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Rehabilitation and convalescent hospital stay in New South Wales: an analysis of 3,979 women aged 75+

Rehabilitation and convalescent hospital stay in New South Wales: an analysis of 3,979 women aged... Acute hospital stays account for the greatest healthcare costs,1 and older people constitute the highest users of acute hospital care.2 Strategies to reduce older people's use of hospital care are necessary to reduce total healthcare expenditure. Within this context, reducing extended hospital stays for rehabilitation or convalescence could be a key strategy in reducing overall hospital burden. These extended stays – where the patient requires extended care, but this care could be provided outside the acute care setting – may cause ‘bed blocking’, where the patient occupies a bed that could have been used by an acute‐care patient.3,4 The resultant demand on hospital resources has implications for the quality of patient care, delays in admission from emergency departments, and increasing length of elective surgery waiting lists.5–7Individual factors such as gender, age and physical conditions generally determine whether a patient will require convalescence or rehabilitation where the underlying condition is non‐acute, and where the main reason for admission is determined by the person's functional capacity and non‐medical needs. These needs may often be better met outside the acute care system. In British Columbia, the proportion of patients labelled as “alternate level of care” is used as a marker of efficiency (or inefficiency), and of whether people receive “appropriate care in the appropriate place”. There is a view that “a substantial portion of care in acute settings could be potentially provided less expensively in other settings”.8,9 Waiting for such alternative care is a major contributor to delayed discharge. In an analysis undertaken in Ireland, for example, 44% of patients who were considered to have a delayed discharge had been waiting for alternative care for more than one month, and 15% of these people had been waiting more than six months.10 Likewise, in the US, home, hospice, rehabilitation, and long‐term care has been identified as a significant factor in explaining regional variation in extended stay and overall Medicare costs.11 With a predicted additional burden on hospitals as populations age, it is therefore imperative to try to reduce extended stays to ease the strain on hospital services, and to improve quality of care and life for older people. Reducing extended stays has the potential to not only save money but also increase patient satisfaction and other outcomes.This project examined health services data linked to population survey data to understand factors associated with hospital admission for ‘Rehabilitation’ or ‘Convalescence’ as defined by ICD‐10 coding of hospital admissions data. By linking longitudinal survey data with health services data, it is possible to determine individual factors associated with admission to hospital and the outcomes for patients.MethodsParticipantsParticipants were from the 1921–26 cohort of the Australian Longitudinal Study on Women's Health (ALSWH), aged 70–75 when first recruited in 1996, with oversampling in rural and remote areas. A total of 12,432 participants were recruited via the national health insurance database (Medicare) and have completed six three‐yearly surveys between 1996 and 2011, and then six‐monthly surveys. For this analysis, survey data were probabilistically linked to the New South Wales Admitted Patient Database (APDC) by the Centre for Health Record Linkage, and to the National Death Index. Only participants who resided in NSW and had never opted out of data linkage were included (N=3,979).MeasuresThe NSW APDC includes all hospitalisations in public and private hospitals in NSW. Data were obtained for all admissions from 01/06/2000 to 30/09/2013. Reasons for hospitalisation were ascertained using the Primary Diagnosis code for each hospitalisation (using the ICD‐10‐AM),12 and re‐coded as either ‘Rehabilitation’, ‘Convalescence’ or “Other Reasons”. Rehabilitation stays included codes under Z50. Convalescence stays included codes under Z54 (convalescence), Z59 (problems related to housing and economic circumstances), Z60 (problems related to social environment), Z63 (other problems related to primary support group, including family circumstances), Z74 (problems related to care‐provider dependency), Z75 (problems related to medical facilities and other healthcare).Date of death was ascertained via linkage to the National Death Index.13Covariates included demographic factors such as marital status, remoteness as measured by ARIA+,14 country of birth, selected chronic conditions, whether they'd had a recent fall to the ground, whether they had a carer, and whether they needed help with daily tasks. Mental health and physical functioning were measured by the SF‐36 quality of life scale, where mental health was dichotomised with scores below 53 determined to be of poor mental health and scores at or above 53 good mental health as determined in the general population as well as in a Swedish study of older women.15,16 As no similar cut‐off has been determined for physical functioning, quintiles were used to categorised physical functioning scores.17Ethics approvalALSWH has ongoing ethical clearance from both the University of Newcastle and University of Queensland's Human Research Ethics Committees (approval numbers H0760795 and 2004000224). Ethical approval for the linkage of ALSWH survey data to the NSW APDC was received from the NSW Population and Health Services Research Ethics Committee and registered with the University of Newcastle. Written informed consent to participate in ALSWH was obtained from all participants upon commencement in 1996.Statistical analysesOne‐way ANOVA was used to analyse the difference between mean length of stay for admissions for rehabilitation or convalescence stay and admission for another reason. Survival analysis was conducted with Wilcoxon (Gehan) test for pairwise comparisons to examine two‐year survival following a rehabilitation or convalescence stay. Univariate and multivariate logistic regressions were conducted to compare differences between those who had a rehabilitation or convalescence stay with those who had another type of hospitalisation in the observation period.ResultsOf the 3,979 participants who resided in NSW, 3,494 (88%) had a hospitalisation in the observation period (35,656 total hospitalisations), and 1,289 (37%) participants had a rehabilitation or convalescence stay. Figure shows the distribution of hospitalisations and deaths across each calendar year.All hospitalisations and deaths per participant by calendar year.Rehabilitation and convalescence stays had a median length of stay of four days compared with one day for other hospitalisations (Mann‐Whitney U=52709457, z=−20.5, p<.001).Table shows the baseline characteristics for those who had a rehabilitation or convalescence stay in hospital and those who had only other hospitalisations. At the univariate level, those who had a rehabilitation or convalescence stay were more likely to live in a regional or remote area (OR=1.54 [1.34, 1.77]), less likely to have been diagnosed with osteoporosis (OR=0.61 [0.52, 0.71]) and less likely have the lowest self‐rated physical health (OR=0.61 [0.48, 0.81]). At the multivariate level, living in a regional or remote area (AOR=1.56 [1.33, 1.83]) and being less likely to have osteoporosis (AOR= 0.66 [0.55, 0.79]) were also found to be related to having a rehabilitation or convalescence stay.Baseline (Survey 1) characteristics of patients who have had a bed blocking stay compared with patients who have had other hospitalisations.Rehabilitation or convalescence hospitalisation (n=1,289)Other hospitalisation (n=2,205)Unadjusted OR (95% CI)Adjusted OR (95% CI)Marital Status  Married/De facto  Separated / Divorced / Never married  Widowed 677139454 1,223181767 Reference0.72 (0.56, 0.92)0.94 (0.81, 1.09) Reference0.71 (0.54, 0.92)0.89 (0.74, 1.04)Remoteness  Major city  Regional / Remote 610679 8141,391 Reference1.54 (1.34, 1.77)* Reference1.56 (1.33, 1.83)*Provide Care  Yes  No 2241,038 3841,758 1.01 (0.84,1.21)Reference 1.06 (0.86, 1.30)ReferenceNeed Help  Yes  No 971,105 1281,919 0.76 (0.58, 1.00)Reference 0.94 (0.65, 1.35)ReferenceFalls  Fall to the ground / Injury from fall  None of these 791,188 992,065 0.72 (0.53, 0.97)Reference 0.86 (0.60, 1.24)ReferenceDiabetes  No  Yes 1,158112 1,977199 Reference1.04 (0.82, 1.33) Reference0.98 (0.73, 1.32)Stroke  No  Yes 1,19471 2,057115 Reference0.94 (0.69, 1.28) Reference0.97 (0.67, 1.41)Hypertension  No  Yes 663611 1,1231,059 Reference1.02 (0.90, 1.18) Reference1.00 (0.84, 1.16)Osteoporosis  No  Yes 881378 1,723448 Reference0.61 (0.52, 0.71)* Reference0.66 (0.55, 0.79)*Cardiovascular  No  Yes 940307 1,672476 Reference0.87 (0.74, 1.03) Reference0.88 (0.72, 1.07)Respiratory  No  Yes 936331 1,666510 Reference0.87 (0.74, 1.02) Reference0.89 (0.75, 1.08)Cancer  No  Yes 816444 1,419742 Reference0.96 (0.83, 1.11) Reference1.04 (0.88, 1.23)SF‐36 Mental Health Index  <53  >=53 1151,137 2021,942 1.03 (0.81, 1.31)Reference 1.32 (0.98, 1.78)ReferenceSF‐36 Physical Functioning  0.01 – 27.78  27.79 – 50.00  50.01 – 70.00  70.01 – 85.00  85.01 – 100 150210320293228 198371511514485 0.61 (0.48, 0.81)*0.83 (0.66, 1.05)0.75 (0.61, 0.93)0.83 (0.67, 1.02)Reference 0.72 (0.52, 1.01)0.89 (0.68, 1.17)0.77 (0.61, 0.97)0.82 (0.66, 1.04)ReferenceTable shows the characteristics reported in the last survey prior to the index hospitalisation for those who had a rehabilitation or convalescence stay in hospital and for those who only had another type of hospitalisation. At the univariate level, living in a regional or remote area (OR=1.48 [1.29, 1.70]) was positively associated with having a rehabilitation or convalescence stay, and having a fall to the ground (OR=0.75 [0.64, 0.89]), being diagnosed or treated for osteoporosis (OR=0.66 [0.56, 0.77]), and being diagnosed or treated for arthritis (OR=0.63 [0.55, 0.73]) were negatively associated with having a rehabilitation or convalescence stay in hospital. At the multivariate level, only living in a regional or remote area was positively associated with having a rehabilitation or convalescence stay (AOR=1.58 [1.33, 1.87]).Characteristics taken from survey closest to hospitalisation of patients who have had a rehabilitation or convalescence hospitalisation compared with patients who have had other hospitalisations.Rehabilitation or convalescence hospitalisation (n=1289)Other hospitalisation (n=2205)Unadjusted OR (95% CI)Adjusted OR (95% CI)Marital Status  Married/De facto  Separated/Divorced/Never married  Widowed 510118649 9651711,055 Reference0.77 (0.60, 0.99)0.86 (0.74, 0.99) Reference0.84 (0.62, 1.16)0.86 (0.72, 1.03)Area of Residence  Major city  Regional / remote 618667 8471,351 Reference1.48 (1.29, 1.70)* Reference1.58 (1.33, 1.87)*Provide Care  Yes  No 274944 4731,636 1.00 (0.84, 1.18)Reference 0.98 (0.80, 1.20)ReferenceNeed Help  Yes  No 1981,033 2651,850 0.75 (0.61, 0.91)Reference 0.95 (0.72, 1.26)ReferenceFalls  Fall to the ground/Injury from fall  None of these 331898 4571,646 0.75 (0.64, 0.89)*Reference 0.80 (0.66, 0.97)ReferenceDiabetes  No  Yes 1,121139 1,912253 Reference1.07 (0.86, 1.34) Reference1.18 (0.90, 1.56)Stroke  No  Yes 1,16297 2,029135 Reference0.80 (0.61, 1.05) Reference0.90 (0.61, 1.34)Hypertension  No  Yes 653606 1,1101,057 Reference1.03 (0.90, 1.18) Reference1.03 (0.87, 1.23)Osteoporosis  No  Yes 935325 1,764403 Reference0.66 (0.56, 0.77)* Reference0.81 (0.66, 0.99)Cardiovascular  No  Yes 975283 1,693471 Reference0.96 (0.81, 1.13) Reference1.11 (0.88, 1.40)Respiratory  No  Yes 1,048212 1,844323 Reference0.87 (0.72, 1.05) Reference0.90 (0.68, 1.16)Cancer  No  Yes 911348 1,550616 Reference1.04 (0.89, 1.22) Reference1.16 (0.96, 1.39)Arthritis  No  Yes 561584 1,188783 Reference0.63 (0.55, 0.73)* Reference0.77 (0.64, 0.91)SF‐36 Mental Health Index  <53  >=53 1421,072 2001,909 0.79 (0.63, 0.99)Reference 0.88 (0.65, 1.19)ReferenceSF‐36 Physical Functioning  0.01 – 27.78  27.79 – 50.00  50.01 – 70.00  70.01 – 85.00  85.01 – 100 226312267198162 327469456439347 0.68 (0.53, 0.87)0.70 (0.55, 0.89)0.80 (0.63, 1.01)1.04 (0.81, 1.33)Reference 0.80 (0.57, 1.12)0.79 (0.59, 1.05)0.91 (0.69, 1.21)1.07 (0.80, 1.43)ReferenceDiscussionWe found that while the annual prevalence of rehabilitation or convalescence hospitalisations was relatively low (0.9–4.6%), 37% of women in the sample had at least one rehabilitation or convalescence stay over the observation period. In many cases, these patients may have been waiting for care in another setting, as in the study by Carey et al., where 13.5% of 2,762 acute hospital days were deemed unnecessary, and 84% of these were due to problems finding a bed in a nursing facility.18Our analysis found that while no individual health factor was significant in the multivariate model, remote area of patient's residence was a significant factor in determining risk factors for having a rehabilitation or convalescence stay. These findings are consistent with the healthcare context in which subacute and non‐acute care may not be as readily available in rural and remote areas. In these contexts, admission to an acute hospital may be the only option for care, even though it may not be the most appropriate form of care. Of note, our measure of remoteness was based on the participant's residence, not the hospital address. It is possible that women were admitted to a hospital in another area, although evidence suggests that they are likely to have been admitted to their local hospital for non‐acute admissions.19 Indeed, Flabouris et al. found that for NSW patients admitted to ICU, the median distance travelled was 7.7–26.7 km.20 These findings are in keeping with those of Brameld et al., who found in their examination of Western Australian hospital admissions between 1994 and 1999 that those living in highly accessible areas had the lowest hospital admission rates.21 Importantly, in our study, the woman's own residential remoteness was significantly related to rehabilitation or convalescence stay, regardless of whether the hospital was in a rural or urban area. However, other personal factors have been significantly related to these types of admissions in other research. Contrary to our findings, in her review article, Manzano‐Santaella found that older people with increased disability are more likely to have a delayed discharge from acute care.22 Rose et al. also found that frailty was predictive of increased length of stay.23 While we did not directly measure frailty, we would expect that our measure of physical functioning would be a reasonable proxy for frailty, and these findings were not significant.There were some limitations to this study. While most previous research has examined inappropriate delays in hospital stays using a measure of days spent after being deemed medically fit for discharge, we were unable to assess delays in the same way and therefore have examined rehabilitation and convalescence types of hospital stay that are characterised with long length of stay, and where acute hospital settings are not necessarily the most appropriate setting. This may reduce the comparability of our finding with other studies, but at the same time we have been able to develop a novel method for evaluating inappropriate hospitalisations from a large administrative dataset. This method may have also underestimated inappropriate hospital stays by only examining a certain set of diagnostic codes, which would exclude patients hospitalised for other reasons but who experienced a delayed discharge. Nonetheless, this analysis has identified a set of diagnostic codes that arguably could be more appropriately managed in a non‐acute setting, and the risk factors for having a rehabilitation or convalescence stay.Our findings suggest that remoteness is the determining factor for experiencing a rehabilitation or convalescence stay in an acute hospitalisation, with older women from rural and remote areas experiencing more of these hospitalisations. These findings are in keeping with another analysis of ALSWH data that found that women living in rural/remote areas were 13% less likely to use residential aged care.24 From a policy perspective, it would appear that an increase in non‐acute care options is required for non‐urban areas. Community‐based rehabilitation services have been identified as one effective strategy to reduce bed blocking.25 Gaughan et al. cautioned that increased nursing home beds only makes a modest improvement in bed blocking; however, it may be possible that additional rehabilitation services and home care services would also make an improvement in rates of inappropriate acute hospital stays.26 Indeed, the lack of significant findings for health‐related conditions suggest that organisational rather than individual factors may be determining factors in inappropriate extended hospital stays in older people.ReferencesAustralian Institute of Health and Welfare. Health Expenditure Australia 2009–10. Health and Welfare Expenditure. Series No.: 46. Canberra (AUST): AIHW; 2011.Australian Institute of Health and Welfare. Australia's Health 2012. Australia's Health. Series No.: 13. Canberra (AUST): AIHW; 2012.Dizdar O, Karadag O, Kalyoncu U, Kurt M, Ulger Z, Sardan YC, et al. Appropriate utilization of hospital beds in internal medicine: Evaluation in a tertiary care hospital. J Eval Clin Pract. 2007;13(3):408–11.Angelillo IF, Ricciardi G, Nante N, Boccia A, Collaborative G. Appropriateness of hospital utilisation in Italy. Public Health. 2000;114(1):9–14.McMillan A. Bed‐blockers, breachers and boarders. Br J Nurs. 2015;24(8):430.Landeiro F, Leal J, Gray AM. The impact of social isolation on delayed hospital discharges of older hip fracture patients and associated costs. Osteoporos Int. 2016;27(2):737–45.Rashwan W, Abo‐Hamad W, Arisha A. A system dynamics view of the acute bed blockage problem in the Irish healthcare system. Eur J Oper Res. 2015;247(1):276–93.Penney C, Henry E. Improving performance management for delivering appropriate care for patients no longer needing acute hospital care. J Health Serv Res Policy. 2008; 13 Suppl 1: 30–4.Sutherland JM, Crump RT. Alternative level of care: Canada's hospital beds, the evidence and options. Healthc Policy. 2013;9(1):26–34.Health and Safety Executive. Delayed Discharge National Report. Dublin (IRL): Department of Health; 2012.Institute of Medicine. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington (DC): The National Academies Press; 2013.National Centre for Classification in Health. International Statistical Classification of Diseases and Related Health Problems;, 10th Revision, Australian Modification (ICD‐10‐AM). 5th ed. Sydney (AUST): University of Sydney NCCH; 2006.Powers J, Ball J, Adamson L, Dobson A. Effectiveness of the National Death Index for establishing the vital status of older women in the Australian Longitudinal Study on Women's Health. Aust N Z J Public Health. 2000;24(5):526–8.Glover J, Tennant S. Remote Areas Statistical Geography in Australia: Notes on the Accessibility/Remoteness Index for Australia (ARIA+ Version). Working Paper Series No.: 9. Adelaide (AUST): Public Health Information Development Unit; 2003.Ware JE. SF‐36 Health Survey. In: Maruish ME, editor. The Use of Psychological Testing for Treatment Planning and Outcomes Assessment. 2nd ed. Mahwah (NJ): Lawrence Erlbaum Associates Publishers; 1999. p. 1227–46.Silveira E, Taft C, Sundh V, Waern M, Palsson S, Steen B. Performance of the SF‐36 health survey in screening for depressive and anxiety disorders in an elderly female Swedish population. Qual Life Res. 2005;14(5):1263–74.Peeters G, Dobson AJ, Deeg DJH, Brown WJ. A life‐course perspective on physical functioning in women. Bull World Health Organ. 2013;91(9):661–70.Carey MR, Sheth H, Braithwaite RS. A prospective study of reasons for prolonged hospitalizations on a general medicine teaching service. J Gen Intern Med. 2005;20(2):108–15.Australian Institute of Health and Welfare. Admitted Patient Palliative Care in Austalia 1999–00. Canberra (AUST): AIHW; 2003.Flabouris A, Hart GK, Nicholls A. Patients admitted to Australian intensive care units: Impact of remoteness and distance travelled on patient outcome. Crit Care Resusc. 2012;14(4):256–67.Brameld KJ, Holman CDAJ. The effect of locational disadvantage on hospital utilisation and outcomes in Western Australia. Health Place. 2006;12(4):490–502.Manzano‐Santaella A. From bed‐blocking to delayed discharges: Precursors and interpretations of a contested concept. Health Serv Manage Res. 2010;23(3):121–7.Rose M, Pan H, Levinson MR, Staples M. Can frailty predict complicated care needs and length of stay? Intern Med. 2014;44(8):800–5.Byles JE, Forder P, Vo K, Curryer C, Loxton D. Cumulative incidence of admission to permanent residential aged care for Australian women – A competing risk analysis. Aust N Z J Public Health. 2017 Forthcoming.Philp I, Mills KA, Thanvi B, Ghosh K, Long JF. Reducing hospital bed use by frail older people: Results from a systematic review of the literature. Int J Integr Care. 2013;13: e048.Gaughan J, Gravelle H, Siciliani L. Testing the bed‐blocking hypothesis: Does nursing and care home supply reduce delayed hospital discharges? Health Econ. 2015;24(S1):32–44. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Australian and New Zealand Journal of Public Health Wiley

Rehabilitation and convalescent hospital stay in New South Wales: an analysis of 3,979 women aged 75+

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Publisher
Wiley
Copyright
© 2018 Public Health Association of Australia
ISSN
1326-0200
eISSN
1753-6405
DOI
10.1111/1753-6405.12731
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29165860
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Abstract

Acute hospital stays account for the greatest healthcare costs,1 and older people constitute the highest users of acute hospital care.2 Strategies to reduce older people's use of hospital care are necessary to reduce total healthcare expenditure. Within this context, reducing extended hospital stays for rehabilitation or convalescence could be a key strategy in reducing overall hospital burden. These extended stays – where the patient requires extended care, but this care could be provided outside the acute care setting – may cause ‘bed blocking’, where the patient occupies a bed that could have been used by an acute‐care patient.3,4 The resultant demand on hospital resources has implications for the quality of patient care, delays in admission from emergency departments, and increasing length of elective surgery waiting lists.5–7Individual factors such as gender, age and physical conditions generally determine whether a patient will require convalescence or rehabilitation where the underlying condition is non‐acute, and where the main reason for admission is determined by the person's functional capacity and non‐medical needs. These needs may often be better met outside the acute care system. In British Columbia, the proportion of patients labelled as “alternate level of care” is used as a marker of efficiency (or inefficiency), and of whether people receive “appropriate care in the appropriate place”. There is a view that “a substantial portion of care in acute settings could be potentially provided less expensively in other settings”.8,9 Waiting for such alternative care is a major contributor to delayed discharge. In an analysis undertaken in Ireland, for example, 44% of patients who were considered to have a delayed discharge had been waiting for alternative care for more than one month, and 15% of these people had been waiting more than six months.10 Likewise, in the US, home, hospice, rehabilitation, and long‐term care has been identified as a significant factor in explaining regional variation in extended stay and overall Medicare costs.11 With a predicted additional burden on hospitals as populations age, it is therefore imperative to try to reduce extended stays to ease the strain on hospital services, and to improve quality of care and life for older people. Reducing extended stays has the potential to not only save money but also increase patient satisfaction and other outcomes.This project examined health services data linked to population survey data to understand factors associated with hospital admission for ‘Rehabilitation’ or ‘Convalescence’ as defined by ICD‐10 coding of hospital admissions data. By linking longitudinal survey data with health services data, it is possible to determine individual factors associated with admission to hospital and the outcomes for patients.MethodsParticipantsParticipants were from the 1921–26 cohort of the Australian Longitudinal Study on Women's Health (ALSWH), aged 70–75 when first recruited in 1996, with oversampling in rural and remote areas. A total of 12,432 participants were recruited via the national health insurance database (Medicare) and have completed six three‐yearly surveys between 1996 and 2011, and then six‐monthly surveys. For this analysis, survey data were probabilistically linked to the New South Wales Admitted Patient Database (APDC) by the Centre for Health Record Linkage, and to the National Death Index. Only participants who resided in NSW and had never opted out of data linkage were included (N=3,979).MeasuresThe NSW APDC includes all hospitalisations in public and private hospitals in NSW. Data were obtained for all admissions from 01/06/2000 to 30/09/2013. Reasons for hospitalisation were ascertained using the Primary Diagnosis code for each hospitalisation (using the ICD‐10‐AM),12 and re‐coded as either ‘Rehabilitation’, ‘Convalescence’ or “Other Reasons”. Rehabilitation stays included codes under Z50. Convalescence stays included codes under Z54 (convalescence), Z59 (problems related to housing and economic circumstances), Z60 (problems related to social environment), Z63 (other problems related to primary support group, including family circumstances), Z74 (problems related to care‐provider dependency), Z75 (problems related to medical facilities and other healthcare).Date of death was ascertained via linkage to the National Death Index.13Covariates included demographic factors such as marital status, remoteness as measured by ARIA+,14 country of birth, selected chronic conditions, whether they'd had a recent fall to the ground, whether they had a carer, and whether they needed help with daily tasks. Mental health and physical functioning were measured by the SF‐36 quality of life scale, where mental health was dichotomised with scores below 53 determined to be of poor mental health and scores at or above 53 good mental health as determined in the general population as well as in a Swedish study of older women.15,16 As no similar cut‐off has been determined for physical functioning, quintiles were used to categorised physical functioning scores.17Ethics approvalALSWH has ongoing ethical clearance from both the University of Newcastle and University of Queensland's Human Research Ethics Committees (approval numbers H0760795 and 2004000224). Ethical approval for the linkage of ALSWH survey data to the NSW APDC was received from the NSW Population and Health Services Research Ethics Committee and registered with the University of Newcastle. Written informed consent to participate in ALSWH was obtained from all participants upon commencement in 1996.Statistical analysesOne‐way ANOVA was used to analyse the difference between mean length of stay for admissions for rehabilitation or convalescence stay and admission for another reason. Survival analysis was conducted with Wilcoxon (Gehan) test for pairwise comparisons to examine two‐year survival following a rehabilitation or convalescence stay. Univariate and multivariate logistic regressions were conducted to compare differences between those who had a rehabilitation or convalescence stay with those who had another type of hospitalisation in the observation period.ResultsOf the 3,979 participants who resided in NSW, 3,494 (88%) had a hospitalisation in the observation period (35,656 total hospitalisations), and 1,289 (37%) participants had a rehabilitation or convalescence stay. Figure shows the distribution of hospitalisations and deaths across each calendar year.All hospitalisations and deaths per participant by calendar year.Rehabilitation and convalescence stays had a median length of stay of four days compared with one day for other hospitalisations (Mann‐Whitney U=52709457, z=−20.5, p<.001).Table shows the baseline characteristics for those who had a rehabilitation or convalescence stay in hospital and those who had only other hospitalisations. At the univariate level, those who had a rehabilitation or convalescence stay were more likely to live in a regional or remote area (OR=1.54 [1.34, 1.77]), less likely to have been diagnosed with osteoporosis (OR=0.61 [0.52, 0.71]) and less likely have the lowest self‐rated physical health (OR=0.61 [0.48, 0.81]). At the multivariate level, living in a regional or remote area (AOR=1.56 [1.33, 1.83]) and being less likely to have osteoporosis (AOR= 0.66 [0.55, 0.79]) were also found to be related to having a rehabilitation or convalescence stay.Baseline (Survey 1) characteristics of patients who have had a bed blocking stay compared with patients who have had other hospitalisations.Rehabilitation or convalescence hospitalisation (n=1,289)Other hospitalisation (n=2,205)Unadjusted OR (95% CI)Adjusted OR (95% CI)Marital Status  Married/De facto  Separated / Divorced / Never married  Widowed 677139454 1,223181767 Reference0.72 (0.56, 0.92)0.94 (0.81, 1.09) Reference0.71 (0.54, 0.92)0.89 (0.74, 1.04)Remoteness  Major city  Regional / Remote 610679 8141,391 Reference1.54 (1.34, 1.77)* Reference1.56 (1.33, 1.83)*Provide Care  Yes  No 2241,038 3841,758 1.01 (0.84,1.21)Reference 1.06 (0.86, 1.30)ReferenceNeed Help  Yes  No 971,105 1281,919 0.76 (0.58, 1.00)Reference 0.94 (0.65, 1.35)ReferenceFalls  Fall to the ground / Injury from fall  None of these 791,188 992,065 0.72 (0.53, 0.97)Reference 0.86 (0.60, 1.24)ReferenceDiabetes  No  Yes 1,158112 1,977199 Reference1.04 (0.82, 1.33) Reference0.98 (0.73, 1.32)Stroke  No  Yes 1,19471 2,057115 Reference0.94 (0.69, 1.28) Reference0.97 (0.67, 1.41)Hypertension  No  Yes 663611 1,1231,059 Reference1.02 (0.90, 1.18) Reference1.00 (0.84, 1.16)Osteoporosis  No  Yes 881378 1,723448 Reference0.61 (0.52, 0.71)* Reference0.66 (0.55, 0.79)*Cardiovascular  No  Yes 940307 1,672476 Reference0.87 (0.74, 1.03) Reference0.88 (0.72, 1.07)Respiratory  No  Yes 936331 1,666510 Reference0.87 (0.74, 1.02) Reference0.89 (0.75, 1.08)Cancer  No  Yes 816444 1,419742 Reference0.96 (0.83, 1.11) Reference1.04 (0.88, 1.23)SF‐36 Mental Health Index  <53  >=53 1151,137 2021,942 1.03 (0.81, 1.31)Reference 1.32 (0.98, 1.78)ReferenceSF‐36 Physical Functioning  0.01 – 27.78  27.79 – 50.00  50.01 – 70.00  70.01 – 85.00  85.01 – 100 150210320293228 198371511514485 0.61 (0.48, 0.81)*0.83 (0.66, 1.05)0.75 (0.61, 0.93)0.83 (0.67, 1.02)Reference 0.72 (0.52, 1.01)0.89 (0.68, 1.17)0.77 (0.61, 0.97)0.82 (0.66, 1.04)ReferenceTable shows the characteristics reported in the last survey prior to the index hospitalisation for those who had a rehabilitation or convalescence stay in hospital and for those who only had another type of hospitalisation. At the univariate level, living in a regional or remote area (OR=1.48 [1.29, 1.70]) was positively associated with having a rehabilitation or convalescence stay, and having a fall to the ground (OR=0.75 [0.64, 0.89]), being diagnosed or treated for osteoporosis (OR=0.66 [0.56, 0.77]), and being diagnosed or treated for arthritis (OR=0.63 [0.55, 0.73]) were negatively associated with having a rehabilitation or convalescence stay in hospital. At the multivariate level, only living in a regional or remote area was positively associated with having a rehabilitation or convalescence stay (AOR=1.58 [1.33, 1.87]).Characteristics taken from survey closest to hospitalisation of patients who have had a rehabilitation or convalescence hospitalisation compared with patients who have had other hospitalisations.Rehabilitation or convalescence hospitalisation (n=1289)Other hospitalisation (n=2205)Unadjusted OR (95% CI)Adjusted OR (95% CI)Marital Status  Married/De facto  Separated/Divorced/Never married  Widowed 510118649 9651711,055 Reference0.77 (0.60, 0.99)0.86 (0.74, 0.99) Reference0.84 (0.62, 1.16)0.86 (0.72, 1.03)Area of Residence  Major city  Regional / remote 618667 8471,351 Reference1.48 (1.29, 1.70)* Reference1.58 (1.33, 1.87)*Provide Care  Yes  No 274944 4731,636 1.00 (0.84, 1.18)Reference 0.98 (0.80, 1.20)ReferenceNeed Help  Yes  No 1981,033 2651,850 0.75 (0.61, 0.91)Reference 0.95 (0.72, 1.26)ReferenceFalls  Fall to the ground/Injury from fall  None of these 331898 4571,646 0.75 (0.64, 0.89)*Reference 0.80 (0.66, 0.97)ReferenceDiabetes  No  Yes 1,121139 1,912253 Reference1.07 (0.86, 1.34) Reference1.18 (0.90, 1.56)Stroke  No  Yes 1,16297 2,029135 Reference0.80 (0.61, 1.05) Reference0.90 (0.61, 1.34)Hypertension  No  Yes 653606 1,1101,057 Reference1.03 (0.90, 1.18) Reference1.03 (0.87, 1.23)Osteoporosis  No  Yes 935325 1,764403 Reference0.66 (0.56, 0.77)* Reference0.81 (0.66, 0.99)Cardiovascular  No  Yes 975283 1,693471 Reference0.96 (0.81, 1.13) Reference1.11 (0.88, 1.40)Respiratory  No  Yes 1,048212 1,844323 Reference0.87 (0.72, 1.05) Reference0.90 (0.68, 1.16)Cancer  No  Yes 911348 1,550616 Reference1.04 (0.89, 1.22) Reference1.16 (0.96, 1.39)Arthritis  No  Yes 561584 1,188783 Reference0.63 (0.55, 0.73)* Reference0.77 (0.64, 0.91)SF‐36 Mental Health Index  <53  >=53 1421,072 2001,909 0.79 (0.63, 0.99)Reference 0.88 (0.65, 1.19)ReferenceSF‐36 Physical Functioning  0.01 – 27.78  27.79 – 50.00  50.01 – 70.00  70.01 – 85.00  85.01 – 100 226312267198162 327469456439347 0.68 (0.53, 0.87)0.70 (0.55, 0.89)0.80 (0.63, 1.01)1.04 (0.81, 1.33)Reference 0.80 (0.57, 1.12)0.79 (0.59, 1.05)0.91 (0.69, 1.21)1.07 (0.80, 1.43)ReferenceDiscussionWe found that while the annual prevalence of rehabilitation or convalescence hospitalisations was relatively low (0.9–4.6%), 37% of women in the sample had at least one rehabilitation or convalescence stay over the observation period. In many cases, these patients may have been waiting for care in another setting, as in the study by Carey et al., where 13.5% of 2,762 acute hospital days were deemed unnecessary, and 84% of these were due to problems finding a bed in a nursing facility.18Our analysis found that while no individual health factor was significant in the multivariate model, remote area of patient's residence was a significant factor in determining risk factors for having a rehabilitation or convalescence stay. These findings are consistent with the healthcare context in which subacute and non‐acute care may not be as readily available in rural and remote areas. In these contexts, admission to an acute hospital may be the only option for care, even though it may not be the most appropriate form of care. Of note, our measure of remoteness was based on the participant's residence, not the hospital address. It is possible that women were admitted to a hospital in another area, although evidence suggests that they are likely to have been admitted to their local hospital for non‐acute admissions.19 Indeed, Flabouris et al. found that for NSW patients admitted to ICU, the median distance travelled was 7.7–26.7 km.20 These findings are in keeping with those of Brameld et al., who found in their examination of Western Australian hospital admissions between 1994 and 1999 that those living in highly accessible areas had the lowest hospital admission rates.21 Importantly, in our study, the woman's own residential remoteness was significantly related to rehabilitation or convalescence stay, regardless of whether the hospital was in a rural or urban area. However, other personal factors have been significantly related to these types of admissions in other research. Contrary to our findings, in her review article, Manzano‐Santaella found that older people with increased disability are more likely to have a delayed discharge from acute care.22 Rose et al. also found that frailty was predictive of increased length of stay.23 While we did not directly measure frailty, we would expect that our measure of physical functioning would be a reasonable proxy for frailty, and these findings were not significant.There were some limitations to this study. While most previous research has examined inappropriate delays in hospital stays using a measure of days spent after being deemed medically fit for discharge, we were unable to assess delays in the same way and therefore have examined rehabilitation and convalescence types of hospital stay that are characterised with long length of stay, and where acute hospital settings are not necessarily the most appropriate setting. This may reduce the comparability of our finding with other studies, but at the same time we have been able to develop a novel method for evaluating inappropriate hospitalisations from a large administrative dataset. This method may have also underestimated inappropriate hospital stays by only examining a certain set of diagnostic codes, which would exclude patients hospitalised for other reasons but who experienced a delayed discharge. Nonetheless, this analysis has identified a set of diagnostic codes that arguably could be more appropriately managed in a non‐acute setting, and the risk factors for having a rehabilitation or convalescence stay.Our findings suggest that remoteness is the determining factor for experiencing a rehabilitation or convalescence stay in an acute hospitalisation, with older women from rural and remote areas experiencing more of these hospitalisations. These findings are in keeping with another analysis of ALSWH data that found that women living in rural/remote areas were 13% less likely to use residential aged care.24 From a policy perspective, it would appear that an increase in non‐acute care options is required for non‐urban areas. Community‐based rehabilitation services have been identified as one effective strategy to reduce bed blocking.25 Gaughan et al. cautioned that increased nursing home beds only makes a modest improvement in bed blocking; however, it may be possible that additional rehabilitation services and home care services would also make an improvement in rates of inappropriate acute hospital stays.26 Indeed, the lack of significant findings for health‐related conditions suggest that organisational rather than individual factors may be determining factors in inappropriate extended hospital stays in older people.ReferencesAustralian Institute of Health and Welfare. Health Expenditure Australia 2009–10. 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J Gen Intern Med. 2005;20(2):108–15.Australian Institute of Health and Welfare. Admitted Patient Palliative Care in Austalia 1999–00. Canberra (AUST): AIHW; 2003.Flabouris A, Hart GK, Nicholls A. Patients admitted to Australian intensive care units: Impact of remoteness and distance travelled on patient outcome. Crit Care Resusc. 2012;14(4):256–67.Brameld KJ, Holman CDAJ. The effect of locational disadvantage on hospital utilisation and outcomes in Western Australia. Health Place. 2006;12(4):490–502.Manzano‐Santaella A. From bed‐blocking to delayed discharges: Precursors and interpretations of a contested concept. Health Serv Manage Res. 2010;23(3):121–7.Rose M, Pan H, Levinson MR, Staples M. Can frailty predict complicated care needs and length of stay? Intern Med. 2014;44(8):800–5.Byles JE, Forder P, Vo K, Curryer C, Loxton D. Cumulative incidence of admission to permanent residential aged care for Australian women – A competing risk analysis. 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Journal

Australian and New Zealand Journal of Public Health – Wiley

Published: Jan 1, 2018

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