Introduction
The prevalence of long-term health conditions, such as chronic heart failure (CHF) and chronic obstructive pulmonary disease (COPD), is on the rise, partly because of the increase in average life expectancy. With similar pathogenic mechanisms, several risk factors in common, and often indistinguishable symptoms, both diagnoses incur substantial morbidity and mortality.1 Healthcare expenditure for CHF in industrialized countries consumes 1–2% of the total healthcare budget, with an increasing tendency.2 The costs for COPD are also reported to have a significant impact on healthcare expenses because of the high prevalence and illness severity.3 Thus, different approaches to caring for these patients have been undertaken to reduce costs. Two examples are telemonitoring and self-management support, which have been shown to lower patient transport costs and reduce the frequency of face-to-face consultations. Moreover, the relevance of these interventions was apparent during the recent COVID-19 pandemic.4
Strengthening self-care and self-management competencies are prominent components of any remote intervention, but studies have demonstrated the difficulties in identifying the effective components of telehealth interventions.5 Structured telephone support has long been known to reduce CHF-related hospitalizations, improve quality of life (QoL), and reduce costs.6 However, in a recent study of telemonitoring vs. usual care for patients with CHF, no cost reduction was observed for the telemonitoring group.7
One suggested mechanism behind the health improvements achieved through self-management programmes for people with long-term conditions is increased self-efficacy, that is, a person's belief in their capability to produce desired results.8 Lorig and Holman propose that the impact of self-management interventions on health depends on the patient's sense of control of the illness. They have found links between perceived self-efficacy and health outcomes, whereby higher levels of self-efficacy indicated better health outcomes.9 Spaling et al.10 found that increased self-efficacy could enable patients to apply self-care strategies in their daily activities. In patients with COPD, self-efficacy has been described as one of the psychological determinants responsible for improved disease self-management.11 Moreover, studies indicate that self-efficacy can be strengthened through person-centred care in patients with CHF or COPD.12–14
Person-centred care aims to give equal weight to patients' subjective understanding of their illness, needs and expectations, and biomedical diagnosis.15 This care approach underlines the importance of co-created care based on the patient's narrative, which elucidates personal resources, expectations, potential barriers, and medical status.15,16 It has been reported that person-centred care improves health outcomes in several diseases, both subjective (such as well-being or quality of care) and objective (such as length of hospital stay).17 In CHF, it has been shown to reduce length of hospital stay, thereby lowering healthcare costs, combined with slight improvements in health outcomes.18 However, a recent review19 of the outcomes of person-centred interventions concludes that only a few studies assessed economic outcomes.
Person-centred care has been shown to increase self-efficacy.12–14 Yet, to our knowledge, no study has investigated whether improved self-efficacy is associated with reduced healthcare costs. If proven, such an association could counteract the predicted increases in healthcare costs for patients with COPD or CHF and result in cost containment. Therefore, this study aims to explore possible associations between self-efficacy and healthcare and drug expenditures (i.e. direct costs) in patients with CHF or COPD in a study investigating the effects of person-centred care delivered by telephone.
Methods
This exploratory analysis uses data from a randomized controlled trial (RCT), in which the effects of remote person-centred care via telephone on patients with CHF or COPD were investigated. Linkage to additional data from regional and national registers was performed.
The results of the RCT have been reported in detail elsewhere.14 Shortly, the results indicate that person-centred support via telephone mitigates worsening self-efficacy without increasing the risk of clinical events in patients with CHF or COPD. Moreover, the cost-effectiveness analysis found that person-centred care improved health-related QoL and resulted in lower average healthcare costs compared with usual care.20 However, variations in self-efficacy and costs and their association have not been investigated.
Study population
The RCT was conducted in a hospital in Western Sweden. Adult patients hospitalized due to relapse or worsening of CHF or COPD were asked to participate during their hospital visit. Patients with expected survival < 12 months, severe disability hindering study participation (severe cognitive impairment or other diseases), ongoing abuse of alcohol or drugs, a severe hearing impairment that prevented the patient from using a telephone or other communication devices, and no current address or if they participated in another study were excluded. Of 610 patients asked to join from January 2015 to November 2016, 243 (40%) accepted and were randomized based on a computer-generated list, stratified for age ≥ 75 and diagnosis (COPD, CHF, or COPD and CHF). An additional 22 patients were excluded from the study because of withdrawal of their consent or wrongful inclusion (e.g. when no longer eligible because the admission diagnosis was changed after inclusion). Thus, 221 (91%) of the 243 randomized patients remained at the 6 month follow-up (118 in the control group and 103 in the intervention group) (Supporting Information, Figure S1).
Person characteristics (baseline data) and health outcomes (generalized self-efficacy, re-hospitalization, and death) were collected during the study. Register data were obtained from the regional patient register (Vega, held by Region Västra Götaland) and the Swedish Prescribed Drug Register (SPDR, held by the National Board of Health and Welfare), covering 1 year after inclusion. Date and causes of death were obtained from the Cause of Death Register, monitored by the National Board of Health and Welfare. Register data were linked using unique personal identity numbers.
Usual care and the person-centred care intervention
All participants received usual care in hospital, including follow-up in primary care and specialized outpatient care when applicable. Patients in the intervention group also received person-centred care through nurse-led telephone support. On average, telephone support corresponded to 81.6 min per patient (median 70.0, range 0–378) for a median of three calls (ranging from 0 to 10; some of the patients died before their first call; thereby, the lowest number of received calls is 0, as these deceased patients are still included in the analysis) during the 6 month follow-up.14 After their discharge from hospital, patients in the intervention group received a first call from one of the four registered nurses (RNs) who performed the intervention. The RNs received continuous training in the ethical principles and practice of person-centred care. During the conversations with the patients, the RNs used the following person-centred communication skills:
- initiating partnership by listening to the patient's narrative and asking open-ended questions responsive to the needs and inner and outer resources and capabilities for health and well-being;
- working the partnership by reflecting and summarizing, thereby engaging the patient as an active partner; and
- safeguarding the partnership by jointly agreed-upon goals that the RN documented in a health plan that was sent to the patient for approval.
The health plan and goals were discussed, evaluated, and revised during the intervention in subsequent conversations. It was also possible for the patients to contact the RNs when needed. During the calls, the RNs could use a mind map consisting of subjects to possibly address, subjects of importance for those living with long-term illness, although the conversations were mostly guided by subject and questions raised by the patients. For further detail of the intervention, please turn to description by Fors et al.14
Outcomes
Participants' self-efficacy was measured using the Generalized Self-Efficacy Scale. This 10-item psychometric scale assesses people's belief in their capability to deal efficiently with unexpected tasks, handle unforeseen situations, and find solutions to problems.21 The scale is well established and has been widely used with diverse populations. For this study, the Swedish version was used.22 Respondents were asked to rate each item on a 4-point scale from 1 (not at all true) to 4 (exactly true). The ratings are summarized to a total score from 10 to 40, with higher scores indicating a higher level of self-efficacy. Questionnaires were completed at enrolment in hospital and through follow-up postal questionnaires at 3 and 6 months after enrolment. Missing responses were handled as suggested by the constructor of the instrument21; that is, if there are up to three missing answers, they can be replaced by the mean of the existing answers; if there are over three missing answers, the response at that time point will be recorded as missing for this participant. Missing baseline medical history was treated as having no previous history of that condition or procedure as the diagnoses or procedures listed were chosen based on being relevant to the study conditions.
A healthcare perspective on costs was adopted, estimating prevalence-based direct costs, including healthcare and prescribed drugs for each participant for up to 1 year after study inclusion. Costs for healthcare visits were calculated using the Diagnosis-Related Groups (DRG) codes and associated national weights for all hospital stays and visits in specialized outpatient care where DRG codes were registered. The weights were then multiplied by the national cost for one DRG. For visits to primary and specialized care lacking a recorded code, costs were assigned based on the national statistics of healthcare use within somatic care from 201623 (Table 1). The cost for the intervention was estimated by multiplying the minutes of conversation each patient received with the mean cost per minute for a specialized nurse in Sweden based on average gross salary24 and corrected for mandated and contracted social insurance contributions paid by the employer. Because of the short time horizon, no discounting was made for costs or health outcomes. The total cost per patient was calculated in bi-monthly intervals from baseline up to 1 year (e.g. 0–2 and 2–4 months) to enable longitudinal analyses. Costs were reported in SEK, which can be divided by 10 for an approximation to EUR [exchange rate in 2016: EUR 1 = SEK 9.4704 (ref: 2022-12-10)].
Table 1 Cost components (SEK) during 1 year from inclusion in the study
Cost components | Control group ( |
Intervention group ( |
Total ( |
|||||||
Primary care | Unit costa | No. | No. | No. | ||||||
Physician | 1522 | 388 | 335 | 723 | ||||||
Physician (phone) | 507 | 17 | 8 | 25 | ||||||
Nurse | 516 | 395 | 265 | 660 | ||||||
Physiotherapist or occupational therapist | 481 | 135 | 127 | 262 | ||||||
Physiotherapist or occupational therapist (phone) | 160 | 37 | 5 | 42 | ||||||
Other staff categories | 609 | 64 | 44 | 108 | ||||||
Specialized care | No. | Mean | 95% CI | No. | Mean | 95% CI | No. | Mean | 95% CI | |
Physician, visits and phone | 764 | 2823 | [2680–2965] | 550 | 2818 | [2612–3025] | 2821 | [2701–2941] | ||
Nurse, visits and phone | 575 | 2272 | [1801–2743] | 327 | 2004 | [1865–2142] | 2174 | [1849–2499] | ||
Physiotherapist or occupational therapist, visits and phone | 163 | 1153 | [1089–1218] | 114 | 1137 | [1086–1189] | 1147 | [1104–1190] | ||
Other, visits and phone | 124 | 1918 | [1627–2209] | 104 | 1840 | [1659–2022] | 228 | 1883 | [1702–2065] | |
Policlinical admissions | 6 | 37 196 | [26 993–47 399] | 5 | 46 195 | [34 008–58 383] | 11 | 41 286 | [33 434–49 139] | |
Hospitalizations, per admission | 403 | 36 798 | [34 879–38 718] | 293 | 39 303 | [37 085–41 521] | 696 | 37 854 | [36 373–39 335] |
Statistical analysis
The association between self-efficacy and cost was investigated by first identifying groups with similar trajectories of self-efficacy and costs and then analysing the effect of self-efficacy on costs using regression analysis.
Trajectories were identified using group-based trajectory modelling.25 The method identifies groups of individuals following the same trajectories over time for a specific factor (i.e. self-efficacy and cost). The number of groups in each trajectory analysis was determined by starting with two groups and then adding groups and assessing model fit using three criteria proposed by Nagin et al.25: (1) average posterior probabilities for all groups > 0.7, (2) odds of correct classification calculated from weighted posterior proportions > 5, and (3) the value, P, that is the proportion in each group based on the assignment of the maximum posterior probability that should be close to the value of total probability.
For the regression analysis, self-efficacy trajectory category membership served as an independent variable. However, multiple alternative definitions of the dependent variable were examined, including using the cost trajectories, absolute costs, and cost categories. The model was selected based on Akaike's and the Bayesian information criteria.26
Descriptive statistics were used to describe baseline characteristics for the two groups of the RCT, costs, and the population of the trajectory groups. Differences between study groups' baseline characteristics were tested using Fisher's exact test and the Mann–Whitney U test.
All statistical analyses were performed using Stata Version 17.0.
Ethical considerations
The investigation conforms to the principles outlined in the Declaration of Helsinki.27 Each patient gave their written informed consent to participate before randomization, and patients were advised of the possibility of withdrawing from the study without providing a reason and without any unfavourable consequences. Ethical approval for the study was obtained from the Regional Ethical Review Board in Gothenburg (Approval Reference Numbers 687-14, T574-17, and T812-17). Trial registry number was ISRCTN55562827 (ISRCTN.com; Care 4 ourselves, C4).
Results
The results show no significant differences in baseline characteristics between the control and intervention groups (Table 2). The mean of the total direct costs at 12 months after inclusion was SEK 184 770, 95% confidence interval (CI) [162 405–207 136], for the control group vs. SEK 158 820, 95% CI [138 459–179 181], for the intervention group (Supporting Information, Table S1).
Table 2 Patient characteristics at baseline
Control group ( |
Intervention group ( |
|
Mean (SD) | Mean (SD) | |
Age, years | 76.9 (8.3) | 78.3 (9.5) |
BMI | 26.7 (6.4) | 26.2 (7.0) |
Generalized Self-Efficacy Score | 28.5 (5.8) | 28.1 (6.5) |
Disposable income in 2014, KSEK | 168 (87) | 200 (255) |
Sex | ||
Female | 69 (58.5) | 51 (49.5) |
Male | 49 (41.5) | 52 (50.5) |
Civil status | ||
Living alone | 63 (53.4) | 62 (60.2) |
Married or partner | 55 (46.6) | 41 (39.8) |
Country of birth | ||
Born domestic | 93 (78.8) | 82 (79.6) |
Born abroad | 25 (21.2) | 21 (20.4) |
Educational levela | ||
Compulsory school | 54 (45.8) | 42 (40.8) |
Upper secondary school | 47 (39.8) | 38 (36.9) |
Higher education | 16 (13.6) | 23 (22.3) |
Diagnosis | ||
COPD | 59 (50.0) | 50 (48.5) |
CHF | 44 (37.3) | 44 (42.7) |
COPD and CHF | 15 (12.7) | 9 (8.7) |
Medical history | ||
MI | 26 (22.0) | 23 (22.3) |
Angina | 23 (19.5) | 15 (14.6) |
Atrial fibrillation | 45 (38.1) | 50 (48.5) |
Hypertension | 61 (51.7) | 61 (59.2) |
CABG | 12 (10.2) | 10 (9.7) |
Stroke | 11 (9.3) | 15 (14.6) |
Diabetes | 27 (22.9) | 29 (28.2) |
CRT | 4 (3.4) | 1 (1.0) |
Pacemaker | 13 (11.0) | 10 (9.7) |
Current smoker | 11 (9.3) | 11 (10.7) |
Previous smoker | 75 (63.5) | 57 (55.3) |
Characteristics of self-efficacy trajectory groups
The trajectory analysis for self-efficacy resulted in five groups: (1) lower/decreasing, (2) lower/increasing, (3) higher/decreasing, (4) higher/stable, and (5) higher/increasing (Figure 1). The largest trajectory group, with higher/stable self-efficacy, included a disproportionately large proportion of participants from the control group. The second largest group, lower/increasing, had an even distribution between the control and intervention participants. In contrast, the third largest group, higher/increasing, included a disproportionately large number of intervention participants. In the higher/decreasing group, also the smallest group, an uneven distribution was observed in the control or intervention group. Therefore, this group differed significantly from the lower/increasing, higher/stable, and higher/increasing groups, as these groups had a more even distribution in the control and intervention groups (the P-values for the pairwise comparisons between groups using the z-test are as follows: P = 0.0104 for the lower/increasing group, P = 0.0201 for the higher/stable group, and P = 0.0036 for the higher/increasing group as compared with the higher/decreasing group). The lower/decreasing self-efficacy group also had an uneven distribution across the intervention and control groups, but this group did not differ significantly from the other groups (the P-values for the pairwise comparisons with the other groups were as follows: lower/increasing, P = 0.1814; higher/decreasing, P = 0.0918; higher/stable, P = 0.2775; and higher/increasing, P = 0.0764).
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Although some of the trajectory groups are small, with limited power to identify differences, the largest group, higher/stable, had a lower proportion of men than women compared with the higher/increasing group. For educational level, there was a smaller proportion of patients who only had completed compulsory school in the higher/increasing group compared with the other groups. In the second largest group, lower/increasing, a larger proportion of patients was diagnosed with CHF compared with the smallest groups (lower/decreasing and higher/decreasing). In addition, the higher/increasing group had a larger proportion of patients diagnosed with COPD than the lower/increasing group (Supporting Information, Table S2).
Characteristics of cost trajectory groups
Three trajectories were identified for costs: (1) lower and increasing slowly, (2) lower and increasing moderately, and (3) higher and increasing more (Figure 2). The first group, with lower and slowly increasing costs, was the largest; in that group, a larger number of patients had CHF compared with Group 2, lower and increasing moderately (Supporting Information, Table S3).
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Regression analysis
The regression model based on fit statistics combined self-efficacy trajectories into two larger categories: increasing and stable self-efficacy or decreasing self-efficacy. The increasing and stable self-efficacy category included the following trajectory groups: (2) lower/increasing, (4) higher/stable, and (5) higher/increasing. The decreasing category comprised the following trajectory groups: (1) lower/decreasing and (3) higher/decreasing.
The regression analysis (Supporting Information, Table S4) revealed an association between accumulated costs and affiliation with the self-efficacy category (coefficient 87 342, 95% CI [34 412–140 273], P = 0.0013). Belonging to the category with self-efficacy that either increased or was stable during the study period was associated with lower costs. In contrast, belonging to the self-efficacy category that had decreasing self-efficacy during the study period was associated with higher costs. Also, age impacted costs, with lower age associated with higher costs.
Participants in the control group who belonged to the decreasing self-efficacy category also had the highest cost of healthcare (Figure 3). The average costs for the increasing or stable self-efficacy category were almost the same for the intervention and control groups.
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Discussion
Five self-efficacy and three cost trajectories were identified, indicating groups of patients with similar development in self-efficacy and costs over time. The regression analysis showed an association between self-efficacy and costs, indicating that increasing or stable self-efficacy was associated with lower costs, while decreasing self-efficacy in the control group was associated with very high costs. In the increasing or stable self-efficacy category, costs were similar between the control and intervention groups.
Although CHF and COPD are progressive diseases that get worse over time,4,28 we found trajectory groups in which self-efficacy remained stable or increased during the study period. In these trajectory groups, the intervention and control group distributions were relatively similar. In the groups in which self-efficacy decreased, there was a larger proportion in the control group. Previous studies have shown that it may be challenging to strengthen self-efficacy through self-management interventions.19 Thus, the reduced deterioration of self-efficacy seen in this study could be considered a successful outcome.
Persons with increasing or stable self-efficacy might be more prone to trust their self-care skills concerning their chronic condition. Recognizing and cultivating patients' capabilities has been suggested as a guiding principle for applying person-centred care in practice.29 One of these capabilities or resources is people's self-efficacy beliefs, that is, their ability to exercise control over themselves and events that affect their lives. Mastery experience (actual performances) is the most influential source of self-efficacy beliefs.30 Central to a person-centred care intervention is listening to the patients' narratives, which provide the basis for creating a jointly developed health plan. The present study's person-centred dialogue helped identify and use personal capabilities as a resource and to set realistic goals.31,32 Thus, it is possible to assume that feedback according to each patient's progress and achievements of such set goals during the follow-up could serve as an example of mastery experience contributing to sustaining or strengthening self-efficacy beliefs and subsequently increasing self-management abilities.
The results of our study suggest that implementing person-centred care at a distance in patients with CHF or COPD might be one meaningful way to affect healthcare costs for these groups. It is outside the scope of this study, but the results might be transferrable to people with other types of long-term conditions as many require self-care and self-management skills and repeated contact with healthcare. The effect of person-centred care through increased or maintained self-efficacy in patients with long-term illnesses should be studied further because it can ensure cost containment for health systems and simultaneously increase the patients' perceived control of their health and well-being.33
The major limitation of this study was that the analysis was not predetermined but exploratory. The results should therefore act as a springboard for further studies. Because the trajectory groups are small, the results should be interpreted with caution.
Many participants declined to participate in the original RCT, which impacts the generalizability of our results. Another limitation is that the self-efficacy follow-up data were limited to 6 months; on the other hand, we have data from multiple time points, and most of our respondents answered the surveys. It would have been desirable to have data available to analyse self-efficacy and costs at 12 months. Still, a regression analysis conducted for costs at 6 months indicates the same direction for costs that we presented in our results.
Concerning cost outcomes, we are almost certain to have captured all participants' healthcare consumption from register data on healthcare use from Vega, as this is a mandatory administrative register created while reimbursing healthcare providers for their work. Thus, the register covers all healthcare provided in units providing tax-funded care. The same applies to the SPDR, created based on all prescribed and dispensed drugs from all Swedish community pharmacies.34 Although using DRG is a crude way of calculating costs that applies better to analyses on the group level, it is an acknowledged system to account for costs and is even sometimes used to reimburse care providers. We have not calculated indirect costs for lost productivity due to sick leave or disability pension because it was not relevant for this population (most of the study participants had reached the age of retirement, and thereby, their income was not affected by their illness). Costs for productivity loss outside of the labour market were not estimated in our study.
Selection criteria for choosing the appropriate number of trajectory groups when performing trajectory modelling are discussed by Serra et al.35 These authors suggest that this selection is arbitrary and that it is up to the researchers to balance statistical criteria vs. possible meaningful interpretations of the trajectories. The stepwise method for selecting the number of groups in our analyses is based on a recommendation in the literature.25 When we consider the trajectories from our analysis, they make sense and help us understand our material.
A multi-trajectory analysis, combing self-efficacy and cost trajectories, could have been an option. However, the groups did not overlap enough for that multi-trajectory model to converge, partly because the trajectory analysis resulted in five groups for self-efficacy and three for costs. Thus, regression analysis was considered the most suitable approach based on actual costs rather than cost trajectories. Regarding which criteria to use when choosing a model for the regression analysis, Akaike's and the Bayesian information criteria prefer different models.26 Thus, the choice will balance between alternative criteria. Our chosen model used accumulated costs (for 12 months) as the dependent variable and self-efficacy category (increasing or stable, or decreasing), study group (control or intervention), participant age, and diagnosis as independent variables and included an interaction term between the self-efficacy categories and study groups. Our model was among the best fitting but not the preferred model by both information criteria. In the end, we used Akaike's information criterion as it led to a model with more variables.
In conclusion, the findings demonstrate that an increased or maintained self-efficacy was associated with lower direct costs in patients with CHF or COPD. Person-centred phone contacts used as an add-on to usual care could result in lower direct costs for those with stable or increasing self-efficacy. For the future challenge that healthcare faces with preserving quality while containing costs, strengthening the patient's self-efficacy through person-centred care provided at a distance could be one viable direction.
Acknowledgements
We gratefully acknowledge the contributions of the study participants and the RNs Eva-Lena Andersson, Pernilla Axhorn, Mahboubeh Goudarzi, and Jonna Norman for performing the intervention. We also thank Region Västra Götaland and the National Board of Health and Welfare for providing the register data.
Conflict of interest
None declared.
Funding
This work was supported by the Swedish Heart & Lung Foundation (Hjärt-Lungfonden; DNr 20180183 to I.E.) and the Swedish Research Council (Vetenskapsrådet; DNr 201701230 to I.E.) and by grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF agreement (ALFGBG-965548 to I.E.). This work was also supported by the University of Gothenburg Centre for Person-Centred Care (GPCC), Sweden. GPCC is funded by the Swedish Government's Grant for Strategic Research Areas (Care Sciences) and the University of Gothenburg, Sweden.
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Abstract
Aims
This study aims to explore possible associations between self‐efficacy and healthcare and drug expenditures (i.e. direct costs) in patients with chronic heart failure (CHF) or chronic obstructive pulmonary disease (COPD) in a study investigating the effects of person‐centred care delivered by telephone.
Methods and results
This exploratory analysis uses data from an open randomized controlled trial conducted between January 2015 and November 2016, providing remote person‐centred care by phone to patients with CHF, COPD, or both. Patients hospitalized due to worsening of CHF or COPD were eligible for the study. Randomization was based on a computer‐generated list, stratified for age ≥ 75 and diagnosis. At a 6 month follow‐up, 118 persons remained in a control group and 103 in an intervention group. The intervention group received person‐centred care by phone as an addition to usual care. Trial data were linked to register data on healthcare and drug use. Group‐based trajectory modelling was applied to identify trajectories for general self‐efficacy and direct costs. Next, associations between self‐efficacy trajectories and costs were assessed using regression analysis. Five trajectories were identified for general self‐efficacy, of which three indicated different levels of increasing or stable self‐efficacy, while two showed a decrease over time in self‐efficacy. Three trajectories were identified for costs, indicating a gradient from lower to higher accumulated costs. Increasing or stable self‐efficacy was associated with lower direct costs (P = 0.0013).
Conclusions
The findings show that an increased or sustained self‐efficacy is associated with lower direct costs in patients with CHF or COPD. Person‐centred phone contacts used as an add‐on to usual care could result in lower direct costs for those with stable or increasing self‐efficacy.
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Details

1 Institute of Health and Care Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, University of Gothenburg Centre for Person‐Centred Care (GPCC), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
2 Department of Public Health, University of Copenhagen, Copenhagen, Denmark
3 Institute of Health and Care Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, University of Gothenburg Centre for Person‐Centred Care (GPCC), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Region Västra Götaland, Research, Education, Development and Innovation, Primary Health Care, Gothenburg, Sweden
4 Institute of Health and Care Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, University of Gothenburg Centre for Person‐Centred Care (GPCC), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Department of Medicine, Geriatrics and Emergency Medicine, Sahlgrenska University Hospital/Östra, Gothenburg, Sweden
5 University of Gothenburg Centre for Person‐Centred Care (GPCC), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, Gothenburg, Sweden