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How the 2018 Japan Floods Impacted Nursing Home Admissions for Older Persons: A Longitudinal Study Using the Long-Term Care Insurance Comprehensive Database

Open AccessPublished:December 29, 2022DOI:https://doi.org/10.1016/j.jamda.2022.11.021

      Abstract

      Objectives

      As disasters become more frequent because of global warming, countries across the world are seeking ways to protect vulnerable older populations. Although these conditions may increase nursing home admission (NHA) rates for older persons, we know of no studies that have directly tested this hypothesis.

      Design

      This was a retrospective cohort study.

      Setting and Participants

      We analyzed data from long-term care insurance (LTCI) users in 3 Japanese prefectures that incurred heavy damage from the 2018 Japan Floods, which is the largest recorded flooding disaster in national history. Specifically, we extracted NHA data from the LTCI comprehensive database, both for disaster-affected and unaffected individuals.

      Methods

      We employed the Cox proportional hazards model to calculate multivariate-adjusted hazard ratios (HRs) for NHAs within a 6-month period following the 2018 Japan Floods, with adjustments for potential confounding factors.

      Results

      Of the 187,861 individuals who used LTCI services during the investigated period, we identified 2156 (1.1%) as disaster affected. The HR for NHA was significantly higher for disaster-affected (vs unaffected) individuals (adjusted HR 3.23: 95% CI 2.88‒3.64), and also higher than the HRs for older age (90-94 years vs 65-69 years: 2.29, CI 1.93‒2.70), cognitive impairment (severe impairment vs normal: 1.40, CI 1.25‒1.57), and physical function (bedridden vs independent: 2.27, CI 1.83‒2.70). According to our subgroup analyses, the adjusted HR for disaster-affected individuals unable to feed themselves was 6.00 (CI 3.68‒9.79), with a significant interaction between the 2 variables (P = .01).

      Conclusions and Implications

      Natural disasters increase the risk of NHA for older persons, especially those who are unable to feed themselves. Health care providers and policymakers should understand and prepare for this emerging risk factor.

      Keywords

      Nursing home admission (NHA) starkly impacts the older population, including persons who are totally dependent on care and those who independently live at home.
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      • O'Sullivan M.
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      Predicting admission to long-term care and mortality among community-based, dependent older people in Ireland.
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      • et al.
      Predictors of early mortality among hospitalized nursing home residents.
      • Keefe J.
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      The care continues: Responsibility for elderly relatives before and after admission to a long term care facility.
      • Beerens H.C.
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      • et al.
      Change in quality of life of people with dementia recently admitted to long-term care facilities.
      NHA not only influences quality of life and mortality for the individual older person, but also impacts their families, thus, increasing the economic burden carried by society.
      • Gorges R.
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      • Konetzka R.
      A national examination of long-term care setting, outcomes, and disparities among elderly dual eligibles.
      Known risk factors for NHA include older age, Hispanic ethnicity, living alone, being female, poor cognitive function, the absence of family caregivers, and insufficient access to care facilities.
      • Lalic S.
      • Sluggett J.
      • Ilomäki J.
      • et al.
      Polypharmacy and medication regimen complexity as risk factors for hospitalization among residents of long-term care facilities: A prospective cohort study.
      To avoid undesirable consequences, NHA should be prevented or delayed as much as possible, although it is often unavoidable, especially when care burdens increase during and after disasters.
      Global climate change has increased the scale and frequency of natural disasters,
      • Van Aalst M.K.
      The impacts of climate change on the risk of natural disasters.
      but it is unclear how this influences the transition to nursing homes (NHs) for older persons. Meanwhile, such events destroy their homes and lifelines, disrupt the actions of caregivers, and generally interfere with the continuation of home life. To compound matters, these conditions are known to worsen chronic disease and increase care needs.
      • Aldrich N.
      • Benson W.F.
      Peer reviewed: disaster preparedness and the chronic disease needs of vulnerable older adults.
      Thus, natural disasters constitute a critical risk factor for NHA among older persons, but there is no scholarly consensus on this topic, nor are there direct guidelines or legislative provisions.

      Catastrophic Rainfall in Western Japan: the 2018 Japan Floods

      The 2018 Japan Floods occurred during the end of June and beginning of July 2018. This was the largest flooding disaster in recorded national history, resulting in 75,000 power outages and 26,000 water cuts because of infrastructure fragmentation.
      Cabinet Office Government of Japan
      Summary: the damage situations caused by the heavy rain in July (in Japanese).
      A total of 6700 and 11,000 houses were completely or partially destroyed, respectively, reaching 10 billion USD in damages (1 trillion yen); only the Great East Japan Earthquake created more national damage.
      Ministry of Land, Infrastructure, Transport and Tourism
      Torrential rains in July 2008 caused the largest amount of damage since statistics began.
      Of the 47 Japanese prefectures, Hiroshima, Okayama, and Ehime were most affected, with 89.4% of all deaths (212 of 237), 79.2% of all injuries (342 of 432), and 2 million evacuations.
      Cabinet Office Government of Japan
      Summary: the damage situations caused by the heavy rain in July (in Japanese).
      Given its scale, the Japanese government designated the 2018 Japan Floods as a “catastrophic disaster,” thus, granting long-term assistance to survivors under the Law Concerning Special Fiscal Aid for Coping with Disasters in Japan. Although it is also likely that care provisions for older persons were critically impacted, there is no relevant information.

      Study Objectives

      Using a comprehensive database on all 5,096,000 public long-term care insurance (LTCI) users in Japan, we extracted data from residents of Hiroshima, Okayama, and Ehime prefectures, thus, providing a basis for examining how the floods impacted NHA for older persons. We compared the NHA risk between older persons who were disaster-affected and unaffected within each region, with an additional focus on influential personal characteristics. Our findings provide evidence that health professionals and policymakers can use to ensure disaster preparedness in LTCI services, especially in the geriatric context.

      Methods

      Study Design and Setting

      Focusing on Hiroshima, Okayama, and Ehime prefectures, we considered data from July to December 2018 (6 months postdisaster). In June 2018, these areas contained a combined population of approximately 6 million (4.7% of the national population). Inhabitants ages 55‒64, 65‒74, 75‒84, and ≥85 years comprised 12.2% (n = 742,000), 14.7% (n = 896,000), 9.9% (n = 606,000), and 5.1% (n = 312,000) of the total, respectively (similar to national percentages, at 12.2%, 13.9%, 9.5%, and 4.3%, respectively
      • Matsuda T.
      • Iwagami M.
      • Suzuki T.
      • Jin X.
      • Watanabe T.
      • Tamiya N.
      Correlation between the Barthel Index and care need levels in the Japanese long-term care insurance system.
      ). The study protocol was approved by the institutional review board of Hiroshima University (E-1389).

      LTCI Claims in Japan

      The LTCI program is a public welfare system that is operated by each local government. It covers all formal nursing services for disabled older persons and reduces the family caregiving burden.
      • Tsutsui T.
      • Muramatsu N.
      Care-needs certification in the long-term care insurance system of Japan.
      ,
      • Houde S.C.
      • Gautam R.
      • Kai I.
      Long-term care insurance in Japan: implications for U.S. long-term care policy.
      Residents over 40 years of age must join the system and can receive services at 65 years of age.
      Statistics of Japan
      Vital statistics vital statistics of Japan final data summary volume 1 3-3-1 summary tables of vital statistics:Japan, each prefecture and 21 major cities.
      Copayments may range from 10% to 30%, depending on income, but cannot exceed 44,000 yen ($420 USD) per month. The LTCI covers welfare equipment rentals, day care, home visit services, short stays, and most institutional placements. Only a few facilities (eg, serviced residences for older persons) are not covered. The details are described elsewhere.
      • Tsutsui T.
      • Muramatsu N.
      Care-needs certification in the long-term care insurance system of Japan.
      ,
      • Houde S.C.
      • Gautam R.
      • Kai I.
      Long-term care insurance in Japan: implications for U.S. long-term care policy.

      Inclusion Criteria

      We included LTCI enrollees (1) with a record of using long-term care (LTC) services and (2) without any NH placement as of July 2018. However, we excluded individuals with missing covariates. The database did not indicate why any individual was lost to follow-up, including whether they died, were hospitalized, or fully recovered. We obtained all study data from the LTCI Comprehensive Database, which is maintained by the Japanese Ministry of Health, Labor and Welfare (special permission under approval no. 0711-1).

      Outcomes

      The study outcome occurred when a person using any LTCI service without any previous NH placements began a long-term NH stay. Because of both our relatively short consideration period (6 months) and the lack of indicated reasons for facility admission/discontinuation, we defined an outcome occurrence as lasting more than 1 month after admission. The type and municipal-level location of NH, date of NHA and length of stay were recorded in the database. Individuals were censored upon their first event, with no subsequent readmissions considered events.
      There are 4 types of NH in Japan, including LTC facilities (tokubetsu-yougo-roujin-homu) for constant nursing, subacute-care facilities (kaigo-roujin-hokenshisetsu) for rehabilitation support prior to home return, geriatric hospitals (kaigo-ryouyougata-iryoushisetsu), and skilled nursing facilities (kaigo-iryouin) for continuous medical care.
      • Sanford A.M.
      • Orrell M.
      • Tolson D.
      • et al.
      An international definition for "nursing home".
      ,
      • Tolson D.
      • Rolland Y.
      • Katz P.R.
      • et al.
      An international survey of nursing homes.
      We considered all 4 types because of their clear LTC roles. Given the long application list, it typically takes 2 years to receive admission to LTC facilities.

      The city of Hiroshima, Hiroshima list of applicants for admission to special nursing homes for the elderly in Hiroshima city, 2022. Accessed May 13, 2022. https://www.city.hiroshima.lg.jp/site/kaigo/2432.html

      Thus, subacute care facilities, geriatric hospitals, and skilled nursing facilities serve as links for subsequent placement; of patients thus discharged, only 20% return home, while 50% enter other hospitals or facilities.
      • Okochi J.
      The role and perspective of the geriatric health services facilities.

      Identifying Disaster-Affected Individuals

      The Disaster Relief Act exempts disaster-affected individuals from all LTC service copayments,
      Ministry of Health, Labour and Wellfare
      The 2018 Japan Floods Exemption Requirement For Affected Residents.
      given certification under one of the following: (1) home completely or partially destroyed or (2) primary family caregiver seriously injured or lost job. We considered individuals with newly waived copayments from insurance claims as the disaster-affected group (exposure group). We identified disaster-affected individuals with fully exempt copayments in the insurance claims database after the onset of the 2018 Japan Floods.

      Potential Confounding Factors

      As covariates, we included age, sex, cognitive function (4-point scale), activities of daily living (ADL) (8-point scale), eating dependence status, and the number of days care services were used each month. We classified age as a categorical variable (<65, 65‒69, 70‒74, 75‒79, 80‒84, 85‒90, 90‒94, and >94 years). As confirmed via family physician, we used information on cognitive function, ADL, and eating dependence to determine the level of care need within the LTCI system.
      • Sato S.
      • Kakamu T.
      • Hayakawa T.
      • et al.
      Predicting falls from behavioral and psychological symptoms of dementia in older people residing in facilities.
      All covariates were obtained via the LTCI comprehensive database.

      Statistical Methods

      For the baseline characteristics, continuous variables were expressed as means (standard deviations), while categorical variables were expressed as numbers and proportions. To detect differences between study groups (disaster-affected vs unaffected), we used the χ2 or Fisher exact test to compare dichotomous variables, and used the Student t-test or Wilcoxon rank-sum test to compare continuous and categorical variables.
      We estimated the event-free rate (EFR) (ie, proportion of individuals without NHA) via the Kaplan-Meier survival analysis, then compared intergroup survival estimates via the log-rank test. Data were censored in December 2018. Individuals were censored upon the date of their loss to follow-up. EFR was calculated from July 2018 to the NH admission date.
      We calculated the hazard ratio (HR) for NHA between study groups using Cox proportional hazards model. We calculated HRs via 3 models, including the crude, age- and sex-adjusted, and adjusted for covariates (ie, multivariate-adjusted).
      We also conducted a subgroup analysis to determine which factors influenced the disaster-based NHA risk for each covariate (ie, age, sex, ADL, cognitive function, and eating dependence status). Using the multivariate-adjusted model, we calculated the interaction effect between disaster-affected status and each subgroup to conduct a likelihood ratio test in which we compared the term with each main result of the multivariable Cox proportional hazards model. In the same subgroup, we determined a 6-month cumulative EFR and 95% confidence interval (CI). We also conducted a sensitivity analysis with a 2-month period of facility utilization as the outcome. All data were analyzed using Stata v 16.1 (StataCorp LP), with significance established at P values of < .05 (2-tailed).

      Results

      Individuals

      We initially included 188,589 individuals who had used LTCI services via home visits, day care, or short-term care services as of July 2018 (Figure 1). However, we excluded 728 because of missing data, resulting in 187,861 for full analysis. The median age category was 85‒90 years, with 30.9% men. Of all individuals, 1.1% were disaster affected. There were no differences in age category, cognitive ability, or ADL between the study groups (Table 1). The disaster-affected group used fewer home visiting services and day care services, but used more short-stay services.
      Table 1Baseline Characteristics of Study Sample
      Total (N = 187,861)Disaster-Affected (n = 2156)Unaffected (n = 185,705)P Value
      Age, categorical (%).27
      Wilcoxon rank-sum test.
       45‒643810 (2.0)52 (2.4)3758 (2.0)
       65‒697721 (4.1)83 (3.8)7638 (4.1)
       70‒7413,206 (7.0)153 (7.0)13,053 (7.0)
       75‒7922,768 (12.1)281 (13.0)22,487 (12.1)
       80‒8440,697 (21.7)453 (21.0)40,244 (21.7)
       85‒9051,743 (27.5)614 (28.5)51,129 (27.5)
       90‒9434,941 (18.6)374 (17.3)34,567 (18.6)
       >9412,975 (6.9)146 (6.8)12,829 (6.9)
      Sex
       Men, n (%)58,172 (31.0)691 (32.1)57,481 (31.0).27
      χ2 test.
      Eating dependence (%).02
      χ2 test.
       Partial or unassisted180,260 (96.0)2087 (96.8)178,173 (95.9)
       Fully assisted7601 (4.0)69 (3.2)7532 (4.1)
      Cognitive skills (%).15
      Wilcoxon rank-sum test.
       166,180 (35.2)766 (35.5)65,414 (35.2)
       265,077 (34.6)784 (36.4)64,293 (34.6)
       342,125 (22.4)462 (21.4)41,663 (22.4)
       414,479 (7.7)144 (6.7)14,335 (7.7)
      ADL (%).3
      Wilcoxon rank-sum test.
       135,967 (19.1)410 (19.0)35,557 (19.1)
       242,487 (22.6)505 (23.4)41,982 (22.6)
       326,621 (14.2)317 (14.7)26,304 (14.2)
       437,168 (19.8)419 (19.4)36,749 (19.8)
       525,512 (13.6)300 (13.9)25,212 (13.6)
       68366 (4.5)99 (4.6)8267 (4.5)
       79533 (5.1)88 (4.1)9445 (5.1)
       82207 (1.2)18 (0.8)2189 (1.2)
      Number of users by type of LTC services (%)<.001
      χ2 test.
       In-home57,504 (30.6)525 (24.4)56,979 (30.7)
       Day care112,042 (59.3)1236 (57.3)110,806 (59.7)
       Short stay22,129 (11.8)425 (19.7)21,704 (11.7)
      Number of days per month using the facilities, mean (SD)
       Home visit services11.03 (7.95)7.21 (7.10)11.07 (7.95)<.001
      2-sample t-test.
       Day care8.74 (6.28)7.06 (6.64)8.75 (6.28)<.001
      2-sample t-test.
       Short stay11.55 (9.05)14.76 (8.6)11.49 (9.05)<.001
      2-sample t-test.
      SD, standard deviation.
      Cognitive status is classified into 4 levels: (1) Independent: those able to make coherent decisions in daily life and make plans for what needs to be done each day and to judge situations; (2) some difficulty: those who can make decisions about daily routines that are repeated daily, but have some difficulty making decisions when faced with new tasks or situations; (3) needs to be watched: those with a decreased ability to make decisions and needs cues or supervision to complete daily routines; and (4) unable to make decisions: those with little or no judgment, and poor ability to make decisions. ADL is classified into 8 levels, indicating a gradual decline in independence in daily life; (1, 2) independent; (3, 4) generally independent indoors; (5, 6) mainly in bed but able to maintain sitting posture; (7, 8) bedridden; Age was defined as under 65 years, from 65 to 95 years in increments of 5 years, and over 95 years (<65, 65‒69, 70‒74, 75‒79, 80‒84, 85‒90, 90‒94, and >94 years).
      Wilcoxon rank-sum test.
      χ2 test.
      2-sample t-test.

      The Disaster-NHA Association

      Figure 2, A shows the EFRs for disaster-affected and unaffected persons, while Table 2 shows the NHA HRs for disaster-affected persons. Of the 2156 disaster-affected and 185,705 unaffected individuals, 239 and 6184 were admitted to NHs postdisaster, respectively; disaster-affected individuals were three times more likely to enter NHs (adjusted HR 3.23; 95% CI 2.88‒3.64) after adjusting for covariates. Although the reasons are unknown, 588 disaster-affected and 14,854 unaffected individuals were lost to follow-up. Significant risks also included older age (90‒94 years; adjusted HR, 2.29; 95% CI 1.93‒2.70), cognitive impairment (cognitive function category 4; adjusted HR 1.40; 95% CI 1.25‒1.57), ADL impairment (ADL category 8; adjusted HR 2.27; 95% CI, 1.83‒2.70), and longer use of short-stay services (adjusted HR1.50 per 10 days; 95% CI 1.48‒1.52). Our sensitivity analysis with 2 months of NH use set as an outcome showed a significantly higher NHA rate for the disaster-affected group (adjusted HR 3.45; 95% CI 2.92‒4.07) (Supplementary Table S1).
      Figure thumbnail gr2
      Fig. 2Kaplan-Meier curves for event-free rate and HR of disaster-affected persons for institutionalization. Graphs show event-free rates among disaster-affected (vs unaffected) persons in (A) all individuals, (B) the group with no or mild dietary assistance, and (C) group with full dietary assistance.
      Table 2Crude and Adjusted HR of Risk Factors for NHA by Univariate and Multivariate Cox Regression Analysis
      Crude (95% CI)Age- and Sex-Adjusted (95% CI)Multivariable-Adjusted (95% CI)P Value for Multivariable-Adjusted Model
      Disaster status
       UnaffectedRefRefRef
       Disaster-affected3.75 (3.34‒4.21)3.79 (3.37‒4.26)3.23 (2.88‒3.64)<.001
      Age, categorical
       45‒640.70 (0.51‒0.95)0.76 (0.56‒1.04).09
       65‒69RefRef
       70‒741.14 (0.94‒1.39)1.10 (0.91‒1.34).33
       75‒791.46 (1.22‒1.74)1.35 (1.13‒1.61).001
       80‒841.82 (1.54‒2.16)1.60 (1.35‒1.89)<.001
       85‒902.49 (2.12‒2.94)2.02 (1.71‒2.39)<.001
       90‒943.07 (2.60‒3.62)2.29 (1.93‒2.70)<.001
       >943.55 (2.99‒4.22)2.27 (1.91‒2.70)<.001
      Sex
       WomenRefRef
       Men1.13 (1.08‒1.18)0.96 (0.92‒1.01).13
      Cognitive functions
       1RefRefRef
       22.07 (1.94‒2.20)1.88 (1.77‒2.00)1.29 (1.20‒1.40)<.001
       32.94 (2.76‒3.13)2.64 (2.48‒2.81)1.43 (1.31‒1.57)<.001
       43.08 (2.84‒3.33)2.74 (2.54‒2.98)1.40 (1.25‒1.57)<.001
      ADL
       1RefRefRef
       21.75 (1.59‒1.92)1.55 (1.40‒1.70)1.38 (1.25‒1.53)<.001
       32.64 (2.40‒2.91)2.27 (2.06‒2.50)1.83 (1.63‒2.06)<.001
       43.25 (2.97‒3.55)2.76 (2.52‒3.02)2.06 (1.84‒2.31)<.001
       54.11 (3.75‒4.50)3.45 (3.14‒3.78)2.32 (2.05‒2.62)<.001
       64.52 (4.05‒5.05)3.75 (3.36‒4.19)2.44 (2.13‒2.81)<.001
       74.07 (3.65‒4.54)3.49 (3.12‒3.89)2.26 (1.95‒2.61)<.001
       83.33 (2.75‒4.03)3.17 (2.62‒3.84)2.27 (1.83‒2.70)<.001
      Eating dependence
       Partial or unassistedRefRefRef
       Fully assisted1.28 (1.16‒1.41)1.29 (1.17‒1.42)0.93 (0.84‒1.04).23
      Number of days used per month (adjusted HR per 10 d)
      Days of each service were analyzed as time varying variable and adjusted HRs were calculated every 10 days each month.
       Home visit services0.86 (0.84‒0.89)0.88 (0.85‒0.90)0.91 (0.89‒0.93)<.001
       Day care0.98 (0.94‒1.03)0.97 (0.92‒1.07)0.86 (0.83‒0.89)<.001
       Short stay1.61 (1.59‒1.62)1.58 (1.57‒1.60)1.50 (1.48‒1.52)<.001
      The following personal characteristics were included in the analysis as covariates: age, sex, cognitive function assessed by the attending physician on a 4-point scale, ADL assessed on an 8-point scale, dietary intake, and the number of days of care services used per month for each individual. Age was defined as under 65 years, from 65 to 95 years in increments of 5 years, and over 95 years (<65, 65‒69, 70‒74, 75‒79, 80‒84, 85‒90, 90‒94, and > 94 years).
      Days of each service were analyzed as time varying variable and adjusted HRs were calculated every 10 days each month.

      Subgroup Analysis

      The disaster-affected (vs unaffected) group had a significantly higher risk of NHA in all subgroups (Figure 3). For the cognitive function and ADL categories, there were no significant interactions between disaster status and each subgroup category for NHA risk. However, we found a significant interaction effect between disaster status and eating dependence for NHA risk (P = .01 for interaction), with adjusted HRs of 6.0 (CI 3.68‒9.79) for the full assistance subgroup and 3.15 (CI 2.80‒3.56) for the partial or nonassistance subgroup (Figure 2, B and C); thus, the combination of disaster status and eating dependence created a higher risk than the simple addition of both. We also found interaction effects between disaster status and age for NHA risk (adjusted HRs for disaster-affected vs unaffected of 7.26, 5.63, 3.85, and 2.88 for ages 45‒64, 70‒74, 80‒84, and 90‒94 years, respectively; P = .01 for interaction); thus, the combination of disaster status and younger age was a larger risk factor than when separate.
      Figure thumbnail gr3
      Fig. 3HR of disaster-affected individuals for subgroups of each covariate. Each HR was adjusted for all other variables. A likelihood ratio test was conducted to investigate the interaction between the disaster-affected and unaffected groups.

      Discussion

      We found that individuals affected by the 2018 Japan Floods were 3.2 times more likely to be admitted to NHs than those who were unaffected, even after adjusting for other known risk factors. The disaster-based NHA risk was stronger than the risks imposed by older age, lower ADL, and lower cognitive function, and also significantly higher among individuals who were unable to feed themselves.
      Disasters also affect older persons as follows: (1) declined ADL due to the new onset or exacerbation of conditions (trauma, infection, and cerebrovascular disease
      • Dosa D.
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      Effects of Hurricane Katrina on nursing facility resident mortality, hospitalization, and functional decline.
      ); (2) increased physical stress, decreased mental and social activity, and social isolation, which may impair cognitive function and increase the demand for nursing care
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      The effect of the 2018 Japan Floods on cognitive decline among long-term care insurance users in Japan: a retrospective cohort study.
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      Longitudinal assessment of cognitive and psychosocial functioning after Hurricanes Katrina and Rita: exploring disaster impact on middle-aged, older, and oldest-old adults.
      ; (3) worsened access to care services and impaired daily activities such as eating, sleeping, and toileting because of fragmented infrastructure
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      ; and (4) altered living environment because of complete housing destruction and forced displacement.
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      • et al.
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      Moreover, research has shown that older age, female sex, lower ADL, worsening cognitive function, and increased short-stay use are associated with NHA in nondisaster situations.
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      Predictors of nursing home admission for persons with dementia.
      Our results support these findings.
      NHA entails deprivations that impact the quality of life, dignity, self-esteem, and mental condition. These social and mental damages further decrease cognitive functioning and increase mortality.
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      NHA is also a financial burden for admitted individuals, their families, and society. To help older persons live independently and avoid LTC facilities, we must identify which groups are at risk for admission and intervene at early stages.
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      Does early intervention reduce the number of elderly people with dementia admitted to institutions for long term care?.
      According to our primary and sensitivity analyses, disasters impose the highest known risk for NHA. Meanwhile, home-visit and day care services may lower this risk, which highlights the need for relevant care resources and local volunteering in disaster-affected areas, even at early stages.
      If disaster-affected persons cannot avoid NHA, then immediate admission should be allowed to maintain their quality of life. Even outside emergency situations, NHs located in disaster-prone areas should have spare beds and rooms to ensure capacity during sudden increases in admission requests. There should also be a method of transferring disaster-affected persons to NHs in unaffected areas with capacity. After NHA, disaster-affected persons should be supported in returning home, which may entail rigorous rehabilitation and a reconstruction of their living environment. However, Japan faces severe provisional shortages, with extensive waiting lists for NHA among older persons.
      • Morin D.
      • Leblanc N.
      Less money, less care: How nurses in long-term care allocate hours of needed care in a context of chronic shortage.
      • Akune T.
      • Muraki S.
      • Oka H.
      • et al.
      Incidence of certified need of care in the long-term care insurance system and its risk factors in the elderly of Japanese population-based cohorts: The ROAD study.
      • Stone R.
      • Harahan M.F.
      Improving the long-term care workforce serving older adults.
      The regular occupancy rates at LTC facilities are near 100%, while those for subacute-care facilities, geriatric hospitals, and skilled nursing facilities exceed 90%.
      Japan Association of Geriatric Health Services Facilities
      Report on research and studyproject on cooperation between long-term care health facilities for the elderly and family physicians.
      Even at subacute-care facilities, the monthly turnover rate is approximately 10%. Thus, the Japanese government should work toward a welfare state that is more resilient to disasters.
      Individuals in the disaster-affected group who originally required eating assistance were at a particularly high risk for NHA, with 30% admitted for a half-year postdisaster. Difficulty with independent eating often occurs under medical conditions such as cerebrovascular disease and impaired cognitive functioning.
      • Thomas D.R.
      • Ashmen W.
      • Morley J.E.
      • et al.
      Nutritional management in long-term care: development of a clinical guideline.
      ,
      • Munshi M.N.
      • Florez H.
      • Huang E.S.
      • et al.
      Management of diabetes in long-term care and skilled nursing facilities: a position statement of the American Diabetes Association.
      Individuals, thus, affected are heavily dependent on caregivers, but duties may have been interrupted by the disaster, thus, hindering independent living. Moreover, food insecurity may lead to malnutrition and increased frailty. Health care professionals should, therefore, identify individuals with eating disabilities to better prioritize the flow of care resources during disasters. Food delivery systems should also be established to serve the fragile.
      • Haumschild M.S.
      • Haumschild R.J.
      The importance of oral health in long-term care.
      Relatively younger (older) individuals were also at high risk of NHA. Although this population generally had a lower risk (EFR of 65‒69 years of age: 98.2% per 6 months) than other older persons, disabled older persons with housing damage likely needed to evacuate to NHs regardless of age. The gap in risks before and after the disaster may explain the high HR of the younger old individuals. Another potential reason is that the disaster revealed younger old people who were potentially at high risk of NHA. Health professionals should not only focus on apparent high-risk groups during disasters, but should also monitor the younger old persons.
      • Bell S.
      • Klasa K.
      • Iwashyna T.
      • et al.
      Long-term healthcare provider availability following large-scale hurricanes: A difference-in-differences study.
      • Radcliff T.
      • Horney J.
      • Dobalian A.
      • et al.
      Long-Term Care Planning, Preparedness, and Response Among Rural Long-Term Care Providers.
      • Pierce J.
      • Morley S.
      • West T.
      • et al.
      Improving long-term care facility disaster preparedness and response: A literature review.
      The 2019 World Health Organization Emergency and Disasters Risk Management Framework and Japan's Basic Act on Disaster Countermeasures emphasize that mere responses to disaster damages are insufficient; rather, comprehensive and continuous disaster countermeasures should be implemented during and after.
      World Health Organization
      A noncomprehensive approach because of poor intersectoral coordination or weak cooperation within the health system can increase health hazards during disaster responses. To reduce these gaps and optimize disaster preparedness, it is important to recognize vulnerable populations. This requires both an understanding of their vulnerability and evidence-based response methods. Although description of financial compensation for LTC users is available, there is no description of NHA risks during disasters; this is also lacking in some Western and other Asian countries.
      Office of long-term care ombudsman programs administration on aging administration for community living. Emergencyy preparedness and response: Model policies and procesures for state long-term care ombudsman programs.
      European Commission
      Building a European Health Union: Reinforcing the EU’s resilience for cross-border health threats.
      Association of South East Asian Nations
      ASEAN Disaster Management Reference Handbooks.
      A recent survey also found gaps between US states in the disaster preparedness requirements for government-funded organizations that provide home and community services.
      • Peterson L.J.
      • Rouse H.J.
      • Slater C.
      • et al.
      State policies concerning disaster preparedness for home-and community-based service providers.
      As older person often lack disaster knowledge and preparedness, it is important to provide disaster education prior to events, especially to prepare them for risks such as NHA and ensure prompt multidisciplinary intervention. Relevant guidelines and legislation should mention the potential for disaster-induced NHA demands alongside the risk factors that increase admissions.
      The main strength of this study was our use of data that included individual-level disaster-affected status. This is lacking in previous studies, which analyzed the risks for affected communities and facilities by comparing unaffected areas without individual data. Further, many studies arbitrarily selected affected population segments, which are not fully representative.
      • Igarashi Y.
      • Tagami T.
      • Hagiwara J.
      • et al.
      Long-term outcomes of patients evacuated from hospitals near the Fukushima Daiichi nuclear power plant after the Great East Japan Earthquake.
      ,
      • Bell S.A.
      • Abir M.
      • Choi H.
      • et al.
      All-cause hospital admissions among older adults after a natural disaster.
      We overcame this ecological fallacy and selection bias by analyzing a comprehensive and individual-level dataset compiled by the national government.
      There were also limitations. First, the dataset excluded some NHA risk factors (eg, cohabitating with family and comorbidity). Thus, we could not adjust the disaster-based NHA risk accordingly. Second, the database did not include healthy older persons. However, despite the long waiting list for LTC facilities, other institutional services and home-visit/day care services have limited waiting lists; with the exception of individuals who do not want LTCI, many eligible persons are certified and enrolled in the service.
      • Yong V.
      • Saito Y.
      National long-term care insurance policy in Japan a decade after implementation: some lessons for aging countries.
      Therefore, we included most of the population at-risk of NHA. Third, the dataset did not include specific reasons that individuals were lost to follow-up, which presumably occurred when individuals were hospitalized for long periods and did not use care services or undergo death. In this study, 588 (27.3%) disaster-affected and 14,854 (8.0%) unaffected individuals were censored from the database for unknown reasons before completing the study period. As our dataset did not list hospitalizations and deaths, we could not conduct such an analysis. However, as shown in our previous study, most NH users discontinue LTCI services because of hospitalization, death, or relocation; thus, the higher follow-up loss in the disaster-affected group may have created an underestimation of NHA risk.
      • Miyamori D.
      • Kamitani T.
      • Ogawa Y.
      • et al.
      Alcohol abuse as a potential risk factor of solitary death among people living alone: a cross-sectional study in Kyoto, Japan.
      Moreover, NHA risk may be affected by price elasticity. The disaster-affected group were exempt from out-of-pocket payments for care services (up to 44,000 yen per month), which may have facilitated admissions. However, most Japanese NHs have long waiting lists. Under such an over-demand, the exemption likely had a minimal impact. Lastly, the considered period was a half year, which prevents longitudinal analyses. The increase in admissions may have created a rebound decrease long after the disaster, known as a “harvesting effect,” which we did not evaluate.
      • Toulemon L.
      • Barbieri M.
      The mortality impact of the August 2003 heat wave in France: investigating the ‘harvesting’effect and other long-term consequences.
      ,
      • Yamanda S.
      • Hanagama M.
      • Kobayashi S.
      • et al.
      The impact of the 2011 Great East Japan Earthquake on hospitalisation for respiratory disease in a rapidly aging society: a retrospective descriptive and cross-sectional study at the disaster base hospital in Ishinomaki.

      Conclusions and Implications

      Under persistent global climate change, flooding is a critical risk factor for older persons, many of whom will experience NHA. This is the case in Japan and other welfare states.
      • Hussein S.
      • Manthorpe J.
      An international review of the long-term care workforce: policies and shortages.
      Many developing countries will also face this problem. Health professionals and policymakers across the world should recognize this emerging risk factor and prepare for sudden increases in the demand for in-facility care after flooding disasters.

      Appendix

      Supplementary Table S1Sensitivity Analysis Using Outcome of 2 Months Administration via Univariate and Multivariate Cox Regression Analysis
      Crude (95% CI)Age- and Sex-Adjusted (95% CI)Multivariable-Adjusted (95% CI)P Value for Multivariable-Adjusted Model
      Disaster status
       UnaffectedRefRefRef
       Disaster-affected4.02 (3.42‒4.75)4.06 (3.45‒4.80)3.45 (2.92‒4.07)<.001
      Age, categorical
       45‒640.80 (0.51‒1.26)0.86 (0.55‒1.36).09
       65‒69RefRef
       70‒741.41 (1.60‒1.88)1.34 (1.01‒1.80).333
       75‒791.61 (1.23‒2.10)1.47 (1.13‒1.92).001
       80‒842.10 (1.63‒2.71)1.82 (1.42‒2.09)<.001
       85‒902.61 (2.04‒3.36)2.09 (1.62‒2.68)<.001
       90‒943.16 (2.46‒4.06)2.32 (1.80‒2.99)<.001
       >943.57 (2.75‒4.64)2.28 (1.75‒2.97)<.001
      Sex
       WomenRefRef
       Men1.13 (1.06‒1.22)0.96 (0.92‒1.01).12
      Cognitive functions
       1RefRefRef
       22.17 (1.98‒2.37)2.00 (1.83‒2.19)1.38 (1.23‒1.55)<.001
       33.04 (2.77‒3.34)2.78 (2.53‒3.05)1.50 (1.31‒1.72)<.001
       42.9 (2.56‒3.27)2.63 (2.33‒2.97)1.43 (1.20‒1.70)<.001
      ADL
       1RefRefRef
       21.68 (1.46‒1.94)1.51 (1.31‒1.74)1.30 (1.12‒1.51)<.001
       32.64 (2.30‒3.05)2.32 (2.01‒2.67)1.72 (1.45‒2.04)<.001
       43.22 (2.82‒3.67)2.80 (2.45‒3.20)1.90 (1.60‒2.26)<.001
       54.13 (3.62‒4.72)3.56 (3.11‒4.07)2.20 (1.84‒2.64)<.001
       64.42 (3.76‒5.20)3.77 (3.20‒4.44)2.32 (1.89‒2.85)<.001
       73.63 (3.08‒4.28)3.17 (2.69‒3.75)2.07 (1.66‒2.57)<.001
       83.09 (2.31‒4.13)2.97 (2.22‒3.97)2.12 (1.54‒2.94)<.001
      Eating dependence
       Partial or unassistedRefRefRef
       Fully assisted0.94 (0.80‒1.11)0.95 (0.80‒1.12)0.69 (0.57‒0.83).23
      Number of days used per month (adjusted HR per 10 d)
       Home visit services0.91 (0.88‒0.94)0.92 (0.89‒0.95)0.96 (0.93‒0.99)<.001
       Day care0.98 (0.95‒1.03)0.97 (0.93‒1.01)0.95 (0.91‒1.00)<.001
       Short stay1.59 (1.56‒1.61)1.56 (1.54‒1.60)1.50 (1.48‒1.53)<.001
      The following personal characteristics were included in the analysis as covariates: age, sex, cognitive function assessed by the attending physician on a 4-point scale, ADL assessed on an 8-point scale, dietary intake, and the number of days of care services used per month for each individual. Age was defined as less than 65 years, from 65 to 95 years in increments of 5 years, and over 95 years.

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