Relationship Between Unit Characteristics and Fall Incidence: A Cross-Sectional Survey Using Administrative Data in Japan

ABSTRACT Background Falls are the most frequent accident experienced by inpatients in hospitals. As falls affect patient outcomes, high fall risk factors should be studied to prevent falls and improve patient safety. However, the relationship between hospital unit characteristics and fall risk has never been assessed. Purpose This study was designed to identify the unit characteristics significantly related to fall risk. Methods A cross-sectional study was conducted on the medical records of patients hospitalized in a Japanese academic hospital between 2018 and 2019. This study quantified unit activities and utilized Diagnosis Procedure Combination data to examine unit characteristics related to falls based on unit day. Results Data on 16,307 patients were included in the analysis, and 355 unit days were certified as fall events. Based on patient condition and medical treatment, the results identified antineoplastic injections, radiation therapy, aseptic treatment room, and functional status of partly assisted transfers, meals, and oral care as unit characteristics associated with increased fall events. Decreased nursing time per patient at night (odds ratio [OR] = 0.75, p = .04) and higher numbers of partially assisted transfer patients were also identified as unit characteristics associated with higher fall incidence rates (OR = 5.56, p = .01). Conclusions The results of this study are expected to assist nurses to predict falls based on unit characteristics; reducing nursing time in the units was found to be a factor associated with higher fall risk. Nurse managers must understand the unit-related fall risk factors, appropriately assign nurse staffing numbers, and demonstrate nursing leadership to prevent falls in their units.


Introduction
Falls among inpatients affect health outcomes and are economic issues in hospitals.The incidence of falls is between 700,000 and 1 million patients annually in the United States, and studies have indicated that approximately one third of falls are preventable (Agency for Healthcare Research and Quality, 2023).Aging-related impaired mobility is one of the most well-known risk factors for falls.According to Japan's Ministry of Health, Labour and Welfare, Japan is the world's most super-aged society, with 35% of the population anticipated to be over 65 years old by 2040.The high risk of falls in hospitalized patients due to aging is increasing.Hence, preventing falls is an important and urgent area of risk management for healthcare organizations.
The impact of falls on patient safety has been of interest to nurses since the 1990s.Many studies have developed fall assessment scales and explored the relationship between nurse staffing level and falls.The results of previous studies indicate that higher registered nurse (RN) staffing is associated with lower fall rates (Staggs & Dunton, 2014).Moreover, staffing levels help improve patient fall performance (Cooke et al., 2022).Patient characteristics influencing falls include age and impaired mobility.Moreover, in terms of organizational factors, patients who receive more hours of care from RNs are significantly less likely to fall (Kim et al., 2019).Although many studies have found that better patient outcomes depend on higher nurse staffing levels, drawing robust conclusions between patient outcomes and nurse staffing levels in acute care hospitals has been difficult (Morioka et al., 2022).
Nursing systems in Japan often rely on hospitals and units.Most hospitals apply a team nursing framework in which, although all nurses are assigned specific patients, nursing care for the unit is a collective responsibility.Acute care hospitals generally apply a nurse-patient ratio of either 1:7 or 1:10.Nurse managers may vary this ratio based on unit characteristics.Thus, nurse managers within the team nursing framework must possess control and leadership abilities.Falls are affected by patient characteristics and environmental factors in the unit.Hence, patient safety in preventing falls is easily influenced by unit characteristics in team nursing.The great dispersion in data between administrative and unit-level data emphasizes the importance of unit-level data sources in measuring nursing staffing in healthcare facilities (Spetz et al., 2008).Consequently, evaluating fall-related risks, including those related to unit characteristics, is necessary.
No studies in Japan have examined falls related to unit characteristics such as nursing staffing and nursing hours.Therefore, this study was designed to determine the fall risk factors associated with unit environmental characteristics, nursing staffing, and nursing hours.This study quantified unit activities and utilized Diagnosis Procedure Combination (DPC) data to evaluate unit characteristics related to falls based on unit day.

Data Sources
The DPC database, which includes the Severity of a Patient's Condition and Extent of a Patient's Need for Medical/Nursing Care (SCNMN) database, the work record notification system (Format 9), and fall incidences reported on the hospital's incidence report system were used as data sources in this study.We linked Format 9 and fall incidence to the DPC data.

Diagnosis procedure combination database
The DPC was developed as a case-mix patient classification system for acute inpatient hospitals to make acute care more transparent and visible (Shigemi et al., 2021).This system started as a lump-sum per-diem payment system in 2003 and has been utilized for acute inpatient care and the distribution of medical resources.Many acute hospitals joined the DPC system to report medical information on Japanese Labor and Welfare (Hayashida et al., 2021;Yasunaga et al., 2012).According to the National Ministry of Health, Labor, and Welfare, 1,757 facilities and 483,180 beds in 2020 were included in the DPC system, covering 24.5% of all general hospitals and 54.4% of the medical beds in the country.The DPC data system is unique in terms of including diagnosis, dates of admission and discharge, hospital admission route, hospital discharge route, outcomes at discharge, and surgical procedure information along with international disease classification information for related health problems as defined under the 10th revision code (e.g., patient age, gender, main diagnoses, preexisting comorbidity diagnoses, and postadmission complications; Hayashida et al., 2021;Yamana et al., 2017).Japan's DPC data have already been used in many epidemiological studies (Oku et al., 2021), and using the database to estimate nurse workload is potentially feasible (Kamijo & Kanda, 2008).
Severity of a Patient's Condition and Extent of a Patient's Need for Medical/Nursing Care The SCNMN is an index originally developed in Japan to measure the nursing care demand of inpatients.The SCNMN is used mainly for the standardization of medication fees, especially basic hospitalization fees for acute care.The SCNMN has been configured into three classifications, with each classification consisting of seven items for a total of 21 items.The three classifications include monitoring and treatment items (used to assess highly specialized nursing care), medical management items (used to evaluate medical treatments related to surgery and emergency care), patient functional status items (including activities of daily living [ADLs], which influence medical care; Hayashida et al., 2022).RNs collect these items daily and enter them into a database.

Work record notification system (format 9)
A work record notification system records individual nursing times, excluding overtime.Nursing staffing (nursing time) must be reported as a reference for basic hospitalization fees, which are claimed payments from medical facilities to the government.Format 9 is recorded in a national unification format.

Fall incidence
From April 2018 to March 2019, fall cases were extracted from the accumulated incident cases in the hospital's incidence report system.We adopted the definition of fall used by Kennedy: "unintentionally coming to the ground or some lower level other than because of sustaining a violent blow, loss of consciousness, sudden onset of paralysis as in stroke, or an epileptic seizure" (Kennedy, 1987).In this study, fall cases that were not under the hospital's control, such as stay-over falls, duplicated reports, and certifiable disease exacerbation, were excluded from the analyzed data.

Study Population
The study population included patients hospitalized at the academic hospital in Japan between April 2018 and March 2019.Patients in pediatric, obstetric, and intensive care units were excluded.The targeted academic hospital had 1,061 RNs with 772 beds as of October 2022.

Patient variables
Variables related to sociodemographic status included age and gender; variables related to patient clinical status included with or without sedative-hypnotics, hypertension, osteoporosis, anemia, comorbidities at hospitalization, and emergency hospitalization.Comorbidities at hospitalization were assessed using the Charlson comorbidity index (CCI) score and calculated using the Quan version (Sundararajan et al., 2007).The variables of the SCNMN were classified into three items: monitoring and treatment, patient functional status, and medical The Journal of Nursing Research Mutsuko MORIWAKI et al.
management, for example, medical treatment related to surgery and emergency care.All applicable variables were divided by the number of patients in the units per day to calculate the numeric units per unit day.

Nursing time variable
Individual nursing times during day (8 hours) and night (16 hours) shifts per unit were recorded in Format 9. Nursing time per patient throughout the day and night shifts was calculated as the total nursing shift hours divided by the number of patients under a nurse's charge.

Statistical Analyses
The following procedures were analyzed: First, chi-squared or Mann-Whitney U tests were used to assess the associations between fall incidence and nonfall incidence and the following variables: sociodemographic status, patients' clinical status, monitoring and treatment, patient functional status, medical management, and nursing time.The finalized variables were selected as significant variables using comparison tests.Finally, logistic regression analysis was performed using these variables as independent variables and fall and nonfall units as dependent variables.The required sample size was not calculated in advance in this study because the reliable effect size (in this case, the proportions and/or the odds ratio [OR]) was not available.Instead, the achieved power was calculated using a post hoc power analysis when no significant difference was found.Statistical analyses were performed using SPSS Version 28 (IBM Corp., Armonk, NY, USA).

Ethical Considerations
This study was approved by the ethical review board of the Graduate School of Medicine, Tokyo Medical and Dental University (approval number: M2018-284).

Sample Description
This study included 16,307 patients out of the total 168,159 patients in the DPC data (Figure 1).The DPC data were linked to the hospital incidence report and work record notification systems, and 4,380 unit days were extracted.Of the 4,380 unit days, 355 were certified as fall events, with 15 fall cases deducted due to the simultaneous occurrence of two fall event cases per day and unit.Thus, 340 cases were selected as analyzable unit-day fall events.Fall incidence unit days were assigned to the fall group, and nonfall incidence unit days were assigned to the nonfall group.Patient characteristics are presented in Table 1.

Comparing Fall Events and Nonfall Events in Terms of Unit Characteristics
Using the nonparametric Mann-Whitney U test, significant differences were observed between fall and nonfall events per unit for the following patient characteristics: sedativehypnotics, hypertension, anemia, and 1-point CCI (Table 2).
No significant differences were observed based on day of the week or between weekdays and weekends.Furthermore, the comparison test between fall and nonfall identified antineoplastic injections, radiation therapy, aseptic treatment

Figure 1
Flowchart of the Study Selection Process room, and functional status of partly assisted transfers, meals, and oral care as significant patient condition and medical treatment predictors (Table 3).

Logistic Regression Analysis for Significant Variables Based on Comparison Tests
Logistic regression analysis was performed on the significant variables in the comparison tests.Variables with a patient ratio of less than 0.01 and duplicated diagnoses with CCI were excluded.Reduced nursing time per patient during night shifts (OR = 0.75, p = .04)and higher numbers of patients requiring partial transfer assistance (OR = 5.69, p = .01)were found to be significantly related to fall incidence in the unit (Table 4).

Discussion
The analyses in this study confirmed an association between fall incidence and nursing time as unit characteristics.Nurse staffing and RN levels have previously been associated with patient outcomes in hospitals (Griffiths et al., 2021;Lasater et al., 2021;Wang et al., 2020).Many studies have claimed that increased nurse-patient ratio and nursing time per patient benefit patient outcomes, including fall prevention and hospital economic issues (Harrison et al., 2019;Kim et al., 2022).In this study, nursing time per patient was found to relate significantly to falls, with a higher risk of falling associated with night shifts with decreased nursing time per patient.Fall  incidences increase considerably during night shifts due to the smaller numbers of RNs working these shifts in Japan.
To reduce the fall rate, numbers of RNs and nursing time per patient should be increased at the shift level (He et al., 2012;Patrician et al., 2011).Increasing nursing staff and time of care in hospitals is a difficult issue.Nevertheless, nurse managers may be required to modify nursing staffing based on the risk factors associated with their unit characteristics.According to Kim et al. (2022), higher rates of newly graduated RNs are associated with higher fall incidence ORs (OR = 7.32).Thus, RN experience level as well as RN staffing levels may influence fall incidence.ADL is a strong factor of influence on in-hospital falls.Similar to previous studies, the impaired patient mobility was identified as a key factor of influence in this study.The increased partly assisted patients in the unit increase the incidence of fall risk in the unit.Patients requiring full ADL assistance are typically not associated with a high fall risk because they are provided with intensive nursing care (Kim et al., 2022;Staggs & Dunton, 2014).In line with this, patients requiring partial assistance for transfer and meal intake in this study were identified as at a high risk of falls.Previous studies have identified these unit characteristics as significantly correlated with patient fall risk.Although aging may generally increase the risks of falls and fall injuries, older age was not associated with fall risk in this study.This finding may be partially explained by the academic hospital setting used in this study.In addition, number of patients in the unit with conditions associated with dizziness, for example, hypertension, anemia, and surgery, was not identified as a unit characteristic significantly associated with fall risk.
A previous study revealed that a high ratio of patients in the fall group used significant amounts of hypnotics (Hart et al., 2020;Yoshikawa et al., 2020), with use of benzodiazepines significantly associated with falls (Sogawa et al., 2022).Falls often influence medication use.Approximately three quarters of all falls in nursing homes were found to be affected by medications, with > 80% associated with psychotropic drug usage (Oya et al., 2022).In this study, a significant relationship between certain medications/treatments and falls was observed.Nurse managers should pay attention to falls when there is a tendency for the number of patients taking sedative-hypnotics, using antineoplastic injections, and using radiation therapy to increase in their units.
Falls in inpatients significantly affect quality of care, healthcare outcomes, and hospital reimbursements (LeLaurin & Shorr, 2019).The risk factors for falls are highly complicated, placing multitasking demands on nurses.Many researchers have attempted to determine how to reduce falls.Fall prevention tools and patient education may be effective in reducing falls (Dykes et al., 2020), although a Cochrane review published in 2018 found little evidence supporting effective tips for hospital falls and concluded that multifactorial interventions may be used to reduce fall rates (Cameron et al., 2012;Morris et al., 2022).Nurse managers should better recognize the risk factors associated with falls and implement prevention strategies to improve patient safety.Nurse managers are responsible for their entire units as well as individual patients.Thus, they should maintain a comprehensive perspective on their units and identify/ameliorate potential fall risks.

Limitations
This study was affected by several limitations.First, this pilot study focused on one hospital in Japan only.Thus, generalizing the results to general nursing practice settings may be difficult.The authors are currently conducting an advanced multicenter study based on the results of this study.Second, patient factors are more likely to influence patient falls than environmental factors.However, this study focused on the relationship between unit characteristics and patient falls and thus does not address Note.Nagelkerke R 2 = .03,Hosmer-Lemeshow test: p = .55.Analyzed by dependent variable of fall incidence in the unit: 1.

The Journal of Nursing Research
Mutsuko MORIWAKI et al.
the influence of patient characteristics.Nonetheless, patient factors also influenced the results found in this study.Future research must examine intrapatient correlations because patients who fall tend to fall multiple times.In addition, to better quantify unit activities, future prospective studies on falls related to environmental factors should examine variables such as medication errors that have a lower impact on patient factors.

Conclusions
This was the first study to assess fall factors related to unit environmental characteristics by quantifying unit activities.This was also the first attempt to utilize DPC data to evaluate unit characteristics related to falls per unit day.Big data such as DPC make nursing studies feasible for analyzing the effectiveness of nursing care, arranging operative nurse staffing by calculating nursing time, and tracking trends in nursing activities over time.By calculating nursing time throughout the year, the findings reveal a relationship between unit characteristics and falls in inpatient acute hospital settings as well as the risks of reducing nursing time during night shifts.Nurse managers must understand the unit-characteristic-related risk factors of falls and demonstrate nursing leadership to reduce fall risk throughout their unit.

Table 2
Comparison Between Falls and Nonfalls Based on Unit Characteristics

Table 3
Comparison Between Falls and Nonfalls by Severity of Patient Condition and Extent of Patient Need for Medication/Nursing Care (SCNMN) per Unit

Table 4
Logistic Regression Analysis for the Variables Related to Fall Unit Characteristics(N = 4,380)