Association of social isolation with health status among community-dwelling Chinese older adults living with homecare services: a cross-sectional survey in Hong Kong.

Background: Defined as having few social relationships or infrequent social contact with family, friends, and the community, social isolation is a public health crisis. We aimed to evaluate the prevalence of social isolation and explore the association between social isolation and health status among community-dwelling Chinese Older Adults living with homecare services. Methods: This is a cross-sectional survey with a structured questionnaire conducted among older adults aged ≥60 in the Central Kowloon District of Hong Kong during 2017-2018. Social isolation was assessed by the Lubben Social Network Scale-6 and a score less than 12 was defined as socially isolated. Six aspects of health status including fall risk, cognitive function, depression, activities of daily living (ADL), instrumental activities of daily living (IADL), and functional mobility were measured by standardized instruments. Multi-criteria decision analysis (MCDA) was applied to estimate an index to represent the overall health status of the respondents. Multivariate logistic/linear regression models were applied to examine the associations between social isolation and health status after adjusting the sociodemographic characteristics. Results: Among the 1,616 participants included in this analysis, the mean age was 80.9 years, 66.3% were female and 41.4% were identified as socially isolated. Compared with the non-isolated group, the socially isolated group had higher proportions of males, divorced or unmarried, ever smoking and drinking, living alone, and living in public housing without religion. After adjusting for confounders, the odds ratios (OR) comparing the socially isolated vs. non-isolated groups were 2.52 (95%CI: 1.79, 3.56) for high fall risk, 1.51 (1.17, 1.94) for cognitive impairment, and 1.78 (1.31, 2.43) for depression. The socially isolated group increased the odds of abnormal ADL, IADL, and functional mobility by 105-150%, and decreased the overall health score by 5.30 (3.42, 7.18). Conclusion: We demonstrated the association of social isolation with poorer physical function and mental health and overall health status among the community-dwelling Chinese older adults living with homecare services. These findings provided new knowledge about the association of social isolation with both physical and mental function for daily living even for those receiving an integrated homecare service in the community. It implies that an unmet healthcare need existed when comparing the service scope of the current homecare services in the community. It also highlighted the need for targeted prevention and intervention initiatives among community-dwelling old adults to alleviate social isolation for better health and good functioning in the community.

A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong.

BACKGROUND: Accurately estimating elderly patients' rehospitalisation risk benefits clinical decisions and service planning. However, research in rehospitalisation and repeated hospitalisation yielded only models with modest performance, and the model performance deteriorates rapidly as the prediction timeframe expands beyond 28 days and for older participants. METHODS: A temporal zero-inflated Poisson (tZIP) regression model was developed and validated retrospectively and prospectively. The data of the electronic health records (EHRs) contain cohorts (aged 60+) in a major public hospital in Hong Kong. Two temporal offset functions accounted for the associations between exposure time and parameters corresponding to the zero-inflated logistic component and the Poisson distribution's expected count. tZIP was externally validated with a retrospective cohort's rehospitalisation events up to 12 months after the discharge date. Subsequently, tZIP was validated prospectively after piloting its implementation at the study hospital. Patients discharged within the pilot period were tagged, and the proposed model's prediction of their rehospitalisation was verified monthly. Using a hybrid machine learning (ML) approach, the tZIP-based risk estimator's marginal effect on 28-day rehospitalisation was further validated, competing with other factors representing different post-acute and clinical statuses. RESULTS: The tZIP prediction of rehospitalisation from 28 days to 365 days was achieved at above 80% discrimination accuracy retrospectively and prospectively in two out-of-sample cohorts. With a large margin, it outperformed the Cox proportional and linear models built with the same predictors. The hybrid ML revealed that the risk estimator's contribution to 28-day rehospitalisation outweighed other features relevant to service utilisation and clinical status. CONCLUSIONS: A novel rehospitalisation risk model was introduced, and its risk estimators, whose importance outweighed all other factors of diverse post-acute care and clinical conditions, were derived. The proposed approach relies on four easily accessible variables easily extracted from EHR. Thus, clinicians could visualise patients' rehospitalisation risk from 28 days to 365 days after discharge and screen high-risk older patients for follow-up care at the proper time.

Relationship between patient experience and hospital readmission: system-level survey with deterministic data linkage method.

BACKGROUND: Linkage of public healthcare data provides powerful resources for studying from a comprehensive view of quality of care than information for a single administrative database. It is believed that positive patient experiences reflect good quality of health care and may reduce patient readmission. This study aimed to determine the relationship between patient experience and hospital readmission at a system level by linking anonymous experience survey data with de-identified longitudinal hospital administrative admissions data. METHODS: Data were obtained by linking two datasets with anonymised individual-level records from seven largest-scale acute public hospitals over seven geographical clusters in Hong Kong. Selected records in the two datasets involving patient experience survey (PES) (2013 survey dataset) and healthcare utilization (admissions dataset) were used. Following data cleaning and standardization, a deterministic data linkage algorithm was used to identify pairs of records uniquely matched for a list of identifiers (10 selected variables) between two datasets. If patient's record from the survey dataset matched with the hospitalization records in the admissions dataset, they were included in the subsequent analyses. Bivariate analyses and multivariable logistic regression models were performed to evaluate the associations between hospital readmission in the next calendar month and patient experience. RESULTS: The overall matching rate was 62.1% (1746/2811) for PES participants aged 45 or above from the survey dataset. The average score for overall inpatient experience was 8.10 (SD = 1.53). There was no significant difference between matched patients and unmatched patients in terms of their score for the perception of overall quality of care received during hospitalization (X2 = 6.931, p-value = 0.14) and score for overall inpatient experience (X2 = 7.853, p-value = 0.25). In the multivariable model, readmission through the outpatient department (planned admission) in the next calendar month was significantly associated with a higher score given to the overall quality of care received (adjusted OR = 1.54, 95%CI = 1.09-2.17), while such association was absent for readmission through Accident and Emergency department (adjusted OR = 0.75, 95%CI = 0.50-1.12). CONCLUSIONS: This study demonstrated the feasibility of routine record linkage, with the limited intrusion of patients' confidentiality, for evaluating health care quality. It also highlights the significant association between readmission through planned readmission and a higher score for overall quality of care received. A possible explanation might be the perceived better co-ordination between outpatient departments and inpatient service and the well-informed discharge plan given to this group of patients.

Impacts of Export Restrictions on the Global Personal Protective Equipment Trade Network During COVID-19.

The COVID-19 pandemic has caused a dramatic surge in demand for personal protective equipment (PPE) worldwide. Many countries have imposed export restrictions on PPE to ensure the sufficient domestic supply. The surging demand and export restrictions cause shortage contagions on the global PPE trade network. Here, an integrated network model is developed, which integrates a metapopulation model and a threshold model, to investigate the shortage contagion patterns. The metapopulation model captures disease contagion across countries. The threshold model captures the shortage contagion on the global PPE trade network. Due to the Pareto distribution in global exports, the shortage contagion pattern is mainly determined by the export restriction policies of the top exporters. Export restrictions exacerbate the shortages of PPE and cause the shortage contagion to transmit even faster than the disease contagion. To some extent, export restrictions can provide benefits for self-sufficient countries, at the sacrifice of immediate economic shocks at not-self-sufficient countries. With export restrictions, a large amount of PPE is hoarded instead of being distributed to where it is most needed, particularly at the early stage. Cooperation between countries plays an essential role in preventing global shortages of PPE regardless of the production level. Except for promoting global cooperation, governments and international organizations should take actions to reduce supply chain barriers and work together to increase global PPE production.

Compliance with Standard Precautions and Its Relationship with Views on Infection Control and Prevention Policy among Healthcare Workers during COVID-19 Pandemic.

Background: Standard precautions prevent the spread of infections in healthcare settings. Incompliance with infection control guidelines of healthcare workers (HCWs) may increase their risk of exposure to infectious disease, especially under pandemics. The purpose of this study was to assess the level of compliance with the infection prevention and control practices among HCWs in different healthcare settings and its relationship with their views on workplace infection control measures during the COVID-19 pandemic. Methods: Nurses in Hong Kong were invited to respond to a cross-sectional online survey, in which their views on workplace infection and prevention policy, compliance with standard precautions and self-reported health during pandemics were collected. Results: The respondents were dissatisfied with workplace infection and prevention policy in terms of comprehensiveness (62%), clarity (64%), timeliness (63%), and transparency (60%). For the protective behavior, the respondents did not fully comply with the standard precautions when they were involved in medical care. Their compliance was relatively low when having proper patient handling (54%) and performing invasive procedures (46%). A multivariate analysis model proved that the level of compliance of the standard precautions was positively associated with the satisfaction on infection control and prevention policy among high risk group (0.020; 95% CI: 0.005-0.036), while older respondents had higher level of compliance among the inpatient and outpatient groups (coefficient range: 0.065-0.076). The higher level of compliance was also significantly associated with working in designated team and having chronic condition of the respondents among high-risk and inpatient groups. Conclusions: Standard precautions are the most important elements to reduce cross-transmission among HCWs and patients while the satisfaction on infection control and prevention policy would increase the compliance among the high-risk group. An overall suboptimal compliance and poor views on the infection prevention and control guidelines is a warning signal to healthcare system especially during pandemics.