Physical function decline predictors in nursing home residents using new national quality indicators.

AIM: To determine the predictors of physical function (PF) decline among nursing home residents using items from the Long-term care Information system For Evidence (LIFE), a system launched in 2021 to ensure the quality of long-term care. METHODS: The LIFE data of 1648 residents from 45 nursing homes in Japan were retrospectively collected in July 2021 (T0) and January 2022 (T1), including demographics, PF assessed by the Barthel index (BI), nutrition and oral health, and cognitive function. The Dementia Behavior Disturbance scale was used to assess the frequency of certain behaviors, such as "waking at midnight." The predictors of PF decline, defined as a decrease ≥5 in the BI score at T1 compared with that at T0, were determined using mixed-effects logistic regression analyses. PF at T0 was classified into high (>60 BI) and low (≤60 BI) groups. RESULTS: The participants' mean age was 87.2 ± 7.1 years, and 45.3% experienced PF decline. The significant predictors of PF decline were age ≥ 90 years, body mass index <18.5 kg/m2 , dementia diagnosis, moderate and severe cognitive impairments, not vocalizing reciprocal exchanges at will, always "waking at midnight," and high PF at T0. CONCLUSIONS: The LIFE items predicted PF decline among nursing home residents, suggesting that LIFE data can be used to ensure the quality of long-term care. Geriatr Gerontol Int 2024; 24: 123-132.

Unplanned Hospital Visits and Poor Oral Health With Undernutrition in Nursing Home Residents.

OBJECTIVES: In 2021, the Japanese government began operating a long-term care (LTC) database called the Long-Term Care Information System for Evidence (LIFE). However, its utility has not been verified. Regarding unplanned hospital visits of nursing home residents, one of the challenges in LTC is that poor oral health with undernutrition could indicate high-risk residents. Therefore, this study examined the association between poor oral health with undernutrition assessed using the LIFE data and unplanned hospital visits of nursing home residents. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: The participants were 237 residents aged ≥65 years in 4 nursing homes in Japan. The analyses included 1041 LIFE data entries repeatedly measured for the participants every month and unplanned hospital visit data during the observation period. METHODS: The participants' LIFE and unplanned hospital visit data were obtained from the nursing home providers. Poor oral health was defined using oral items included in the LIFE data and body mass index. Using the LIFE data, the association between poor oral health and unplanned hospital visits within 1 month after LIFE assessment entries was analyzed. The odds ratios (ORs) and 95% CIs were calculated using a generalized linear mixed model. RESULTS: In total, 59 of 1041 LIFE data (5.7%) entries were unplanned hospital visits within 1 month after LIFE assessment. Among patient characteristics, significant differences were noted in dementia diagnosis [OR (95% CI): 2.66 (1.26-5.63)], although no significant differences were observed in other characteristics. Multivariate analysis using participant identification as a random effect confirmed that poor oral health was associated with unplanned hospital visits within 1 month [adjusted OR (95% CI): 2.63 (1.05-6.61)]. CONCLUSIONS AND IMPLICATIONS: Poor oral health assessed using the LIFE data could be used as an indicator to identify nursing home residents at high risk for unplanned hospital visits.

Real-Time Prediction of Medical Demand and Mental Health Status in Ukraine under Russian Invasion Using Tweet Analysis.

Russia's invasion of Ukraine (February 24, 2022) has begun and there are concerns about the impact on health care supply and mental health. This study analyzed tweets in the Ukrainian language to capture the medical needs and mental health conditions in wartime Ukraine by focusing on ostensibly relevant words. The number of tweets containing the keywords and their overall proportion was compared before and after the Russian invasion of Ukraine. The survey period was divided into four phases-the pre-2022 Russian invasion, acute phase (4 weeks), subacute phase (12 weeks), and the chronic phase (8 weeks) up to August 10, 2022. The analysis targeted tweets sent in Ukrainian. The tweets were screened using a set of six classes with 75 key groups and 303 Ukrainian (204 original Japanese) keywords. Overall, 98,526,440 tweets were analyzed, with a pre-invasion and post-onset average of 1,096,976 and 3,328,243 tweets/week (a 3.0-fold increase), respectively. Of these, 3,197,443 tweets contained the keywords, with a pre-invasion and invasion average of 26,241 and 114,640 tweets/week (a 4.4-fold increase), respectively. The post-onset phase witnessed a considerable increase in all classes-medical services, treatment, medical resources, medical situations, and special situations-but not in the symptom class. Keywords related to psychological distress and anxiety immediately increased during the acute phase; those related to depression and post-traumatic stress reactions continued increasing as the invasion persisted, which may have reflected the mental state of those impacted. Analyzing tweets is useful for predicting people's real-time physical and mental health needs during wartime.

Use of Medical Information and Digital Services for Self-Empowerment before, during, and after a Major Disaster.

Disaster response procedures have been developed and improved following the Great East Japan Earthquake. Innovative services have also been created through digital transformation, including an acceleration and deepening of artificial intelligence technology. Things that were once technically impossible are now possible. These innovative technologies will spread across various fields, and disaster response will not be an exception. The Ministry of Health, Labour and Welfare is promoting the use of personal health records in a way that effectively supports the management of treatments by using data from wearable devices and specific applications. During the COVID-19 pandemic, the trade-off between protecting personal information and enabling social benefits, such as in the use of digital tracking, and infodemics, including misinformation, have become new social challenges. Reviewing past disaster preparedness and the services and value provided by digital transformation indicates what new disaster preparedness should be. Digital transformation does not require literacy (ability to collect, analyze, and use information) but competence (beneficial behavioral traits derived from experience). Understanding behavior through data and enabling rational behavior are crucial. By increasing human productivity, we can save time and improve self- and mutual-help in times of disaster. Medical information and digital services must be properly used in normal times. A society that uses such services will be more disaster resilient.

Incidence of aspiration pneumonia during hospitalization in Japanese hospitalized cases did not increase whereas concern factors were exacerbated in a time-dependent manner: analysis of Diagnosis Procedure Combination (DPC) data.

This study aimed i) to investigate about items with high relevance for aspiration pneumonia during hospitalization among cases evaluated using Diagnosis Procedure Combination data, and ii) to determine whether the concern factors for aspiration pneumonia during hospitalization were exacerbated with the trend of the time. The Diagnosis Procedure Combination data were gathered from 2010 through to 2015 with 63,390 cases at Saga University Hospital. The occurrence of concern factors of aspiration pneumonia during hospitalization were compared in the two time periods set (2010-2012 and 2013-2015). The concern factors included: male, age, dysphagia at admission and during hospitalization, use and days in the emergency care unit or high care unit, use of the intensive care unit, and use of an ambulance. Age, dysphagia, and use of the intensive care unit were time-dependently exacerbated. The incidence of aspiration pneumonia during hospitalization in hospitalized cases did not differ between years 2010-2012 and 2013-2015. Aspiration pneumonia during hospitalization complicated with surgery and number days in the emergency care unit or high care unit diminished in years 2013-2015. Despite an increased concern of aspiration pneumonia during hospitalization, the complication rate of aspiration pneumonia during hospitalization did not increase.

High Cost of Hospitalization for Colonic Diverticular Bleeding Depended on Repeated Bleeding and Blood Transfusion: Analysis with Diagnosis Procedure Combination Data in Japan.

BACKGROUND: Bleeding from a colonic diverticulum is serious in aged patients. The aim of this study was to determine the risk factors for high-cost hospitalization of colonic diverticular bleeding using the diagnosis procedure combination (DPC) data. METHODS: From January 2009 to December 2015, 78 patients with colonic diverticular bleeding were identified by DPC data in Saga Medical School Hospital. All patients underwent colonic endoscopy within 3 days. The patients were divided into 2 groups: the low-cost group (DPC cost of <500,000 yen) and the high-cost group (DPC cost of >500,000 yen). RESULTS: Univariate analysis revealed that aging, hypertension, rebleeding, a low hemoglobin concentration at admission, and blood transfusion were risk factors for high hospitalization cost. Multivariate analysis revealed that rebleeding (OR 5.3; 95% CI 1.3-21.3; p = 0.017) and blood transfusion (OR 3.8; 95% CI 1.01-14.2; p = 0.048) were definite risk factors for high hospitalization cost. CONCLUSION: Rebleeding and blood transfusion were related to high hospitalization cost for colonic diverticular bleeding.

Infectious disease during hospitalization is the major causative factor for prolonged hospitalization: multivariate analysis of diagnosis procedure combination (DPC) data of 20,876 cases in Japan.

This study aimed to evaluate causative factors for prolonged hospitalization based on hospitalization status, type of hospital ward, and comorbidities, specifically diabetes mellitus and infectious diseases, in 20,876 patients hospitalized in Saga University Hospital from April 1, 2012, to February 28, 2015. Prolonged hospitalization was defined as hospital days exceeding period 3 in the diagnosis procedure combination system. Among all factors, causative (risk) factors for prolonged hospitalization were evaluated by multiple logistic regression analysis. Multivariate analysis indicated causative factors for prolonged hospitalization were aging, comorbid diabetes mellitus, time spent in the intensive care unit, and infectious diseases contracted during hospitalization. The risk factors for contracting infectious diseases during hospitalization were aging, male sex, comorbid diabetes mellitus, and increased number of days spent in period 3 in the diagnosis procedure combination code. These data indicated that critical factors for discharge from hospital within an appropriate time frame were prevention of infectious diseases during hospitalization, and a fast and effective therapeutic approach to patients in the intensive care unit.