60%, of the variance between the groups. Of them, the first three symptoms, i.e., undue fatigue, insomnia, and forgetfulness, explain nearly 55% of the variance. The six-item version of the NSS (NSS6) and the three-item version (NSS3) were formed by these six and three symptoms, respectively. Stage 2 The number of patients identified as having nonpsychotic illness was 103 (51.5%), with 42 having only psychiatric illness, and 61 having both physical and psychiatric illnesses. The remaining 97 patients had only physical illness. There was no significant difference between the mean ages of group A (34.8 2 SD 12.9 years) and group B (31.7 ? SD 12.7 years). The females again numbered more (N = 71) than the males (N = 32) in group A (xl = 14.76, p < 0.001). A prevalence rate of psychiatric illness by gender among the screened population showed that about 36% of the males and 64% of the females were affected. Depression was the most common problem identified (N = 75), as in the first stage. There were 19 cases of anxiety, 16 of psychalgia, and two of neurasthenia. A comparison was made between the sexes in the psychiatric group regarding the mean number of symptoms scored on the NSS-6. The females had a significantly higher mean score (4.75 2 SD 1.08)

than the males (4 2 SD 1.59, z = 2.44, p < 0.02). The validity of the SRQ-20 at a cutoff score of 7 was also measured using this patient sample and compared with the NSS.

Discussion Vulidit~ of the Method The validity coefficients given in Table 3 show the significant validity of the NSS method in identifying nonpsychotic morbidity. The NSS-6 has a high ability to identify cases (sensitivity) as well as noncases (specificity). The validity of the method changes with the cutoff score adopted, with a higher specificity and lower sensitivity at high cutoff scores of 5 or 6, and vice versa at lower cutoff scores like 1 or 2. This would mean that the NSS6 will yield more false-negative errors at higher cutoff points and more false-positive errors at lower cutoff levels. At its highest cutoff score of 6, the NSS-6 had a perfect specificity of 100% with no false-positive errors. The most useful cutoff point, at which both these errors are minimum, can be selected by observing the point at which the number of false-negatives exceeds the number of falsepositives [46]. Table 3 shows that this point on the NSS-6 is at the cutoff point 3, where the specificity is 84%) and the sensitivity is 89%, with a misclassification rate of only 13.5%. These validity indices are comparable to those of the SRQ-20 at its cutoff score of 7. It was also seen that the NSS-6 and the SRQ-20 have a high index of agreement of 85% in classifying cases and noncases correctly at their identified cutoff scores (Table 4). As the psychiatric group consisted of two subgroups (one with and the other without physical comorbidity), the proportion of cases that scored 3 or more symptoms in each subgroup was compared, and the difference was found to be not significant (two-tailed ztest score = 0.97, NS). Though the indices of specificity and sensitivity are constant properties of the screening method, for practical application of the screen they are not useful, and other applicable indices like the predictive values or likelihood ratios should be used [46]. The positive predictive value (PPV) and the negative predictive value (NPV) indicate the chances of the prediction of a case or a noncase, respectively, actualizing. Table 3 shows that these two indices of the NSS-6 are high at the cutoff score of 3. At this point the method has 85% accuracy in predicting a case and 887~ accuracy in predicting a

109

T. N. Srinivasan and T. R. Suresh

Table 3. Validity indices of NSS and comparison Screening criterion

with SRQ-20

Sp

Sn

FP

FN

PPV

NPV

Lpos

Lneg

MCR

1 2 3 4 5 6

68 77 84 87 96 100

99 97 89 80 59 21

32 23 16 13 4 0

1 3 11 20 41 79

77 82 85 86 94 100

99 96 88 80 69 54

3.09 4.22 5.56 6.15 14.75 infinity

0.015 0.039 0.13 0.23 0.43 0.79

16 12.5 13.5 17 23 40.5

NSS3 cut-off score

1 2 3

70 89 98

99 79 63

30 11 2

1 21 37

78 88 95

99 80 59

3.3 7.18 31.5

0.014 0.235 0.377

15 16.5 38.5

SRQ-20 cutoff score

7

89

91

11

9

90

91

8.27

0.112

10

NSS-6 cutoff score

Abbreviations: Sp, specificity (a); Sn, sensitivity (%); FP, false-positives (%); FN, false-negatives (W); WV, positive predictive value (%); NW, negative predictive value (%); Lpos, likelihood ratio for a positive result; Lneg, likelihood ratio for a negative result; MCR, misclassification rate (%).

noncase. The ability of the NSS-6 to correctly predict a case increases with an increase in the cutoff score, whereas a lower cutoff point increases its ability to predict a noncase. When all the six symptoms are present in a patient, the prediction of a case is absolutely true, with 100% accuracy. Clinically this would mean that when the patient has all the six symptoms of the NSS, he or she is bound to have a psychiatric illness, irrespective of whether there is coexisting physical illness or not. Though the predictive values are practically useful expressions of the validity of a method, they are not constant properties of the method, as they vary with the prevalence rate of the disorder in the population to which the method is applied. When the prevalence of the disease is lower, the PPV falls and the NPV increases. Therefore the predictive values of the NSS-6 discussed above accurately apply only for populations where the morbidity rate is at least 51.5%. The likelihood ratio (LR), which is independent of the disease prevalence, is a more useful way of expressing the predictive ability of a Table 4. Index of agreement SRQ-2o”

Agreement

on

Cases Noncases % Agreement

NSS and

NSS-6 (cutoff = 3)

NSS3 (cutoff = 2)

92 78 92 + 781200 85%

84 85 84 + 851200 84.5%

“SRQ-20 cutoff score of 7.

110

between

screening method. There are two indices of LR. One is the LR for a positive result (Lpos), which indicates the probability of the prediction of a case actualizing, and the other one is the LR for a negative result (Lneg), which indicates the probability of a noncase proving false [46]. A high Lpos value indicates the high probability of a prediction of a case actualizing, and a low Lneg value shows the low probability of a prediction of a noncase proving false. Table 3 shows that the NSS-6 has a high Lpos value at higher cutoff scores and a low Lneg value at lower cutoff points. At the previously identified cutoff point of 3, both the ratios are moderately significant. The validity of the shorter three-item version of NSS (NSS3) was found to be high at a cutoff score of 2, comparable to that of NSS-6 and the SRQ-20 (Table 3). At this cutoff point, the NSS3 also had a high degree of agreement with the SRQ-20 (84.5%) in classifying cases and noncases (Table 4). Thus it was observed that the shorter NSS3 had as much utility as the NSS-6. Though the threeitem version also has good validity, for maximal screening efficiency it is advisable to use the full version of the method, as was recommended by Goldberg in the use of the General Health Questionnaire [47]. The question of whether the NSS method applies with equal accuracy to men and women needs attention, as it has previously been noted that the males not only had a lower prevalence rate than the females, but also had a significantly lesser mean number of nonspecific symptoms. Hence the validity of the NSS-6 and the NSS3 were measured

The Nonspecific

Table 5. Validity

Method NSS-6

NSS-3

Cutoff score

Sn

PPV

NW

MCR

3’ 2 4

86 (84) 75 (89) 75 (85) 86 (88) 18 (13.5)” 84 91 76 94 13.5 93 56 82 79 20

2” 1 3

89 (89) 62 (79) 71 (88) 81 (80) 20 (16.5) 81 92 76 98 12.3 98 81 86 68 30.3

Prevalence rate

15%

“MCR for female

are for the whole

patients

psychiatric

group

given

Validity index

30%

Method

NW

(85) 98

56 (88) 99 93 96

(88)

(80)

for both sexes separately (Table 5). It was observed that at the identified cutoff scores of 3 (NSS-6) and 2 (NSS-3), the sensitivity of the WV is less for the males, who were misclassified more often than the females. A lowering of the cutoff score for the males on both versions of the NSS gave an improved sensitivity and NPV, with lesser degree of misclassification. An increase in the cutoff point lowered the sensitivity of the screen and increased the misclassification rates. Hence, in applying the NSS to a male population, a lower cutoff score than that used for females is recommended to yield comparable validity. It has already been noted that the predictive value of the NSS discussed above is not applicable to populations with lower prevalence rates. The prevalence rate observed in this study is quite high when compared to other studies. But several random surveys conducted at the study center yielded rates ranging between 50% and 60%. The commonly prevalent poor socioeconomic conditions and chronic physical illnesses that can contribute to psychiatric morbidity could explain the high prevalence rate observed in this study. The predictive values and the misclassification rate of the NSS-6 and NSS3 were projected for disease rates of 15% and 30% (which are the common rates reported in the community and primary care, respectively), using the LR values and other validity indices by the method given in Appendix 1. The results shown in Table 6 indicate changes in the validity of both versions of the NSS, with a steep fall in the PPV and an increase in the NW and misclassification rate at their previously identified

15 (13.5)

43 72

20

9

12.5 (16.5)

37

85

99

94

25.5

7

PPV

70

64 86

75

59

93

NPV

(85) 95

98 84

(88) 91

99

86

(88) 14.5 (13.5)

(80) 17 15 14 (16.5)

30

12.5

’ MCR for female patients 13.5% Abbreviations: ative predictive

NSS-3 score 2” 1 3

50

MCR

10.8%

NSS-6 score 3” 25

PPV

MCR

Abbreviations: Sp, specificity (%); Sn, sensitivity (%); PPV, positive predictive value (%); NPV, negative predictive value (96); MCR, misclassification rate (%). “Values in parentheses in Table 3.

Screening

Table 6. Validity of NSS at low disease prevalence

of NSS for male patients

Sp

Symptom

WV, positive predictive value (7%); NW, negvalue (%); MCR, misclassification ratio (%).

“Values in parentheses and given in Table 3.

observed

at prevalence

rate

of 51.5%

cutoff points. An increase in the cutoff point to 5 on the NSS-6 and 3 on the NSS3 yielded a better PPV and lesser misclassification with only a minimal fall in NW. Hence, in applying the NSS method to populations with lower disease prevalence, it would be more effective to use a high cutoff point. Though standard cutoff scores have been identified for the NSS, for practical application it is advisable to be flexible in choosing the cutoff score, depending upon the aim of the screening. If the screening is done for the purpose of community case identification, it is better to use a low cutoff score [35], like 1 or 2 on the NSS-6. This would be advantageous because the low false-negative errors and the high NW at low cutoff points would identify most of the cases in the population screened. The high false-positive cases can be excluded at subsequent detailed evaluation. The availability of a psychiatric care facility should also be considered in choosing a cutoff point. If the resources and manpower are scarce, a high cutoff point is advisable. The higher specificity and PPV would reduce the false-positive cases, which lessens the burden of conducting several detailed examinations.

Advantages

artd Limitations

The NSS method has several advantages for routine use in clinical practice. Important benefits in-

111

T. N. Srinivasan

and T. R. Suresh

elude its simplicity, avoidance of abruptness with the patient, and helpfulness to the busy practitioner. The method can easily be remembered by the clinician and incorporated into the routine initial clinical work-up of presenting complaints. Moreover, patients with an underlying psychiatric disorder presenting with nonspecific symptoms would not be likely to experience this kind of questioning as abrupt, nor would patients without an underlying psychiatric disorder. Thus, the problems of elicitation by the interviewer and reporting by the patient of emotional symptoms are avoided. The time taken to do the NSS screening can usually be counted in seconds, and in patients who present with multiple nonspecific complaints as the reason for consultation, screening could be even more quickly accomplished. In addition, as the method is very simple, nonprofessional health workers can also be easily trained in its routine usage. Another advantage of the NSS method stems from its language structure. The terms for symptoms on the NSS are those commonly used by patients; they are not translations from another language such as English or “linguistically correct” versions of the terms in the Tamil language. In a community such as the one described, where the vocabulary is often a product of several languages (including English) and local dialects, the use of terms translated from English to the regional language sometimes becomes inapplicable, though they may be linguistically correct. For example, there are several terms in the Tamil language to describe the feeling of weakness, but the English term “weakness” often itself elicited a better response than any Tamil term with an equivalent meaning. It is to be noted that use of locally prevalent idioms of distress in an assessment method will increase its sensitivity in assessing psychopathology, especially crossculturally, and that standard research instruments or translating practices do not tap locally used metaphoric meanings of symptoms [48]. Lastly, the NSS method can be applied with validity at all levels of health care systems with different disease prevalence rates by adopting the appropriate cutoff score. There are certain limitations apparent in the use of the NSS method. First, the NSS method will not identify nonpsychotic morbidity of less than 3 months’ duration because of the time frame used by the method. This limitation is probably not severe, as the prevalence of nonpsychotic morbidity of less than 3 months’ duration is possibly low in primary care settings. In a series of 439 nonpsy112

chotic patients detected by screening the outpatient population at the study center, we found that only about 7% had an illness of this short duration (unpublished report). The proportion of such cases presenting at a private general practitioner’s clinic may be higher, as patients generally seek treatment at a practitioner’s clinic before reaching a hospital service. The second limitation of the NSS method is in identifying certain nonpsychotic disorders that have not been present in this study population, such as obsessive-compulsive neurosis, hysteria, and personality disorders. But as the prevalence of these disorders is generally low in comparison to the more commonly occurring anxiety and depressive illnesses, the NSS could still be useful. The third limitation is in the application of NSS to nonclinical populations, as it has been developed and standardized in a clinical setting. However, it is highly probable that the same nonspecific symptoms as those included in the NSS instrument would differentiate nonpsychotic cases from normal, healthy people in the nonclinical community setting, though at a different cutoff score. In conclusion, the method of screening nonpsychotic morbidity based on nonspecific symptoms has been found to have high degrees of reliability and validity. This method could be of use for all health personnel in primary care settings in routine clinical practice. It overcomes some of the difficulties encountered in applying standardized screening questionnaires and psychiatric interviews for purposes of screening. This method needs further testing to establish its usefulness in different clinical and cultural settings. The authors thank Dr. Vijay Nagaswami, Schizophrenia Research Foundation (India), Madras, for assisting in the analysis of data.

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The Nonspecific

6. Shepherd M, Cooper B, Brown AC, Kalton G: Psychiatric illness in general practice. Oxford, Oxford University Press, 1966 7. Goldberg DP, Kay C, Thompson L: Psychiatric morbidity in general practice and the community. Psychol Med 6:565-569, 1976 8. Lipowski ZJ: Review of consultation psychiatry and psychosomatic medicine. II. Clinical aspects. Psychosom Med 29:201-204, 1967 9. Mari JJ: Psychiatric morbidity in three primary medical care clinics in the city of Sao Paulo. SOC Psychiatry 22:129-138, 1987 10. Nikapota AD, Patrick A, Fernando LHS: Aspects of psychiatric morbidity in the outpatient population of a general hospital in Sri Lanka. Ind J Psychiatry 23:219-223, 1981 11. Bagadia VN, Ayyar KS, Lakadwala I’D, Susainathan V, Pradan PV: Value of General Health Questionnaire in detecting psychiatric morbidity in a general hospital outpatient population. Ind J Psychiatry 27: 293-298, 1985 12. Sen B: Psychiatric phenomena in primary health care-Their extent and nature. Ind J Psychiatry 29: 33-40, 1987 13. Blacker CVR, Clare AW: Depressive disorders in primary care. Br J Psychiatry 150:737-751, 1987 14. Culpan R, Davies B: Psychiatric illness at a medical and surgical outpatient clinic. Compr Psychiatry 1:221-235, 1960 15. Yager J, Wells KB: Psychiatry and the primary care physician. In Gaind RN, Fawzy FI, Hudson BL, Pasnau RO (eds), Current Themes in Psychiatry, ~013. London, Macmillan Press, 1984, pp 237-264 16. Glass RM, Freedman DX: Contempo 85: Psychiatry. JAMA 254:2280-2282, 1985 17. Ford CV: The somatizing disorders. Psychosomatics 27:1-8, 1986 18. Goldberg DP, Huxley P: Mental illness in the community. London, Tavistock Publications, 1980 19. Maguire P: Psychiatrists also need interview training. Br J Psychiatry 141:423-424, 1982 20. Watts CAH: A long-term follow up of mental hospital admissions from a rural community. J R Co11 Gen Pratt 20:79, 1970 21. Widmer RB, Cadoret RJ: Depression in primary care: Changes in patterns of patients’ visits and complaints during development of depression. J Fam Pratt 7:293-302, 1978 22. Varma VK, Chaturvedi SK, Malhotra A, Chari P: Psychiatric aspects of chronic intractable pain. Ind J Psychiatry 25:173-179, 1983 23. Goldberg D, Richels J, Hesbacher P: A comparison of two psychiatric screening tests. Br J Psychiatry 129:61-67, 1976 24. Wadsworth M, Butterfield WJH, Blancy R: Health and sickness: The choice of treatment. London, Tavistock Publications, 1968 25. Carstairs GM, Kapur RL: The great universe of Kota: Stress change and mental disorder in an Indian Village. London, Hogarth Press, 1976 26. Giel R, Harding TW: Psychiatric priorities in developing countries. Br J Psychiatry 128:513-522, 1976

Symptom

Screening

Method

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Likelihood Ratios

For a positive result ( LpOS)= sensitivity/l-specificity For a negative result (L_) = l-sensitivity/specificity Validity at Different Prevalence Rates

Consider p = prevalence of cases q = prevalence of noncases L

Positive predictive value = ( L,,,p~ F, “+ 4

APPENDIX 1: Measurement of Validity Indices

4 Negative predictive value =

Quantities Involved in Measurement

Misclassification rate

Diagnosis Test Response Yes No Total

Noncase

Case

b d n,

a C

n,

Calculation of Validity Indices

Specificity Sensitivity False-positive rate False-negative rate Positive predictive value Negative predictive value Misclassification rate

114

= = = = = = =

din, aln,

b/n, or l-specificity c/n, or l-sensitivity a/m, d/m, b -I clN

[461

4 + (Leg x P)

1461

= (q x false-positive rate) plus 1381 (p x false-negative rate)

Total ml m2 N

APPENDIX 2: The Nonspecific Symptom Screen The following symptoms should be present for at least 3 months: 1. Fatigability Insomnia Forgetfulness Giddiness/Dizziness Generalized aches and pains Feeling weak

2. 3. 4. 5. 6.