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Future Oncol. (2009) 5(5), 72773810.2217/FON.09.43 2009 Future Medicine Ltd

High levels of distress have been well documented in many populations of cancer patients [1,2].Awareness of this problem is reected in the Canadian Strategy for Cancer Controls endorse-ment of Emotional Distress as the sixth vital sign in June 2004, with the implication that psychosocial distress be monitored and treated in all patients on a regular basis [3,4]. Likewise, in the USA, the National Comprehensive Cancer Network (NCCN) guidelines require that all distress be recognized and treated, and recom-mend screening for distress [101]. Considerable steps have since been taken to move the concept of screening for the sixth vital sign forward [58].In recent months, monitoring distress has been implemented as a standard of care by the Institute of Medicine in the USA [9], and has become part of the Canadian Council Health Services Accreditation 2009 guidelines for cancer populations [10].

Even with institutional-level commitment towards screening every patient for distress, there are still a few important factors to be con-sidered: rst, there is little, if any, consensus on the methods for identication of distress in cancer populations. Various institutions will often use different questionnaires, and even when there is consistency here, different cut-offs may be used to determine caseness [1116].Second, in some contexts, literacy levels or

comprehension of the host language may inuence test scores, thereby confounding risk categorization and/or caseness. In addition, a standard exclusion criterion for many research studies is the inability to speak the host lan-guage, which serves to further confound the generalizability of ndings. Third, quality con-trol of the implemented screening program will be particularly difcult at institutions facing a high patient volume. These institutions will likely either not have the resources to screen or will not have the resources to treat the screened patients [17]. Fourth, the nancial commitment especially recurring costs such as stafng to screen every patient would likely be quite large. Given the ever increasing cancer incidence and the long-term survival of patients, the required growth in infrastructure will be hard pressed to keep pace. Compound these concerns with a global recession, and costs become prohibitive.

The consensus on measures and cut-offs may be resolved with time [5,7]. With the increas-ing salience of distress screening, the money required for sufcient screening and treatment resources will come with time [18]. Of course, institutional support and buy-in from practitio-ners of the biomedical model will require a ben-et to be found. Clinical studies have repeatedly demonstrated benets from psychosocial care in terms of improved quality of life, decreased

Screening for distress (the sixth vital sign) in a global recession: sustainable approach to maintain patient-centered care

Bejoy C Thomas, Vasudevanpillai NandaMohan, Madhavan K Nair, John W Robinson & Manoj PandeyAuthor for correspondence: Department of Psychosocial Resources, Alberta Cancer Board Holy Cross Site, 2202 2nd St. S.W., Calgary, Alberta, T2S 3C1, Canada Tel.: +1 403 355 3213 Fax: +1 403 355 3206 [email protected]

A substantial volume of research on the psychosocial impact of cancer clearly indicates that patients are likely to experience emotional distress. There is also evidence that psychosocial interventions aimed at decreasing distress provide tangible cost offsets to cancer patients, caregivers and treating institutions. One seemingly major drawback in the setup and delivery of a fully fledged screening program for distress is the extensive pecuniary requirements. Given that the categorical need for distress screening may be confounded by financial limitations, especially in a time of global recession, a cost-effective alternative seems appropriate. The model proposed herein is not a substitute screening program, nor does it eliminate the need to allocate resources to address the identified risks. It does, however, offer a cost-effective alternative to implement a high-risk distress patient identifying process, quite similar to algorithms used in screening for prostate cancer.

Keywords

distress malignant neoplasm patient-centered care screening sixth vital sign

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Future Oncol. (2009) 5(5)728 future science group

distress and better treatment adherence [1,2,1921].Unfortunately, these benets may be seen as iso-lated and not in conjunction with cancer con-trol outcomes. Consequently, even when cancer institutions commit ideologically to distress screening, less than 20% routinely screen all cancer patients [17]. The key to implementation and sustainability of screening may include a pared down, more cost effective delivery system that is, synchronized with the activity of the treating team.

Economical approachThe usual approach to identifying patients who are experiencing higher distress levels and who would benet from psychosocial intervention is to administer a screening questionnaire to every patient. Those that the instrument identies as most likely to actually be experiencing high distress are then further assessed. The question is: might there be a more efcient way of iden-tifying those who truly need assistance without administering a questionnaire to every cancer patient? A possible solution may be to identify high-risk patient groups and focus resources on those groups. This approach is similar to the strategy used to screen for prostate cancer. Not every person with a prostate is administered a prostate-specic antigen (PSA) test. This kind of screening is reserved for groups that have been identied as being at highest risk of having pros-tate cancer. Similarly, our recommendation is that the administration of psychosocial screen-ing questionnaires be reserved for those at high risk of distress.

Research has been undertaken to establish risk factors for prostate cancer, which are now used to identify men at high risk who would benet from PSA screening. In the context of distress screening, might it not be rational to establish high-risk groups and focus our screening efforts on those groups (FIGURE 1)?

Test of principle The objective of the present manuscript is to test the alternative pathway, the prescreen model, as seen in FIGURE 1, the key ingredient being a regression model. Therefore, we have used an existing (relatively robust) dataset as a working example to test this principle. Each of the methodology subsections will be broadly divided into two parts, the rst being the work-ing example where the principle was tested within the limitations of the dataset available and the second, our recommendation if one wished to repeat this exercise.

Patients & methodsMeasureWorking exampleThe Distress Inventory for Cancer version 2 (DIC2) total score [2224] was used. Distress was dened as a multidimensional construct, which, on a global state, encompasses an area beyond the boundaries of normal feelings of sadness and fear, but well before a clinical state of anxiety or depression. The DIC2 has an internal consistency of 0.87, and that of the subscales ranged from 0.72 to 0.83. Covergent/divergent and criterion validity have also been established [22,24,25].

RecommendationVery recent literature has noted the efcacy of existing distress screening tools [8,26,27]. An established tool with validated cut-off scores would be advantageous. However, in the absence of such, the clinician would have to use what is available, as in the working example, in order to facilitate the process.

PatientsWorking exampleA total of 760 patients were approached at the Regional Cancer Centre, Trivandrum, Kerala, India, for a DIC2 psychometric/outcomes study. The study was approved by the institu-tions research board and ethics committee. This study sample was a random distribution of those attending the outpatient and inpatient settings. Head and neck cancers were well rep-resented, given that this was the most preva-lent cancer, followed by breast and other sites. Of these, 723 patients had responded to more than 80% of the items in the DIC2 and, thus, constituted the nal sample. Sociodemographic information was provided by the respondents, and tumor site, disease staging and treatment information were obtained from medical records (see TABLE 1 for a list of variables and sample proportions).

RecommendationThe sample should be representative of the pop-ulation seen in the respective institution. This process has been well dened in the statistical and epidemiological literature, and will there-fore not be discussed in detail here. Every effort must be taken to capture as much, as well as complete, information as possible be it psy-chosocial or demographic, or symptom- and treatment-related in order to enable a more robust regression model.

Research Article Thomas, NandaMohan, Nair, Robinson & Pandey

www.futuremedicine.com 729future science group

Statistical analysisThe objective is to determine if a patient is likely to be at risk for caseness (here higher distress) or not. Step 1: identify the key determinants of the outcome univariate analysis (analysis of vari-ance [ANOVA]) was carried out to identify the determinants of the distress scores. Bonferroni correction was used here to control for mul-tiple p-value testing. Step 2: identify the key predictors of the outcome multinomial logis-tic regression was used to identify signicant independent predictors of the outcome variable. The 33.33rd and 66.66th percentiles were used to categorize the total distress scores into low, moderate and high, since the DIC2 does not

have a specic cut-off score(s). Study parameters that were identied as statistically signicant in the univariate analysis were included in the multinomial logistic regression equation.

RecommendationIf a tool with established cut-off scores is used thereby creating a dichotomy steps 1 and 2 could be substituted with a binomial logistic regression with a stepwise method (preferably for-ward conditional) to identify the key predictors of the outcome. Step 3 would then have to fol-low. Step 3: identify the interaction effects of the outcome predictors a binomial logistic regres-sion was used to develop the hazards ratio-based

Figure 1. Algorithm incorporating identication of high-risk distress populations in distress screening.

A ll cancer pa tien ts

U sua l pa thw ay - E veryone

ge tsscreened

S creen ing bystandard ized tools and

m easures

A ssessm entand/or triage

Yes N o

H igh D istressleve ls?

P re -screen M ode l cra fted on p rio r da ta

Yes N o

A t risk fo r h igherd is tress?

S creen at a la te r tim e

R ecom m endeda lte rna tive pa thway

All cancer patients

Recommendedalternative pathway

Prescreen modelcrafted on prior data

No Yes

No Yes

Screen at a later time

Screening by standardizedtools and measures

High distress levels?

Assessmentand/or triage

Usual pathway everyone getsscreened

At risk forhigher distress?

Cancer distress model Research Article


Table 1. Variables short-listed after the ANOVA and multinomial regression analysis.

Univariate analysis Multivariate logistic analysis

ANOVA Multinomial regression Binary regression

Demographic variables (Variable included if signicant in the analysis from the previous column)

Gender Gender* Gender*

Female (366)

Male (357)

Age Age* Age*





Demographical variables (Variable included if signicant in the analysis from the previous column)

Number of unmarried children

None (184)

Some (161)

All (281)

N/A (97)

Proximity to the treatment center


model. The total low and high distress scorers were retained as the binary groups of the depen-dent variable. Variables that were identied as independent predictors of high levels of distress in the multinomial logistic regression (step 2) were selected for this model. The independent variables

were entered one at a time, and the respective odds for higher distress levels tabulated. The process was then repeated with all possible paired combi-nations of subgroups of the independent variables as interaction terms. Subsequent interaction terms were then created using triplet, quadruple and




Disease-related variables (Variable included if signicant in the analysis from the previous column)

Composite staging Composite staging* Composite staging*

I (86)

II (192)

III (337)

IV (69)

X (39)

Months between registration & interview


so on combinations, until all possible subgroup combinations of the independent predictors of distress were exhausted. The HosmerLemeshow goodness-of-t test statistic was used.

Step 4: the process in step 3 creates a substan-tial amount of output. In the case of the working example it was more than 250 unique variable combinations and respective risk scores. Clearly, an effective means of identifying the risk of a patient from a very large table (35 pages in the working example) is needed. For this we assigned each of the variable combinations, and their respective risk scores, a unique number. A HTML/Java program was written to collect the input variables, calcu-late the unique combination and then retrieve the respective risk value (the HTML le of the work-ing example is available on request). Creating this interface maximizes the productivity, as well as simplies, the prescreen model.

ResultsPreparing the data: steps 1 & 2With 26 variables being studied (demographic, disease- and treatment-related), Bonferronis correction lowered the D for individual tests to 0.0019231, to get an overall D-level of 0.05.

Demographic variablesFour of the 11 demographic variables (36.4%) were signicant in both the univariate and multivariate analysis (TABLE 1).

Gender

Signicantly higher distress (F: 24.89; p = 0.001) was observed in females (mean: 31.9) com-pared with males (mean: 26.7). The multivari-ate analysis also showed a 2.6-times increased risk of higher distress (hazard ratio [HR]: 3.6; 95% CI: 1.77.8) compared with males (TABLE 2).

Age

Patients below 44 years of age (mean: 32.6) had signif icantly greater overall distress (F: 12.1; p = 0.001) than patients who were older (4457 years: mean = 29.1; t 57 years: mean = 26.2). Patients in the age group of 4457 years also had signicantly greater over-all distress compared with patients 57 years or above. The multivariate analysis also indicated a signicant increase in risk for higher distress in patients younger than 44 years (HR: 2.1; 95% CI: 1.23.6) compared with patients above the age of 57 years (TABLE 2). The younger patients also showed an increased risk for moderate dis-tress levels (


indicated these patients to be at signicantly greater risk for higher distress levels (HR: 2.6; 95% CI: 1.64.0) than patients whose treatment plan did not include surgery (TABLE 2).

Preparing the data for the model: step 3Steps 1 and 2 shortlisted six of the 26 variables for the nal predictor model (TABLE 1, third column). Using the HosmerLemeshow goodness-of-t test statistic it was noted that in nearly 45% of the combinations, the F2 statistic had the desirable

outcome of nonsignicance. The binomial logis-tic model was run with all (more than 250) possible paired combinations and the outcomes tabulated with each combination and the result uniquely coded.

Preparing the program: step 4The HTML/Java computer program leads the user through a series of questions concerning the variables that were found to be possible indicators of greater likelihood of distress. The

Table 2. Predictors of overall distress: multivariate analysis using multinomial logistic regression.

Variable Grouping High distress: HR (CI) Moderate distress: HR (CI) Low distress: HR (CI)

Gender Female 3.622* (1.687.79) 1.621 (0.783.35) 1

Male 1 1 1

Age


respondent is prompted to choose one option for each of the set of six variables (e.g., the respon-dent will have to choose male or female from the variable name gender) (FIGURE 2 and see examples below).

Example 1: higher risk for distressIn case the selected variable combination indicates an increase in the risk for higher distress, (e.g., female, 57 years, high income, no surgery, regu-lar income and stage II disease) indicates a lower risk for higher distress, the following is returned depending on the context: This patient is pre-disposed for a statistically signicant 0.31-fold lesser likelihood (95% CI: 0.1570.613) for higher levels of distress.

Example 3: the risk for distress cannot be identiedDespite the large sample size, there may be a unique combination in the primary dataset that does not have sufcient patient numbers for a computation. In such an event, the following is returned: The patients risk for higher levels of distress cannot be computed, based on the present demographic, disease and treatment parameters. Kindly have the patient take the distress test.

DiscussionThere is evidence that with unresolved emo-tional needs, cancer patients are more likely to use community health services and to visit emergency facilities [20], place higher demands on scarce care-provider resources, and are more likely to be offered third- and fourth-line chemotherapy [19]. Several pharmacologic and nonpharmacologic interventions have been shown to be effective in preventing or relieving distress in cancer patients [28,29]. There are also studies that have demonstrated the benet of psychosocial care with no increased institutional costs [30] up to a 25% reduction in billings to the medical system [31].

The distress model approach presented here was initially conceptualized in the context of a ter-tiary cancer care center having more than 10,000 new case registrations a year, and with limited resources in implementing, let alone sustaining, a comprehensive (all patient) screening program. The result was that patients could be categorized to be either high risk or not at high risk, based on relatively enduring sociodemographic variables without a questionnaire. This process, subsequent to creating the database and front-end application, would take less than 1 minute.

The population could be sampled again at another time period (e.g., 6 months to 1 year later) to observe if there are changes to the model. Alternatively, the study cohort could be homo-genized to specic time points, creating multiple models specic to the patients position on the treatment continuum. The implication here is that intermittent hands-on screening procedures could be used to tweak a system that is able to identify those at immediate risk, while utilizing minimal daily resources. There are a few factors to consider regarding the model. First, the sample size of the population: having a large sample size

Figure 3. Graphic user interface results pop-up screen.

Figure 2. Graphic user interface risk calculation page.



upon which to base the model is crucial. The larger and more representative the sample, the more generalizable the model. Selective sampling could be used here to control for low-incidence cancer sites. The model would then be dependent on whether a difference in distress levels exists and is based on the site of the disease.

Second, the point of assessment: the pres-ent model utilized a cross-sectional population; therefore, the results are limited in the knowl-edge of the distress trend over time. However, this

Executive summary

IntroductionHigh levels of distress have been well documented in cancer populations.Emotional distress is acknowledged as the sixth vital sign.Monitoring distress has been implemented as a standard of care for cancer populations; however, even with institutional commitment

towards screening every patient for distress, there are still a few important factors to be considered. First, there is little consensus on the methods for identication of distress in cancer populations. Second, literacy levels/comprehension of the host language may inuence test scores, thereby confounding risk categorization/caseness. Third is the possible lack of resources to screen or treat screened patients, particularly at institutions with a high patient volume. Fourth, is the need for substantial nancial commitment.

The key to implementation and sustainability of screening may include a pared down more cost-effective delivery system.

Economical approachRecommendation is to administer psychosocial screening questionnaires to those that are at high risk of distress. A possible solution is to identify high-risk patient groups, similar to the strategy used to screen for prostate cancer.Research has gone into establishing risk factors for prostate cancer, which are now used to identify men at high risk who would benet

from prostate-specic antigen screening. Similarly, might it not be rational to establish high-risk distress groups and focus our screening efforts on those groups?

Test of principle The objective of this article is to test the alternative pathway of a prescreen model.A working example has been used for the process; recommendations are also made.

Patients & methodsThe Distress Inventory for Cancer version 2 (DIC2) total score was used. A total of 723 patients responded to more than 80% of the items in the DIC2.Sociodemographic, disease staging and treatment information were also collected.Analysis involves rst identifying the key determinants of the outcome with univariate analysis; second, identifying the key predictors of

the outcome with multinomial logistic regression; third, identifying the interaction effects of the outcome predictors with a binomial logistic regression; and fourth, creating an interface, and computing the risk of new patients using the regression model.

ResultsA total of 26 variables were studied (demographic, disease- and treatment-related), of which steps 1 and 2 shortlisted six for the nal

predictor model.A HTML program prompts the user to select a combination of the six variables and computes the respective risk score.

DiscussionThe distress model approach was initially conceptualized in the context of a large, high-volume tertiary cancer care center with limited

resources in implementing, let alone sustaining, a comprehensive screening program. The present manuscript highlights a moderate maintenance, low-cost alternative to identify population groups at high risk for distress

that might allow institutions to screen and address their needs despite limited fund allocations. Patients could be categorized to either high risk or not at high risk based on relatively enduring socio-demographic variables without

a questionnaire. This process, subsequent to creating the database and front-end application, would take less than 1 minute. The population could be sampled again at another time period (e.g., 6 months to 1 year later) to observe if there are changes to the model. The model does not replace the distress screening process, but does attempt to simplify it and facilitate sustainability.Some of the limitations and considerations to the model are discussed.

Conclusion The distress model is one attempt to bridge the gap between the need for distress screening and resource insufciency to screen

every patient.

aspect could be built into the model. Third, one limitation is that the success of this process may be dependent on establishing the ratio of false-positives/false-negatives. Obviously the next step in the model the use of the actual screening measure and/or the assessment (either a more comprehensive questionnaire battery or a clini-cal interview) would identify the false-positives (see FIGURE 1). False-negatives, on the other hand, may be more difcult to measure, but could be carried out given sufcient resources.



A proxy to identifying true positives could be approached by establishing a causeeffect rela-tionship with a tangible measure, say a treatment outcome or health behavior, that is relevant to the treating team. In an earlier report from the same data, it was observed that those scheduled for treatment with curative intent, or those with no evidence of disease at the last follow-up but who reported signicantly higher levels of dis-tress, were at a signicantly greater risk of being lost to follow-up or treatment within 6 months post-assessment [24]. Besides the probability of obtaining a higher distress score, a tangible ben-et to the treatment team was that this risk could translate into the measurable possibility of those patients forgoing cancer treatment or follow-up, making a difference to their cancer outcome [24].Fourth, the high-risk population identication process could be biased to the institution it is cre-ated in. The implication here is that the relatively enduring characteristics that identify high-risk populations in one cancer center may not gener-alizable to another.

This paper highlights a moderate maintenance, low-cost alternative to identify population groups at high risk for distress that might allow institu-tions to screen and address patient needs despite limited fund allocations. Of course, screening and subsequent denitive management are necessary steps in the process of addressing patient distress the cost offsets it brings are documented [30,31].Inevitably, resources must also be made available, but reducing the denominator of the population we have to target would increase our success rates. There is little doubt that distress screening is going to be part of cancer practice [5,10,18], and given a large funding pool, this could be carried out more elaborately at any institution.

Conclusion & future perspective The distress model is one attempt to bridge the gap between the need for distress screening and resource insufciency. Clearly, the model is not

the ultimate solution; however, it may, in the absence of elaborate funding, serve the popu-lation in question. A relatively low-cost, high-risk distress identication program may be the rst step towards generating buy-in of stake-holders (administration, funding agencies, and the frontline healthcare providers) to improve patient-centered care.

AcknowledgementsThe authors acknowledge Joshua Lounsberry for his assistance with editing and structuring of the manuscript.

Financial & competing interests disclosureDr Bejoy Thomas is a postdoctoral fellow (20072009) funded by the Alberta Heritage Foundation for Medical Research, Alberta, Canada. Dr Thomas was awarded The Bultz Student Award Series for Best Student Oral Research Presentation, Canadian Association for Psychosocial Oncology (CAPO), Halifax, Nova Scotia, Canada in 2008, presenting the manuscript Anyone can screen for distress with a million bucks: A case study of a potentially cost-effective screening methodology. This work comprises part of the doctoral (Applied Science) work of Dr Thomas, Modeling Distress in Cancer patients: A Psycho-Futuristic Approach University of Kerala, India, 2006. The authors have no other relevant afliations or nancial involvement with any organiza-tion or entity with a nancial interest in or nancial conict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

BibliographyPapers of special note have been highlighted as: of interest of considerable interest

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Afliations Bejoy C Thomas

Department of Psychosocial Resources, Alberta Cancer Board Holy Cross Site, 2202 2nd St. S.W., Calgary, Alberta, T2S 3C1, CanadaTel:.+1 403 355 3213Fax: +1 403 355 [email protected]

Vasudevanpillai NandaMohanDepartment of Futures Studies, University of Kerala, Trivandrum, Kerala, India

Madhavan K NairSUT Institute of Oncology, Sree Utharadom Thirunal Hospital, Trivandrum, Kerala, India and,Division of Radiation Oncology, Regional Cancer Centre, Trivandrum, Kerala, India

John W RobinsonDepartments of Oncology and Clinical Psychology, University of Calgary, Alberta, Canadaand, Tom Baker Cancer Centre, Calgary, Alberta, Canada

Manoj PandeyDepartment of Surgical Oncology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India


Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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