Combined effects of four major geriatric syndromes on adverse outcomes based on Korean National Health Insurance claims data

Kim, Sun Young; Park, Jong-Heon; Won, Chang Won

doi:10.1111/ggi.13513

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…In this current study also, men, those from accommodation such as retirement villages or short-term accommodation, those with geriatric syndromes such as falls or delirium or illnesses like cancer and people with difficulties with personal activities of daily living, communication, transportation and community participation were less likely to wait six months or more when compared to those waiting less than 30 days. Health conditions such as delirium, falls and cancer increase an older person’s risk of mortality or residential age care placement ( 20 ). Older people with higher activity of daily living needs may overwhelm caregiver capacity sooner thus resulting in an earlier reliance on HCPs or transition residential aged care services ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

Prolonged Wait Time Prior to Entry to Home Care Packages Increases the Risk of Mortality and Transition to Permanent Residential Aged Care Services: Findings from the Registry of Older South Australians (ROSA)

Visvanathan

Amare

Wesselingh

et al. 2019

The Journal of nutrition, health and aging

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Background Older Australians prefer to live in their own homes for longer and reforms have attempted to increase the volume of home care packages (HCPs) accordingly but there remains a queue with the longer-term consequences unclear. Objectives This study aims to characterise older Australians according to their wait times for a home care package (HCP), evaluate the association between wait time and mortality and evaluate the association between wait time and transition to permanent residential aged care services after HCP. Design A retrospective cohort study using data from the National Historical cohort (2003-2014) of the Registry of Older South Australians (ROSA) was conducted. Setting Home based aged care services, national cohort. Methods Wait time was estimated from approval date to date of receiving a HCP. Descriptive, survival estimates (95% confidence intervals (CIs)), and multivariable survival analyses (Cox-regression) were conducted to evaluate the risk of mortality and transition to permanent residential aged care services by quartiles of wait time for HCP. Results The cohort was followed for 4.0 years (interquartile range IQR (1.8-7.2)) and 38% were alive at the end of the study period with a median wait time for HCP of 62 (21-187) days. From 178,924 older people who received a HCP during the study period (2003-2013), 33.2% people received HCP within 30 days, 74.3% within 6 months and 25.7% after 6 months. The effect of wait time on risk of mortality was time-dependent, with longer wait times associated with higher mortality in the longer term. Compared to people who waited ≤30 days for a HCP, individuals who waited more than 6 months had an almost 20% excess risk of death (adjusted hazard ratio (aHR), 95%CI = (1.18, 1.16-1.21)) 2 years after entry into a HCP. Those who waited more than 6 months also had a 10% (1.10, 1.06-1.13) higher risk of transition to permanent residential aged care services after two years. Conclusion Prolonged wait times for HCP is associated with a higher risk of long-term mortality as well as transition to permanent residential aged care. It remains to be seen if a shortening of this wait time translates into better health outcomes. Electronic Supplementary Material Supplementary material is available for this article at 10.1007/s12603-018-1145-y and is accessible for authorized users.

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Section: Discussionmentioning

confidence: 99%

Prolonged Wait Time Prior to Entry to Home Care Packages Increases the Risk of Mortality and Transition to Permanent Residential Aged Care Services: Findings from the Registry of Older South Australians (ROSA)

Visvanathan

Amare

Wesselingh

et al. 2019

The Journal of nutrition, health and aging

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“…These data can be readily integrated into clinical computer systems, automatically populated, and made available without requiring additional resource input [ 44 ]. Further, although we found some studies that used large sample sizes comprised of more than 200,000 participants [ 18 , 20 , 28 , 31 , 43 , 44 , 47 ], this did not necessarily mean that the predictors or outcome variables were distinctive. In addition, although many studies used administrative claims data, the use of billing information or the construction of cost predictors was rarely observed.…”

Section: Discussionmentioning

confidence: 88%

Identifying Predictors of Nursing Home Admission by Using Electronic Health Records and Administrative Data: Scoping Review

Han¹,

Kharrazi²,

Shi³

2023

JMIR Aging

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Background Among older adults, nursing home admissions (NHAs) are considered a significant adverse outcome and have been extensively studied. Although the volume and significance of electronic data sources are expanding, it is unclear what predictors of NHA have been systematically identified in the literature via electronic health records (EHRs) and administrative data. Objective This study synthesizes findings of recent literature on identifying predictors of NHA that are collected from administrative data or EHRs. Methods The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines were used for study selection. The PubMed and CINAHL databases were used to retrieve the studies. Articles published between January 1, 2012, and March 31, 2023, were included. Results A total of 34 papers were selected for final inclusion in this review. In addition to NHA, all-cause mortality, hospitalization, and rehospitalization were frequently used as outcome measures. The most frequently used models for predicting NHAs were Cox proportional hazards models (studies: n=12, 35%), logistic regression models (studies: n=9, 26%), and a combination of both (studies: n=6, 18%). Several predictors were used in the NHA prediction models, which were further categorized into sociodemographic, caregiver support, health status, health use, and social service use factors. Only 5 (15%) studies used a validated frailty measure in their NHA prediction models. Conclusions NHA prediction tools based on EHRs or administrative data may assist clinicians, patients, and policy makers in making informed decisions and allocating public health resources. More research is needed to assess the value of various predictors and data sources in predicting NHAs and validating NHA prediction models externally.

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“… 8 Diagnosis of delirium increases the probability of first-time hospitalization in the geriatric era. 9 It can also increase the probability of subsequent re-hospitalization 10 and prolongs the time to discharge. 11 Several variables such as anemia, electrolyte imbalance, nutrition disorders, medications employed, decreased function in sensory organs, infection, trauma, and malignancies commonly seen in the geriatric era play a preparatory role in delirium.…”

Section: Introductionmentioning

confidence: 99%

Mortality Rates during Hospitalization and Affecting Factors in Geriatric Delirium Patients: a Retrospective Cohort Study

Özdin

Bolat²

2021

Dement Neurocogn Disord

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Background and Purpose The purpose of this study was to investigate the prevalence of mortality during hospitalization among patients diagnosed with delirium at geriatric consultations requested in the previous one year, together with the factors affecting this. Methods The electronic medical records of geriatric consultations requested from the psychiatry department between January 1, 2019 and December 31, 2019 were examined from the automation system. The 200 geriatric delirium patients were included in the study. Patients' age, sex, length of hospital stay (LOHS), time between hospitalization and consultation, the department requesting consultation, reason for consultation request, psychiatric recommendations after consultation, reason for hospitalization, number of comorbid medical diseases, number of daily medications used, and history of psychiatric disease were retrieved from the electronic medical records in the automation system. Results LOHS and time from hospitalization to consultation were longer in the exitus group. Numbers of comorbid disease and daily medications used were higher in the died patients. Male gender, higher numbers of comorbid diseases, and daily medications were predictors of death. Conclusions Early detection of delirium may be important for short term results of disease. When evaluating these patients, reviewing the drugs used as much as possible can affect the outcome of the disease.

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Combined effects of four major geriatric syndromes on adverse outcomes based on Korean National Health Insurance claims data

Abstract: Delirium, fall-related fractures, incontinence and pressure ulcers were associated with increased risks of institutionalization and mortality. The magnitude of these risks increased with increasing numbers of coexisting geriatric syndromes. Geriatr Gerontol Int 2018; 18: 1463-1468.

Cited by 6 publications

References 26 publications

Prolonged Wait Time Prior to Entry to Home Care Packages Increases the Risk of Mortality and Transition to Permanent Residential Aged Care Services: Findings from the Registry of Older South Australians (ROSA)

Prolonged Wait Time Prior to Entry to Home Care Packages Increases the Risk of Mortality and Transition to Permanent Residential Aged Care Services: Findings from the Registry of Older South Australians (ROSA)

Identifying Predictors of Nursing Home Admission by Using Electronic Health Records and Administrative Data: Scoping Review

Mortality Rates during Hospitalization and Affecting Factors in Geriatric Delirium Patients: a Retrospective Cohort Study

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