Transitions from Hospital to Long Term Care in Norway

1. Transitions from Hospital to Long Term Care in Norway Professor Christina Foss (Not Present) Professor Dag Hofoss PhD candidate Line Kildal Bragstad Institute of Health and Society Department of Nursing Science

2. Part 1 – The patientsTransitions from Hospital to Long Term Care in Norway Christina Foss, RN, PhD, Professor (Not present) Institute of Health and Society Department of Nursing Science

3. Norway • Population close to 5 mill. • Low population density (13 pr square kilometer) • Has a small but aging population 15% 65+ • Scandinavian Welfare model (health care within public sector) • Ranked second highest in health spending per capita in OECD countries • Primary health care under the responsibility of 430 municipalities

4. The Norwegian health care system is organized within two main sectors; Primary health & long term care is the responsibility of municipalities GP, public health nurses, nursing homes and home care – financed through federal block grants, local taxation and out-of-pocket payments (small fees) A municipality with 10 000 inhabitants would have about 10 GP’s, 90 nursing home beds and 150 nurses/nursing aids /home helpers working in home care for elderly and disabled Hospitals and specialist services is the responsibility of the national health authorities Hospitals are run byfour Regional Health Enterprises – since 2002 55 hospitals have been merged into 21 health enterprises Hospital sector is financed through government grants (40% DRG-based and 60% block grants) Private insurance plays a very marginal role in funding (estimated 1-2 %)

5. ”Transitions from Hospital to Long Term Care in Norway” • A 5-year research project financed by the Norwegian Research Council • Cooperation between 3 institutions : (Oslo University, GjøvikUniversity College and a semi-governmental research institute (NOVA) From UiO, Group for elderly care research; Marit Kirkevold, Line K. Bragstad, Tonje Hegli, Grete Lill Helle, Dag Hofoss and Christina Foss

6. Hospital Home with home care Nursing home Sub-study 3: User perspective: • What are the experiences of elderly patients (80+) and their spouses with the transition process from hospital to community care?

7. Design • Individual interviews of patients 2-3 weeks after discharge • Patients are discharged from 14 different hospitals to 53 different municipalities • The municipalities were distributed within the 4 different Regional Health Enterprisesand the municipalities were stratified according to size (small, middle and large) • Questionnaire was developed by the research team

8. What may predict how well elderly patients manage after discharge? • Time for discharge(LeClerk et al 2002, , Penney & Wellard 2007). • Practical issues concerning daily activities(LeClerk et al 2002, Grimmer et al 2004). • Insufficient medication management (Forster et al 2004, Coleman et al 2005, Witherington et al 2008 Grimmer et al 2004). • Sufficient and well adapted community health care(LeClerk 2002, Williams et al 2006). • Family (Popejoy et al. 2009; Bauer et al. 2009). • Loneliness /anxiety/poor psychological health(McLeod et al 2008).

9. The questionnaire Part I - Here and now Part- II The hospital stay • About the discharge • Information/learning • Patients’ role in planning the discharge • Communication • (Spouses role) Part III – Summing up Part IV – Background information

10. Patients included if: Material: Data was collected from October 2007 – May 2009 254 patient interviews Response rate 61.5% • Discharged from somatic hospital wards to home with home care or to nursing homes • Been hospitalized for a minimum of 2 nights • Aged 80 years or older

11. Participant characteristics • The mean age 85,8 years (SD 4,7). • 67,9 % of the respondents are females, • and 70,7 % of the home-dwelling respondents live alone. • 15,1 % of the male and 11,9 % of the female respondents had 12 years of education or more. • Hospitalized for a mean of 12 nights (median of 9 nights)

12. To what degree do elderly patients participate during the discharge process from hospital to community care?

13. Do elderly patients think that participation is important?

14. The impact of different factors on the likelihood of patients agreeing that their opinions were taken into account (1 = agree, fully or partly, 0 = disagree fully or partly) Reference group Education = College/University

15. Did the patients’ preferences for participation match their actual participation? Sumscore of Table 1 questions : actual degree of participation Sumscore ofTable 2 questions: how important participation was • The correlation between the two sum-scores was r = .09, with a significance level of .35, indicating no significant correlation between the patients’ quest for participation and their actual participation.

16. Does participation matter? Correlations between participation and how patients experienced to have managed after discharge from hospital (% (n)). Pearson Chi-Square test p-value of .046

17. Ending comment • Old people can and will contribute with their experiences and views and • Their experiences provide important information that fully compensates for the efforts obtaining them

18. Part 2 – The family caregiversTransitions from Hospital to Long Term Care in Norway Line Kildal Bragstad, OT, MSc Institute of Health and Society Department of Nursing Science

19. Our sample (254 patients) 262 family caregivers • Hospitals • Municipalities • 7 Counties Map: Statens kartverk (cc-by-sa-3.0)

20. Material Structured survey interviews (254 patients) 262 family caregivers Follow-up interviews 19 family caregivers

21. Caregiver questionnaire • Demographic background • Patient’s last hospital stay • Caregiver’s role in discharge planning • Afterdischarge • Summing up • Caregiver as proxy • During and after the hospital stay • Patient’s physical functioning (ADLs) and cognitive functioning

22. Family Caregiver Characteristics • Mean age 60 years (median 58 years). • 63 % of the family caregivers are female • 17,9 % are the patients' spouse • 67,6 % are the patients’ children • 33,7 % have a college or university degree • 62,2 % of the caregivers have a full time or part time job in addition to their caregiving tasks

23. Family Caregiver Characteristics • 31,1 % live with the patient (19,4 % before hospital admission) • 80,3 % of the patients receive help from family or friends • 53,3 % of the caregivers help the patients once or several times per day • 18,9 % report sole responsibility for coordination of care for the home-dwelling patient

24. PhD dissertationin Norway • 3 scientific articles + 1 summary = 1 PhD dissertation • My 1st article: Factors predicting successful discharge

25. Factors predicting successful discharge

26. Factors predictingsuccessful discharge • Adequate formal home health care • 28.4% of the patients found the formal help insufficient • (average 4,65 hours per week in 2012)

27. Factors predictingsuccessful discharge • Someone present at home • 57,7 % informal caregivers • 12,2 % formal caregivers • 15.4% empty house

28. Family caregivers • Caregiver’s role in discharge planning • Participation and shared decision-making • Caregiver’s role after discharge

29. One-Way Information, Dialogue and Joint Decision-Making: Traces of Thompson Dag Hofoss, PhD, ProfessorUniversity of Oslo: Institute of Health and Society (Dept of Nursing Science)University of Tromso: Instit of Community Health (Dept of Health Services Res)

30. One-Way Information, Dialogue and Joint Decision-Making: Traces of Thompson’s Levels-of-Involvement Taxonomy in the transfer back home from acute somatic hospitals of 284 Norwegian octogenarians Thompson A. The meaning of patient involvement and participation in health care consultations: a taxonomy. Social Science & Medicine 2007; 64: 1297-1310

31. Levels of patient involvement 1) Patient-determined: caregiver suggests, patient decides (”patient-as-customer”) 2) Joint decision making: patient has a say in decision making (”patient-as-architect’s-client”) 3) Dialogue: patient and caregiver exchange info, caregiver decides (”patient-focussed paternalism”) 4) Profession-determined: caregiver provides informed decision and informs patient how to comply and cope (”good old days”)

32. Level 1 probably does not exist (not in Norway, not in somatic hospital back home transfers) = What we looked for – by AMOS* – was evidence that our data conform to this Thompson-inspired three-factor patient participation model: JointDM Dialogue One-Way Info *Analysis of MOment Structures (Arbuckle J, Wothke W. Amos (4.0) User’s Guide. Chicago: 1999)

33. We looked at three aspects of the discharge process: 1) Deciding date of discharge 2) Preparation for self-medication after discharge 3) Streamlining of home care/services after discharge each aspect subject to Confirmatory Factor Analysis by AMOS to verify existence of the three basic dimensions - Oneway information from caregiver to patient - Dialogue and discussion between caregiver and patient - Joint decision-making by caregiver and patient Analysis: ”Ongoing, but so far unsuccessful” Many questions may be put into the factor models (see slides at bottom of file), so far no combination has made the models fit the data

34. Goodness-of-fit indicators (Browne MW, Cudeck R. Alternative ways of assessing model fit. In: Bollen KA, Long JS (Editors). Testing structural equation models. Newbury Park, California: Sage 1989 (136-62)) Indicator Should be CMIN1)/DF Close to 1? < 2? < 3? <5? p > ,05 NFI(2) > ,90 (Normed Fit Index) RMSEA < ,08 (Root Mean Squared Error of Approximation Pclose(3) > ,05 Hoelter 05(4) > 200 1), 2), 3), 4): Next slide

35. 1) CMIN = χ2 2) NFI (Normed Fit Index) > ,90 – ”it always is” = NFI not very helpful 3) pclose = the probability that the true RMSEA is below .05, an indication that model is ”a close fit” even if the sample RMSEA was too high. (”The friend in need”: models are invariably wrong, so the real question is ”Are they close?”) 4) Hoelter 05: Because discrepancy increases by sample size, model fit is always more difficult with larger samples. Hoelter’s critical N (Hoelter 05) indicates the largest sample on the basis of which the model would be accepted (should be relatively large, say 200: ”164” means if the GFI-criterion was p < ,05, model would be accepted if tested on a sample of 164, but rejected if sample was 165). Hoelter JW. The analysis of covariance structures: goodness-of-fit indices. Sociological Methods and Research 1983; 11: 325-44

36. Here’s how good/bad it is 1: Date of Discharge CMIN/DF: 3,5 p: < ,001 NFI: ,942 RMSEA: ,125 pclose: < ,001 Hoelter 05: 70

37. Here’s how good/bad it is 2: Medication CMIN/DF: 2,1 p: ,001 NFI: ,885 RMSEA: ,08 pclose: ,05 Hoelter 05: 116

38. Here’s how good/bad it is 3: Home Services CMIN/DF: 4,4 p: < ,001 NFI: ,950 RMSEA: ,15 pclose: <,001 Hoelter 05: 56

39. Why? 1) ”Thompson is wrong” 2) Our settings are incomparably different: Thompson: GP consultations with (e.g.) diabetic patient We: Hospital discharges, very old patients 3)”Norway’s wrong” = is more old-fashioned than we think – JointDM just never happens, factor structure models containing that factor can’t possibly fit 4) ”We are wrong”: Our questions not really designed to test Thompson

40. Thank you for your patient and friendly attention!