Control preferences in treatment decisions among older adults — Results of a large population-based study

    Control preferences in treatment decisions among older adults — ¡!–[INS][r]– ¿R¡!–[/INS]–¿esults of a large population-based study Sabine Lechner, Wolfgang Herzog, Friederike Boehlen, Imad Maatouk, Kai-Uwe Saum, Hermann Brenner, Beate Wild PII: DOI: Reference:

S0022-3999(16)30209-4 doi: 10.1016/j.jpsychores.2016.05.004 PSR 9159

To appear in:

Journal of Psychosomatic Research

Received date: Revised date: Accepted date:

9 February 2016 6 May 2016 8 May 2016

Please cite this article as: Lechner Sabine, Herzog Wolfgang, Boehlen Friederike, Maatouk Imad, Saum Kai-Uwe, Brenner Hermann, Wild Beate, Control preferences in treatment decisions among older adults — ¡!–[INS][r]–¿R¡!–[/INS]–¿esults of a large population-based study, Journal of Psychosomatic Research (2016), doi: 10.1016/j.jpsychores.2016.05.004

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ACCEPTED MANUSCRIPT Sabine Lechnera, PhD, Wolfgang Herzoga,, MD, Friederike Boehlena, MD, Imad Maatouka, MD, Kai-Uwe Saumb, PhD, Hermann Brennerb, MD, Beate Wilda, PhD

Control preferences in treatment decisions among older adults –

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results of a large population-based study

Running head: Control preferences among older adults

of General Internal Medicine and Psychosomatics Im Neuenheimer Feld 410, 69120 Heidelberg Medical University Hospital, Heidelberg, Germany

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a Department

of Clinical Epidemiology and Aging Research, Im Neuenheimer Feld 581, 69120 Heidelberg German Cancer Research Center, Heidelberg, Germany

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b Division

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Email-Adresses: [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected]

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Corresponding author:

PD Dr. sc. hum. Dipl.-Psych. Dipl.-Math. Beate Wild Department of General Internal Medicine and Psychosomatics Medical University Hospital Heidelberg Im Neuenheimer Feld 410, 69120 Heidelberg Phone: +49 (0) 6221 56 8663 / Fax: +49 (0) 6221 56 5749 E-mail: [email protected]

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Abstract

Objective: Older adults appear to be a specifically vulnerable group that could benefit

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considerably from the assessment of their decision-making preferences. The aim of this study

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was to estimate prevalence rates and to explore characteristics of control preferences in a

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population-based sample of older adults.

Methods: Data was derived from the 8-year follow-up of the ESTHER study - a German epidemiological study in the elderly population. n=3124 participants ages 57 to 84 were visited

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at home by trained medical doctors for a comprehensive assessment regarding various aspects of their life. The German version of the Control Preferences Scale (CPS) was used to assess

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decision-making.

Results: Most of the participants reported a preference for an active role in the decision-making process (46%, 95%-CI [44.3; 47.9]), while 30.0% [28.4; 31.5] preferred a collaborative role, and

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23.9% [22.4; 25.5] a passive role. Participants aged ≥ 65 years preferred a more passive role in

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decision-making compared to persons aged < 65 years. Participants with clinically significant depression symptoms (CSD) preferred significantly more often a passive role compared to those

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without CSD. Similarly, multimorbid patients preferred a passive role compared to people with none or one chronic disease. Conversely, in groups with active or collaborative control preferences the morbidity index was lower compared to the group with passive control

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preferences.

Conclusion: Results indicate that physical and mental health in the elderly are associated with the preference role. It should, however, be investigated whether multimorbidity or mental diseases influence the treatment preference of older adults.

Keywords: control preferences scale, CPS, decision-making, elderly, population-based study

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1. Introduction

In the past decades, the involvement of patients in treatment decision making has been of central

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interest. To date, many studies have investigated control preferences regarding decision making

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among patients and the possible benefits of involvement in decisions such as improved patient satisfaction and health outcomes [1, 2]. Meanwhile the shared decision-making approach is a widely

decision-making [3, 4].

Medical Care and Control Preferences in the Elderly

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used method where both the consumer and provider are involved in providing information and

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Among aging people, it is common to have more than one chronic condition. Results of a populationbased study indicate that 67.3% of the German population aged 50 to 75 suffers from multimorbidity

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[5]. The co-occurrence of mental disorders frequently aggravates the course of multimorbidity in older age [6-8]. When taking into account their experience of many social challenges, older adults appear to be a specifically vulnerable population that is more likely to face complex medical decision making.

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They could therefore benefit considerably from the assessment of their preferences: whether or not to

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participate in treatment decision making. Due to a higher bio-psycho-social burden, it is possible that older adults differ from younger adults in regard to their treatment preferences; it is also conceivable that treatment preferences change in advanced age along with other changing conditions.

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Understanding the control preferences and their associated factors in older people could help us to better deal with complex medical situations as well as improve the individualization of both care and clinical outcomes [9-11]. However, to date, studies that have evaluated the control preferences of older

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adults are scarce. Furthermore, previous studies regarding control preferences have largely investigated selected patient samples. To date, there are only a few studies that have investigated the associations between control preferences and multimorbidity or mental disorders in a large sample of older adults [5].

The Control Preferences Scale The Control Preferences Scale (CPS) [12] is a reliable and widely used instrument to measure patients’ decision making preferences regarding medical treatment. The Control Preferences Construct is defined as “the degree of control an individual wants to assume when decisions are being made about their medical treatment” (p. 21). Thus, according to this definition, the control preferences of individuals differ from their request for information. Studies that used the CPS in various patient groups (e.g. cancer, asthma, hepatitis C) revealed inconsistent results regarding the factors that influence preferences of patients. Most studies reported that the younger and better educated patients – and women – more often preferred an active role in decision-making compared to older or less educated patients or men [13-17].

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However, other studies failed to show that gender, age, or educational status were significantly associated with control preferences [18]. Anderson et al. [19] found an association between control preferences and mental disorder symptoms in intensive care unit patients: the more passive the role in decision-making, the higher the amount of anxiety and depressive symptoms. Moise et al. [20] found

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similar results in patients with depression and a comorbid illness. Results of other studies [21-23]

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indicated that most adult patients with mental illnesses preferred a collaborative role in decision-

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making.

Regarding multimorbidity Schneider et al. [24] reported that patients with chronic and severe diseases had the lowest scores in preference for participation in the medical decision-making process. Efficace et al. [17] found that patients with at least one comorbid disease preferred significantly more

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collaborative or passive control in the decision-making process than active ones.

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Purpose

The objectives of the present study were, (a) to estimate the prevalence rates of the various control preferences (active, passive, collaborative) and (b) to determine the associations between control preferences, demographic factors, mental disorders, and multimorbidity in older adults. We

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hypothesized that decision-making preferences according to the CPS would be related to age group,

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sex, educational status, and multimorbidity. Furthermore, we hypothesized that older adults with depressive symptoms would more often prefer a passive role in decision-making than participants

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without depressive symptoms.

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2. Method

Study sample The data were derived from the eight-year follow-up of the ESTHER study – a population-based

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cohort study of older adults in Germany [25, 26]. The study was approved by the ethics committees of

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the University of Heidelberg and of the medical board of the state of Saarland, Germany. Informed

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written consent was obtained from all participants.

At the baseline of the ESTHER study (between July, 2000 and December, 2002) in the federal state of Saarland, 9949 participants were recruited by their general practitioners in the course of a health check-up that is offered biennially to older adults in Germany. The ESTHER study sample was shown

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to be representative with respect to both demographic variables and chronic diseases of the general German population [27]. Follow-ups were conducted 2, 5, and 8 years after recruitment. At the beginning of the 8-year follow-up of the ESTHER study 8770 participants were still alive. Of

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these, 505 participants were not able to complete a standardized questionnaire – leaving 8265 possible participants. All in all, between 2008 and 2010, 6086 older adults participated in the third 8-year follow-up. All participants of the eight-year follow-up were asked if they would take part in a longer

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home visit to be conducted as personal interviews and a geriatric assessment. Of the 6086 ESTHER

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participants, 3124 (51.3 %) agreed to be visited at home. The home visits served as a comprehensive assessment tool regarding functional status and control preferences, as well as medical,

Measurements

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pharmacological, socio-economic, and psychosocial aspects of their life.

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The German version of the Control Preferences Scale (CPS) [12, 28] was used to assess the decisionmaking preferences of the home visit participants. The CPS was developed by Degner et al. [12] to assess the preferences of patients regarding their role in treatment decision making. It consists of five cards, each of them with a statement and drawing about decision making preferences ranging from fully active to fully passive. In our study, we added headings in capital letters on the cards to facilitate understanding for the older adults. The headings were approved by two experts of the shared decisionmaking research. Figure 1 shows the five CPS cards used in our study.

- Please insert Figure 1 about here -

The CPS has been evaluated in a variety of patient populations, e.g. cancer [14], chronic hepatitis [15] or elderly patients [29]. It has been demonstrated to be a useful, easily understandable and administered, reliable instrument that generates valid data to measure patients’ preferences regarding medical decision making [30, 31].

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We adapted Zhang´s method [15] to administrate the CPS: The study doctors showed the participants the five cards and asked to bring them into a rank order. The first card in each ranking order represents the patients’ most preferred role whereas the last card represents the least preferred role the patient wishes to have in medical decision-making. For example, EDCBA represents the preference order of

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persons that strongly desire to leave all decisions regarding their treatment up to their doctor.

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Covariates

Depressive symptoms were assessed using the 8-item Patient Health Questionnaire depression scale (PHQ-8) [32].The PHQ-8 consists of eight of the nine Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) - diagnostic criteria for major depressive disorder. Scores of the

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PHQ-8 range from 0 to 24, with higher scores indicating a higher severity of depressive symptoms. Test-retest reliability of the PHQ depression module ranged between 0.81 and 0.96 [33]. A cut-off point of ≥10 is recommended for the detection of any depressive disorder, demonstrating a sensitivity

significant depressive symptoms (CSD).

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of 87% and a specificity of 76% [34]. Participants with a score ≥ 10 were defined as having clinically

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Self-perceived cognitive impairment was assessed by the three following questions: (1) “Lately, I

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often confuse names, phone numbers, and dates“, (2) “I’ve often been misplacing things lately “, and

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(3) “I often forget names and numbers lately “(0 = no; 1 = yes, sometimes; 2 = yes, often, always).

Chronic illness burden was rated by the general practitioners of the respondents by using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) [35]. The CIRS-G is based on the Cumulative Illness Rating Scale [36] which is a well-established measure of multimorbidity. 14

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categories refer to clinically relevant physiological systems and psychiatric illnesses: 1. heart, 2. vascular, 3. hematopoetic, 4. respiratory, 5. eyes, ears, nose, throat and larynx, 6. upper gastrointestinal tract, 7. lower gastrointestinal tract, 8. liver, 9. renal, 10. genitourinary, 11. musculoskeletal/integument, 12. neurological, 13. endocrine/metabolic and breast, and 14. psychiatric disorder. These categories are rated on a five-point severity scale ranging from 0 (no problem) to 4 (extremely severe). Several indices can be generated: morbidity index (MI) as sum of the categories 1 to 14 (sum score 0-56), somatic morbidity index (SMI) as a sum of the categories 1 to 13 (sum score 0-52) and relevant somatic morbidity (RSK) as number of the categories 1-13 with ratings 3 or 4.

Multimorbidity was defined as the presence of more than one chronic disease. We categorized a participant as being multimorbid by using the INTERMED interview. The INTERMED is a semistructured interview that classifies the information into four domains (biological, psychological, social, health care) and three points in time (past, present, future) [37]). It has proven to be a reliable instrument that generates valid data to measure complex health care needs [20, 38, 39]. Scores of each

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domain and time point are added to a sum score ranged 0-60. A score of 3 in the Item 1a (Chronicity) of the INTERMED interview for the elderly (IM-E) [40] indicates that multimorbidity is present [41].

A cancer diagnosis was given according to the questionnaire that participants had completed. In this

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questionnaire, all participants were asked whether new diseases had been diagnosed since the last

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survey. The item provided multiple answer possibilities such as “cancer”, “diabetes”, or “pneumonia”

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- with the answer format yes/no. Participants who reported that they had been diagnosed with cancer between the last and the present follow-up were categorized as newly diagnosed cancer patients.

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Statistical analysis

Analysis of the CPS was carried out in accordance with the algorithm provided by Degner and Sloan

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[42] and Zhang et al. [15]. We treated the CPS as an ordinal variable with 5 categories describing an ordered list of preferences in decision making ranging from completely active to completely passive. Furthermore, we categorized the 5 CPS-cards into 3 groups of decision making preferences reflecting “active”, “passive” and “collaborative” roles according to the first card that the participants had

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chosen. Frequencies for the 3 categories of the CPS were described together with their confidence

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intervals. Cross tabulations and Chi square tests for the 3 CPS groups “active”, “passive” and “collaborative” stratified by age group, sex, educational status, newly diagnosed cancer, or

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multimorbidity were performed. Analyses of variance were used to compare depressive symptom severity scores as well as CIRS indices between CPS groups. A multinomial logistic regression model was fitted to predict treatment preferences using the active role as reference category. Age, gender, education, multimorbidity, CSD, and self-perceived cognitive

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impairment were included as predictor variables. Statistical analysis was performed using SAS, version 9.4.

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3. Results

Participant characteristics

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Out of the 3124 patients visited at home a total of 3112 participants completed the CPS and were

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included in the study (missing n=12; 0.38%). The demographic characteristics of the participants are

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shown in Table 1.

Tab. 1: demographic characteristics of study participants

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- Insert table 1 about here -

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Control preferences

Table 2 shows the distribution of role preferences according to the various covariates. Close to half of the participants preferred active roles regarding medical decision-making (46%, 95%CI [44.3; 47.9]) while 30.0% [28.4; 31.5] preferred a collaborative role, and 23.9% [22.4; 25.5] a passive role. Women

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reported the preference of collaborative roles significantly more often and less often a preference for

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passive roles compared to men. Also, the younger age group preferred active roles significantly more often – when compared to older age groups. Furthermore, persons with a higher education had a

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significant preference for active roles compared to persons with less education. Participants with clinically significant depressive symptoms (CSD) preferred passive roles significantly more often compared to those without CSD.

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Regarding the association between self-assessed cognitive impairment and treatment preferences we found that older persons who reported to often or always confuse names preferred passive roles significantly more often, and collaborative roles less often, compared to those who reported to never confuse names.

Multimorbid patients preferred passive roles in the decision-making process significantly more often compared to people with none or one chronic disease. Regarding control preferences, there were no significant differences between newly diagnosed cancer patients and participants without cancer. Tab. 2: Tabulation of the 3 CPS groups “active”, “passive” and “collaborative” for various subgroups - Insert table 2 about here -

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Results of the ANOVA, when comparing the CPS-groups regarding their disease burden (according to the CIRS), revealed that persons with a lower disease burden had a significant preference for active or collaborative roles compared to persons with a higher disease burden. As to the various role groups we found that both the morbidity (MI) and somatic morbidity indices (SMI) were lower in groups with

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active or collaborative control preferences. Furthermore, there were no statistically significant

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differences between control preferences and the CIRS organ-specific categories.

Tab. 3: Tabulation of the 3 CPS groups “active”, “passive”, and “collaborative” according to CIRS - Insert table 3 about here -

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The multinomial logistic regression analysis showed that the odds of preferring the passive role were significantly elevated for men and older adults aged ≥ 65 years. In addition, the odds of preferring the

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passive and collaborative role were significantly elevated for multimorbid patients compared to persons with none or one chronic disease and decreased for persons with higher education. All the

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other associations were not significant in the multiple logistic regression model.

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4. Discussion

The present study investigated the control preferences of older adults in a large population-based sample. Results showed that most of the participants preferred an active role in decision making which

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is in line with the results of a household survey in middle-aged adults from Degner et al. [42].

Regarding the predominant preference of an active role the result should be discussed, taking into consideration the age group as well as the study setting. In our study we included older persons, both patients and non-patients. Degner et al. [42] included householders as well as patients in their study and found that most householders preferred an active role. Here, householders aged < 50 years preferred an active role more often than those householders aged 50 and older (69% vs. 53%). In contrast, the majority of the patient group (59%) preferred a passive role. However, age group appeared to be associated with treatment preference: 42% of the patients aged < 50 years as well as 64% of the patients aged ≥ 50 years preferred a passive role in treatment decision making. In their large sample of patients, Arora et al. [5] showed that the odds for preferring an active role significantly decreased with age - patients between the ages of 35 and 44 were 6.9 times more likely to be active than those who were 75 years of age and older. We also found that the older age group in our sample (aged ≥ 65 years) significantly more often preferred a passive decision-making role as compared to the younger age group (aged < 65 years).

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These findings are consistent with the results of previous studies of older patients [16, 17]. We could therefore infer that in general, younger persons are more likely to prefer an active role in treatment decision making. However, there also appears to be an additional difference in role preference between population (more active) and patient samples (more passive); this should also to be taken into

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account. Interestingly, in this regard we found differing, contrasting results compared to previous

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studies. Regarding control preferences in decision making our sample has, for the first time,

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compared newly diagnosed cancer patients with older persons who did not have newly diagnosed cancer; our results indicated no differences between these two groups: both groups preferred more active roles.

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In line with previous studies, we found that having a higher education was more often related to the preference of an active role, and that passive roles were preferred significantly more often by men than

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by women. These results continued to remain significant in the multiple logistic regression model, indicating that in older people gender and education are independently associated with treatment preferences.

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A further result of our study showed that older adults with clinically significant depressive symptoms

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(CSD) more often preferred a passive role compared to those without CSD. This result is new in respect to the evaluation of the association between depressive symptoms and preference-roles in older

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adults. In general, this corroborates findings from previous studies in very different patient samples. Recently, Moise et al. [20], for instance, found in a primary care sample with a mean age of 64 years that elevated depressive symptoms were associated with a preference for clinician- directed decision making. However, in the multiple regression model CSD did not remain significantly associated with

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treatment preferences indicating that its influence is dependent on context variables.

Multimorbid elderly patients had a significant preference for passive roles. In the multiple regression model the odds of preferring an active role were significantly reduced compared to people with none or one chronic disease. These findings are consistent with previous results in younger patient samples [17, 24, 43]. Furthermore, indices of disease burden such as the MI or SMI of the CIRS were significantly higher in elderly persons with passive control preferences. In contrast, there were no significant differences between persons with vs. without newly diagnosed cancer regarding control preferences. Thus, multimorbidity appears to be associated more often with a passive role in the elderly, whereas having newly diagnosed cancer shows no association with role preference. Interestingly, in contrast to our results, most previous studies on cancer patients reported that they preferred more passive roles in decision making

[14, 44]. These differences suggest that the

distribution of control preferences in population-based samples could be different compared to patient samples. The differing results could be based on differences in gender, age, and tumour stages in the

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various patient samples. However, it is also possible that cancer patients receiving inpatient treatment change their role preference from more active to more passive ones. In any case, to prove such a speculation a longitudinal study would be necessary [14, 44].

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One important clinical implication of our study is that we believe it is important for clinicians and

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caregivers to know that the majority of older people prefer an active role in treatment decision making.

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However, we also have to emphasize that the population of older people is a highly heterogeneous group [43]. The odds of preferring the active role in older age appear to be decreasing with age and education status, and to be significantly reduced for male and multimorbid persons. To date, many researchers and clinicians have advocated that physicians carefully assess the

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interactional preferences of patients at the beginning of each meeting [45, 46]. However, Singh et al. [14], in their meta-analysis that included 3491 patients, illustrated that about 40% of patients

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experienced discordance between their preferred versus actual roles in decision making. Other studies showed that a discordance between the preferred and actual roles of the patients was associated with higher satisfaction regarding treatment choice, reduced emotional well-being, and less effective treatment [45, 47]. In addition to the foregoing, we also advocate that assessment of role preference is

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an important part of the shared-decision process. Medical doctors and caregivers should, preferably,

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first ask older people what their control preferences are, and then adapt the decision making process

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accordingly.

Our study has several limitations: Decision-making preferences may be influenced by other factors that were not part of the ESTHER study, e.g.: trust in doctors, patient´s past treatment experiences

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with doctors, type of decision (taking medications vs. surgery), and type, duration, and severity of disease. In contrast to other studies we asked the patients about their general views regarding preferences in decision-making (we did not differentiate between several medical situations or types of doctors). In addition, we examined the associations of preferences for participation in the decisionmaking process, but not the preferences in seeking information. There is, in fact, evidence that people are active in seeking information but passive in participating [10].

However, the strengths of our study are the large population-based study sample and the detailed assessment made by the study doctors during intensive home visits. Results show that older adults living at home prefer more active roles in treatment decision making. In addition, our results indicate that there may be differences between the role preferences of hospitalized patients and patients living at home. Further studies should take this into account.

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The results of this population-based study indicate that physical and mental health in the elderly are associated with the preference role. Multimorbid patients more often prefer passive control compared to people with none or one chronic disease. Similarly, patients with depression more often preferred a passive role compared to those without depression. The results raise the question of influence

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directions in general: Do control preferences change throughout the course of a disease? Could it be

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that people with passive control preferences have a higher probability for a higher disease burden? Or

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could it be that a higher disease burden triggers the preference to not be actively involved in treatment decisions? Further research using a prospective design should investigate these temporal dependencies.

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Results of our study indicate that control preferences in older adults are associated with diseases and disease burden. It could be useful to briefly assess and talk about the control preferences of elderly

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patients before treatment beginning. It is also important to keep in mind that psychosomatic burden appears to be associated with the preference for a passive role in treatment decisions.

Acknowledgements

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This study is part of the consortium “Multimorbidity and frailty at old age: epidemiology, biology,

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psychiatric comorbidity, medical care, and costs” funded by the German Ministry of Research and Education (Grant Number 01ET0718). The funding source had no involvement in the study design,

this study possible.

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collection, analysis, and interpretation of the data. The authors thank all the participants for making

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Competing interest statement

The authors have no competing interests to report.

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[26] Gao L, Weck MN, Raum E, Stegmaier C, Rothenbacher D, Brenner H. Sibship size, Helicobacter pylori infection and chronic atrophic gastritis: a population-based study among 9444 older adults from Germany. Int. J. Epidemiol. 2010;39:129-34. [27] Loew M, Stegmaier C, Ziegler H, Rothenbacher D, Brenner H. [Epidemiological investigations of the chances of preventing, recognizing early and optimally treating chronic diseases in an elderly population (ESTHER study)]. Dtsch. Med. Wochenschr. 2004;129:2643-7. [28] Vogel BA, Bengel J, Helmes AW. Information and decision making: patients' needs and experiences in the course of breast cancer treatment. Patient Educ. Couns. 2008;71:79-85. [29] O'Neal EL, Adams JR, McHugo GJ, Van Citters AD, Drake RE, Bartels SJ. Preferences of older and younger adults with serious mental illness for involvement in decision-making in medical and psychiatric settings. Am. J. Geriatr. Psychiatry. 2008;16:826-33. [30] Henrikson NB, Davison BJ, Berry DL. Measuring decisional control preferences in men newly diagnosed with prostate cancer. J. Psychosoc. Oncol. 2011;29:606-18. [31] Sung VW, Raker CA, Myers DL, Clark MA. Treatment decision-making and information-seeking preferences in women with pelvic floor disorders. Int Urogynecol J. 2010;21:1071-8. [32] Kroenke K, Spitzer RL, Williams JB, Lowe B. The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. Gen. Hosp. Psychiatry. 2010;32:345-59. [33] Loewe B, Unutzer J, Callahan CM, Perkins AJ, Kroenke K. Monitoring depression treatment outcomes with the patient health questionnaire-9. Med. Care. 2004;42:1194-201. [34] Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J. Affect. Disord. 2009;114:163-73. [35] Miller MD, Paradis CF, Houck PR, Mazumdar S, Stack JA, Rifai AH, et al. Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res. 1992;41:237-48. [36] Linn BS, Linn MW, Gurel L. Cumulative illness rating scale. J. Am. Geriatr. Soc. 1968;16:622-6. [37] Huyse FJ, Lyons JS, Stiefel F, Slaets J, de Jonge P, Latour C. Operationalizing the biopsychosocial model: the intermed. Psychosomatics. 2001;42:5-13. [38] Stiefel FC, de Jonge P, Huyse FJ, Guex P, Slaets JP, Lyons JS, et al. "INTERMED": a method to assess health service needs. II. Results on its validity and clinical use. Gen. Hosp. Psychiatry. 1999;21:49-56. [39] de Jonge P, Latour C, Huyse FJ. Interrater reliability of the INTERMED in a heterogeneous somatic population. J. Psychosom. Res. 2002;52:25-7. [40] Wild B, Lechner S, Herzog W, Maatouk I, Wesche D, Raum E, et al. Reliable integrative assessment of health care needs in elderly persons: the INTERMED for the Elderly (IM-E). J. Psychosom. Res. 2011;70:169-78. [41] Wild B, Heider D, Maatouk I, Slaets J, Konig HH, Niehoff D, et al. Significance and costs of complex biopsychosocial health care needs in elderly people: results of a population-based study. Psychosom. Med. 2014;76:497-502. [42] Degner LF, Sloan JA. Decision making during serious illness: what role do patients really want to play? J. Clin. Epidemiol. 1992;45:941-50. [43] Mira JJ, Guilabert M, Perez-Jover V, Lorenzo S. Barriers for an effective communication around clinical decision making: an analysis of the gaps between doctors' and patients' point of view. Health Expect. 2012. [44] Nakashima M, Kuroki S, Shinkoda H, Suetsugu Y, Shimada K, Kaku T. Information-seeking experiences and decision-making roles of Japanese women with breast cancer. Fukuoka Igaku Zasshi. 2012;103:12030. [45] Kiesler DJ, Auerbach SM. Optimal matches of patient preferences for information, decision-making and interpersonal behavior: evidence, models and interventions. Patient Educ. Couns. 2006;61:319-41. [46] Janz NK, Wren PA, Copeland LA, Lowery JC, Goldfarb SL, Wilkins EG. Patient-physician concordance: preferences, perceptions, and factors influencing the breast cancer surgical decision. J. Clin. Oncol. 2004;22:3091-8. [47] Keating NL, Guadagnoli E, Landrum MB, Borbas C, Weeks JC. Treatment decision making in earlystage breast cancer: should surgeons match patients' desired level of involvement? J. Clin. Oncol. 2002;20:1473-9.

ACCEPTED MANUSCRIPT Tables: Table 1: Demographic characteristics of the study participants

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Table 2: Tabulation of the three CPS groups “active”, “passive”, and “collaborative” regarding the various subgroups

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Table 3: Tabulation of the three CPS groups “active”, “passive”, and “collaborative” according to the CIRS

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Table 1: Demographic characteristics of the study participants

Age (years) (Mean±SD) < 65 65-74 > 74

Study participants (n=3112) n % 69,62 ± 6,30 776 24,9 1610 51,7 726 23,3

Gender Female Male

1635 1477

Education (years) ≤ 9 Jahre 10-11 Jahre ≥ 12 Jahre

2028 550 489

52,5 47,5

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Multimorbidity (IM-E) yes no

1620 1487

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CIRS (Mean±SD) MIa SMIb Rskc

65,2 17,7 15,7

52,1 47,8

6,86 ± 5,39 6,43 ± 5,03 1,60 ± 0,97

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Clinically Significant Depression (CSD) d yes no

115 2995

3,7 96,3

MI= morbidity index of the Cumulative Illness Rating Scale (CIRS); b SMI= somatic morbidity index of the CIRS; c RSK = relevant somatic morbidity according to the CIRS; d Participants with a PHQ depression score >10 were diagnosed as having a clinically significant depression (CSD)

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Demographic variable

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active N/ %

collaborative N/%

passive N/%

Age group < 65 (N= 776) 65-74 (N=1610) > 74 (N= 726)

403 / 51.9 728 / 45.2 303 / 41.7

244 / 31.4 489 / 30.4 200 / 27.5

129 / 16.6 393 / 24.4 223 / 30.7

Sex Male (N=1478) Female (N=1634)

673 / 45.6 761 / 46.5

408 / 27.6 525 / 32.1

Educational status ≤ 9 (N=2028) 10-11 (N=550) ≥ 12 (N=489)

868 / 42.8 284 / 51.6 266 / 54.4

628 / 31.0 153 / 27.8 135 / 27.6

532 / 26.2 113 / 20.5 88 / 18.0

Multimorbiditya Yes (N=1620) No (N=1487)

662 / 40.9 768 / 51.6

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Table 2: Tabulation of the three CPS groups “active”, “passive”, and “collaborative” regarding the various subgroups

449 / 27.7 296 / 19.9

Clinically Significant Depression (CSD)b Yes (N=114) No (N=2995)

47 / 40.9 1386 / 46.3

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396 / 26.8 349 / 21.4

χ2 (4) = 42,50 p < .001

χ2 (2) = 15,05 p = .0005

χ2 (4) = 32,46 p < .001

χ2 (2) = 41,60 p < .001

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509 / 31.4 423 / 28.4

test statistics

39 / 33.9 705 / 23.5

χ2 (2) = 6,59 p = .037

83 / 43.9 1328 / 46.2

52 / 27.5 870 / 30.3

54 / 28.6 676 / 23.6

χ2 (2) = 2,55 p = .298

Cognitive impairment (confusing names) No Sometimes Often, always

851 / 47.9 504 / 43.1 37 / 50.0

545 /30.7 350 / 29.9 16 / 21.6

381 / 21.4 316 / 27.0 21 / 28.4

χ2 (4) = 15,64 p = .0035

Cognitive impairment (misplacing things) No Sometimes Often, always

721 / 47.2 633 / 45.1 38 / 41.7

475 / 31.1 412 / 29.4 24 / 26.4

331 / 21.7 357 / 25.5 29 / 31.9

χ2 (4) =9.25 p = .055

Cognitive impairment (forgetting names) No Sometimes Often, always

514 / 45.9 800 / 45.9 79 / 42.0

344 / 30.8 515 / 30.7 55 / 29.3

261 / 23.2 400 / 23.3 54 / 28.7

χ2 (4) =3.17 p = .53

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Newly-diagnosed Cancer Yes (N=189) No (N=2874)

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29 / 25.2 904 / 30.2

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Participants with a PHQ depression score >10 were diagnosed as having a Clinically Significant Depression (CSD); b according to the INTERMED

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Table 3: Tabulation of the three CPS groups “active”, “passive”, and “collaborative” according to the CIRS Active

Collaborative

Passive

Significance

MIa

6,80

6,56

7,35

SMIb

6,39

6,13

6,88

rskc

1,63

1,54

F = 4,23 p = 0.015 F = 4,22 p = 0.015 n.s.

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CIRS

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1,62

MI= morbidity index of the Cumulative Illness Rating Scale (CIRS); b SMI= somatic morbidity index of the CIRS; c RSK = relevant somatic morbidity according to the CIRS

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Figures:

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Figure 1: CPS- cards applied in the ESTHER study

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SELFDETERMINATION

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(A)

I prefer to make the final selection about which treatment I will receive.

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INFORMED DECISION-MAKING BY PATIENT (B)

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I prefer to make the final selection of my treatment after seriously considering my doctor’s opinion.

COOPERATIVE DECISION (C)

I prefer that my doctor and me share responsibility for deciding which treatment is best for me.

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INFORMED DECISION-MAKING BY DOCTOR

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I prefer that my doctor makes the final decision about which treatment will be used, but seriously considers my opinion.

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WAIVER OF PARTICIPATION IN DECISION-MAKING (E)

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I prefer to leave all decisions regarding my treatment to my doctor.

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Highlights

The majority of older adults prefers an active role in treatment decision making

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CSD patients more often prefer a passive role compared to those without CSD

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Multimorbid patients more often prefer a passive role compared to the other participants

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Newly diagnosed cancer patients prefer an active role in medical decision making

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Control preferences in a population- based sample appear to be different compared with inpatient samples

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