Underestimated Needs for Lymphoma Patients: An Assessment Issue

Abstract

Background and aim of the work: The aim of the current study was to explore under-considered psychosocial needs for lymphoma cancer group. A model of the role of psychosocial factors and Stressful Life Events was operationalized.

Method: We used Discriminant Analysis to test predictive power of the model. 103 oncological patients (gender: 42.7 % vs 49.3 % of females 55.2 ±15.6 vs 53.7±14.9) were matched with 140healthy control groups in the study. The following instruments were utilized to conduct the study: the Florence Psychiatric Interview, Hospital Anxiety Depression Scale, Multidimensional Scale of Perceived Social Support, Beck Depression Inventory I, and Sense of Mastery.

Results: The model satisfied the assumption criteria and were significant (Ʌ= .665, χ2= 105.83, p< .001).

Conclusion: Stressful events, depression and anxiety were adequate markers of the psychological status of lymphoma patients. Our results point out the relevance of taking into account psychosocial factors in hematology.

Figure 1.

Psychoncological assessment model tested on haematologic cancer group.

Figure 2.

Standardized Function Coefficients of the model. Note. Standardized Discriminant Coefficients of the final discriminant function that separates out the Haematologic group (n = 103) from the Control group (n= 140). Standardized Discriminant Coefficients are the equivalent of beta weights in regression. Good predictors tend to have large values of the Standardized Discriminant Coefficients. In particular valid predictors (named Adequate Variables) are variables showing a Standardized Discriminant Coefficient higher than |.30|. In this model, adequate Variables are: Cumulative Effect of Early Life Events, Depression Scale HADS, Beck Depression Inventory, Others support, Family Support and Friend Support. Black Columns represent the variables able to discriminate

Figure 3.

Classification of those who belonged to the clinical group in model Note. The validity of Discriminant Analysis is tested by contrasting the power to discriminate provided by the Discriminant Model with a chance classification: reclassification of the Hematologic Cancer Group (n= 103) and of the Control Group (n= 140) obtained with the Discriminant Model. On the left we have reported the “improvement over chance criterion” (IOCC), showing that about 69.9% of the Hematologic Cancer patients are correctly reclassified by the Discriminant Function (gray bar, left). On the right re-classification is obtained with the cross-validation procedure. It is described as a ‘jack-knife’ classification, in that it successively classifies all cases but one to develop a discriminant function and then categorizes the case that was left out. This process is repeated with each case left out in turn. This cross validation produces a more reliable function. Here the difference between the results obtained with the two methods is minimal, as more than 67.7% of the Hematologic cancer patients are correctly reclassified with cross-validation (gray bar, left). Both procedures detect a valid discriminative power for the Model