Was exposure to Hurricane Irma associated with an increased risk of hospitalization and mortality among nursing home residents in the 30 and 90 days after the storm compared with a control group?In this cohort study of 61 564 nursing home residents exposed to Hurricane Irma and a control group of 61 813 nonexposed residents, the odds of a first hospitalization and mortality increased significantly at 30 and 90 days for those exposed. A long nursing home stay was associated with a greater risk for mortality compared with a short stay.Findings suggest that prioritizing heightened emergency preparedness in disaster situations for nursing home residents is warranted. Nursing home residents are at heightened risk for morbidity and mortality following an exposure to a disaster such as a hurricane or the COVID19 pandemic. Previous research has shown that nursing home resident mortality related to disasters is frequently underreported. There is a need to better understand the consequences of disasters on nursing home residents and to differentiate vulnerability based on patient characteristics.To evaluate mortality and morbidity associated with exposure to Hurricane Irma, a Category 4 storm that made landfall on September 10, 2017, in Cudjoe Key, Florida, among short-stay (<90-day residence) and long-stay (≥90-day residence) residents of nursing homes.Cohort study of Florida nursing home residents comparing residents exposed to Hurricane Irma in September 2017 to a control group of residents residing at the same nursing homes over the same time period in calendar year 2015. Data were analyzed from August 28, 2019, to July 22, 2020.Residents who experienced Hurricane Irma were considered exposed; those who did not were considered unexposed.Outcome variables included 30-day and 90-day mortality and first hospitalizations after the storm in both the short term and the long term.A total of 61 564 residents who were present in 640 Florida nursing home facilities on September 7, 2017, were identified. A comparison cohort of 61 813 residents was evaluated in 2015. Both cohorts were mostly female , mostly White , and approximately 40% of the residents in each group were over the age of 85 years. Compared with the control group in 2015, an additional 262 more nursing home deaths were identified at 30 days and 433 more deaths at 90 days. The odds of a first hospitalization for those exposed (vs nonexposed) were 1.09  within the first 30 days after the storm and 1.05  at 90 days; the odds of mortality were 1.12  at 30 days and 1.07  at 90 days. Among long-stay residents, the odds of mortality for those exposed to Hurricane Irma were 1.18  times those unexposed and the odds of hospitalization were 1.11  times those unexposed in the post 30-day period.The findings of this study suggest that nursing home residents are at considerable risk to the consequences of disasters. These risks may be underreported by state and federal agencies. Long-stay residents, those who have resided in a nursing home for 90 days or more, may be most vulnerable to the consequences of hurricane disasters. This cohort study assess the association between exposure to Hurricane Irma and 30- and 90-day mortality and morbidity among short- and long-term residents of nursing homes in Florida. Furthermore, the number of deaths in the nursing home setting significantly exceeded those reported by health care officials using death certificate records.3Nursing home preparedness in the face of all hazard emergencies is a vital area of importance, particularly given the demonstrated mortality within long-term care facilities associated with the coronavirus disease 2019 (COVID-19) pandemic. Recent data show that nearly one-quarter of the US deaths related to COVID-19 have occurred in the long-term care setting.5 Similar research in the nursing home setting is limited. Studies of residents exposed to Hurricane Gustav found that posthurricane hospitalizations increased for the most functionally impaired residents and that death increased for those with dementia.7Despite this data spotlighting the vulnerability of nursing home residents to disasters, little is known about which residents are more susceptible to harm associated with exposure to a hurricane or other disaster. Previous research in community-living older adults has found that hurricane exposure is associated with increased emergency department visits and hospitalization for those with diabetes or those receiving dialysis.9They are more likely than short-stay residents to have osteoporosis and to have been diagnosed with Alzheimer disease or other dementias and heart disease, although short-stay residents are more likely to have diabetes.10 Previous studies concerning nursing home resident hurricane exposure examined only long-stay residents.7There is a need to better understand the factors that increase residents’ vulnerability to morbidity and mortality in a disaster. Nursing home residents overall have a high level of morbidity. Nearly 50% have been diagnosed with Alzheimer disease or other dementias, 32% have diabetes, and nearly 40% have heart disease. However, most nursing homes serve 2 different populations, short-stay and long-stay residents. Residents admitted to a nursing home after a hospital stay for Medicare-covered skilled care or rehabilitation (up to a maximum of 89 days) are classified as short-stay. Short-stay residents may be discharged at the end of their stay, or they may remain at the nursing home as a long-stay resident. Long-stay residents, most covered by Medicaid, make up two-thirds of the nursing home population. Previous research has shown that long-stay residents are generally frailer than short-stay residents.11 Its immense size, measuring 400 miles across before landfall, ensured that the entire peninsula of Florida was affected.11 It was reported to have caused at least 1 direct and 33 indirect deaths in South Florida, including the subsequent heat-exposure deaths of 14 residents at the Rehabilitation Center at Hollywood Hills.11 Given previous research suggesting that the deaths occurring in the weeks and months after a hurricane exceed official reports,3 this study seeks to use Medicare claims data and nursing home administrative data to estimate the 30- and 90-day mortality and morbidity associated with Hurricane Irma among nursing home residents. Specific attention was paid to the association of Irma with morbidity and mortality among short-stay vs long-stay residents in the nursing home cohort in Florida. This study aims to identify the degree to which hurricane disasters are associated with outcomes among nursing home residents beyond what is initially reported; findings may help to improve preparedness efforts to protect those who may be most at risk of harm.Hurricane Irma, a large storm that struck the west coast of Florida in 2017, provides us with an opportunity to assess the role of patient characteristics not examined in previous research concerning the outcomes of exposed nursing home residents. Hurricane Irma made landfall in Cudjoe Key, Florida on September 10, 2017, as a Category 4 storm with winds in excess of 130 mph.12 Data are collected quarterly on all nursing home residents and are also collected after significant changes in health care status. The institutional review boards at Brown University and the University of South Florida approved this study and provided a waiver of informed consent for secondary data under 45 CFR 46.116.We used the Centers for Medicare and Medicaid Services Standard Analytical Files combined with the Minimum Data Set to create a cohort of data on nursing home residents residing in Florida facilities during calendar years 2015 and 2017. The Standard Analytical Files are part of the Limited Data Set and contain records on Medicare claims such as hospitalization, hospice status, and mortality. The Minimum Data Set 3.0 is a federally mandated assessment of nursing home residents’ personal health history with information on comorbidities, functional level, and cognitive status.6 Hurricane Irma made landfall in Florida on September 10, 2017, but its vast size affected the entirety of the state of Florida, and its predicted point of landfall remained unclear until just before the storm. Our exposure cohort was therefore defined as all Florida residents of nursing homes as of September 7, 2017. We excluded residents younger than age 65 years (n = 6341) as their health care outcomes are not captured with Medicare claims. We chose this date as 3 days before landfall to capture health care outcomes related to evacuation. Previous research suggests that nursing home administrators generally make decisions around 72 hours before the storm on whether to evacuate or shelter.13 Finally, we characterized residents as short-stay vs long-stay based on their duration of residence at the nursing home. Long-stay residents were defined as having resided at the facility for at least 90 days before to September 1. We used these same criteria when creating the comparison cohort. The comparison cohort contained all nursing home residents residing in Florida nursing homes as of September 7, 2015, excluding residents younger than age 65 years (n = 7196). We choose the year 2015 as a comparison year due to lack of similar activity in 2015 compared with Hurricane Matthew and Hurricane Hermine in 2016.We identified a study population of nursing home residents exposed to Hurricane Irma.3Our dependent variables included an assessment of mortality and first hospitalization. We used 30- and 90-day windows for identifying outcomes based on previous research that showed diminishing effects of storms after 180 days.P values using general estimating equations to cluster for persons who were in years 2015 and 2017 and by nursing home facility. General estimating equations uses a sandwich estimator that was specifically designed to handle and adjust for correlations within nested data. To reduce the strong bias toward type I error in this study, whereby P values are spuriously low due to extremely large population size, we used the 2-tailed P value of .01  as the threshold for statistical significance.We calculated descriptive statistics on study population demographic characteristics using the Minimum Data Set. We determined proportions and means for each cohort by long-stay and short-stay status of the resident and calculated respective We used the Standard Analytical Files to determine the 30- and 90-day total health outcomes of each cohort after the September 7 date for each respective year. We calculated the first hospitalization cumulative incidence and the mortality rate by dividing the total outcomes at 30- and 90-day by the total population on September 7 of the respective year. We adjusted the cumulative incidence and mortality rates for nursing home facility and calculated 95% CIs, displaying the final numbers per 1000 nursing home residents. We determined excess morbidity and mortality by calculating the risk difference for both first hospitalization and mortality between 2015 and 2017 and multiplying it by the total population in 2017. We also used general estimation equations to estimate the odds of mortality or first hospitalization in 2017 compared with 2015. We clustered the model by person ID, to account for persons who were in 2015 and 2017, and by facility ID. We have included a directed acyclic graph (DAG) as eFigure in the 14 were higher among long stay residents  and that activity of daily living scores were comparable. Both the average number of days in the nursing home before the storm and the number of acute first hospitalization in the month before the storm differed among short stay residents in 2015 and 2017 but not among long-stay residents.A total of 67 835 residents of nursing homes were identified in the exposure cohort in 2017. We excluded 6271 residents (9.2%) who were younger than 65. Our final exposure cohort included 61 564 nursing home residents who were present in 640 Florida nursing home facilities on September 7, 2017. For our comparison sample, we identified 68 921 residents as of September 1, 2015. We excluded 7108 (11.5%) residents based on similar exclusions for a final comparison cohort of 61 813 residents. P < .01) between 2015 and 2017. While our 95% CIs slightly widened, our point estimates stayed similar to the unadjusted model  for the entire population of Florida.3 Collectively, these results underscore the susceptibility of nursing home residents to destabilizing disasters such as hurricanes and the more recent COVID-19 crisis.16 These findings add context to the situation facing nursing home residents during the COVID-19 crisis. Nursing home residents have been shown to be significantly more at risk for mortality from COVID-19.16 Although hurricane disasters are geographically more localized than the COVID-19 pandemic, there are similarities in their disruption of usual nursing home care.This finding is consistent with previous research involving Hurricanes Katrina, Rita, Gustav, and Ike, which showed that the actual effects of mortality and morbidity post storm on the nursing home cohort was also significantly higher than originally recorded.17 Long-stay residents are also likely to be more at risk for the transfer trauma associated with evacuation and to poststorm heat exposure such as the conditions that occurred at the Rehabilitation Center at Hollywood Hills in Broward County post storm.19 It is noteworthy that Florida has recently instituted a emergency power rule to ensure that nursing homes have the capacity to provide an environment where temperatures do not go above 81 °F for at least 96 hours in the event of a power outage.20 Previous research on heat exposure and frail populations, including those exposed to historical heat waves in Chicago in 1995 and in Europe during 2003, show significant increases in mortality among those who are bedbound or frail.24 Future research exploring whether excess mortality can be attributed to specific patient characteristics, to the evacuation process itself, to delays in receiving appropriate health care services, or to post disaster consequences such as heat exposure among residents of facilities that lost power in the days following the storm is warranted.This study found that long-stay residents experienced disproportionately greater morbidity and mortality associated with the hurricane. Long-stay residents often have significant cognitive and functional impairments, making the care disruptions that are inevitable during storms more taxing to residents and staff.26 notable because of the association between nurse staffing levels and resident quality of care.28 Although the staffing-quality relationship is dependent on the staff type , the outcome of interest , or the methodologic rigor of the study ,29 direct-care staffing may be of greater importance specifically during hurricanes when frail residents may experience the storms’ physical and psychological force. Further study regarding the role of staffing in morbidity and mortality is warranted.An additional factor that may contribute to excess hospitalization or mortality during a hurricane is the availability and adequacy of direct-care nurse staffing. Qualitative studies suggest that obtaining adequate direct-care nurse staffing may be difficult during hurricanes due to shortages and absenteeism,30 In the nursing home setting, differential flu rates can account for changes in morbidity and mortality.31 According to the Florida Department of Health’s Florida Flu Review, however, rates of flu remained low during September of 2015 and 2017 with slightly more activity in 2015 than our exposure year. Nevertheless, these differences might be associated with other factors despite the storm.32 Finally, it is likely that not all of the mortality and morbidity increases were directly attributable to exposure to the storm. Certainly, hurricane disasters are significant for their considerable disruption to the public health infrastructure. Crowded hospitals, reduced access to care, and reductions in staffing at the nursing home due to mandatory public evacuations are likely to be key components of the differential morbidity, but those topics are beyond the scope of this study.There are limitations to this work. First, our data were based on a comparison between 2 years of data, specifically 2017 (exposure) and 2015 (control). We selected 2015 as our comparison year for its lack of hurricanes. Research has reported geographic differences to annual mortality and morbidity rates.This study provides the health outcomes of nursing home residents who experienced Hurricane Irma. There were considerable increases in mortality among the nursing home population. Moreover, although nursing home residents represent only a small portion of Florida’s population, our analysis shows substantially higher counts of death than were reported by the CDC for the entire state. Our finding that long-stay residents experienced greater mortality and morbidity associated with the storm is important for public health officials who must prioritize this frail population for heightened emergency preparedness in disaster situations.


The current study aims to evaluate the burden of disease in Singapore by estimating the quality-adjusted life years (QALYs) lost due to mental disorders and chronic physical conditions. The second Singapore Mental Health Study (SMHS-2016) was conducted in 2016 among 6126 respondents aged 18 years and above. The World Health Organization Composite International Diagnostic Interview version 3.0 (WHO-CIDI 3.0) and a modified version of the CIDI chronic medical disorders checklist were used to assess the 12-month diagnoses of mental and chronic physical disorders while the SF-6D scores derived from the 12-item Short Form Health Survey instrument was used to estimate the QALYs lost. The mean SF-6D score in this population was 0.87. The largest reduction in SF-6D scores among people with mental disorders was observed in Generalized Anxiety Disorder (GAD), followed by Major Depressive Disorder (MDD), alcohol abuse, bipolar disorder and Obsessive Compulsive Disorder (OCD) while the largest reduction in SF-6D score among people with chronic physical conditions was observed in ulcer, followed by lung disease, chronic pain and cardiovascular disease. At the population level, chronic pain was associated with the greatest QALY loss followed by MDD . Lung disease was associated with the smallest QALY loss (376). These findings highlight chronic pain, MDD, OCD, cardiovascular disease and GAD as the five leading contributors of QALYs lost in the general population which deserve prioritisation in public health prevention programmes. The Global Burden of Disease Study (GBD)2 has reported that the leading contributors of years lived with disability (YLDs) are related to pain - low back pain, headache disorders, mental - depressive disorders, and metabolic disorders-diabetes1. In parallel with the transformation of the healthcare model towards a holistic person-centred approach5 and transition of focus from infectious diseases to non-communicable diseases7, quality-adjusted life years (QALYs)8 has increasingly become a valuable tool to estimate the burden of disease in the general population11. A QALY is a summary measure that combines the length of survival of an individual and the health-related quality of life8 by placing a value on time spent in different health states12. The value is reflective of the preference weight that society gives for different health states based on their own health preference12. For example, a person with full health has a utility score value of 1 while a health state equivalent to being dead is given a value of 013.Between 1990 and 2017, the decline in mortality rates has been associated globally with increasing life span, and an ageing population which has translated into an increase in the magnitude of the non-fatal disease burden15. The QALY losses in the population can be calculated as a product of the marginal effect of each disorder, i.e., change in the health-related quality of life associated with a disorder multiplied with its prevalence in the general population16. The value is interpreted as the annual loss in QALYs resulting from the disorder, without considering mortality15. This method is commonly used to measure the burden of disease in the general population so that health services and initiatives can be planned to target the relevant chronic disorder that has a higher impact on the individual and the society.Several studies have been conducted to estimate QALY losses attributed to chronic medical conditions which includes both physical and psychiatric disorders17. As the population in Singapore is rapidly ageing with a growing chronic disease burden, data on current disease burden in terms of QALY losses due to chronic physical and mental disorders are important tools for monitoring the burden of these conditions on the population9. An epidemiological study conducted in 2010 - the Singapore Mental Health Study (hereafter referred to as SMHS-2010) had shown that chronic pain conditions, hypertension, and major depressive disorders (MDD) were the largest contributor to QALY losses in Singapore9. It was also found that the impact of the two mood disorders - MDD and bipolar disorder, as well as one of the anxiety disorders - obsessive compulsive disorder (OCD), examined in the study were associated with significantly larger QALY losses than the impact of any chronic physical condition at an individual level9. Recently, the second SMHS was carried out in 2016 (hereafter referred to as SMHS-2016)18. Although previous studies have investigated the impact of mental and physical disorders on QALY in the general population9, little is known about the change in the prevalence of these conditions over the years and its impact on QALYs. Hence, the current study aims to estimate QALYs lost due to mental disorders and chronic physical conditions in Singapore using the recent data from the second SMHS-2016.Singapore, a country in South-East Asia has a total population of about six million. The Chinese (74.4%) form the majority of the population, followed by Malays (13.4%), Indians (9.0%) and those from other ethnic groups (3.2%)18 - a nationally representative cross-sectional survey conducted among resident adults aged 18 years and above in Singapore. The study design and characteristics of the sample of this survey have been described in detail elsewhere18. In brief, the study applied a disproportionate stratified random sampling design. Over a period of 1-year, face-to-face interviews were conducted with the participants. The respondents received an inconvenience fee of $60 for their participation in the survey.Data were obtained from the SMHS-2016 survey19, a multidimensional health classification system assessing the six health domains of physical functioning, role limitation, social functioning, pain, mental health, and vitality, across 4–6 levels for each domain20. This instrument is based on 11 items from the 36-item Short Form Health Survey (SF-36)21 or 7 of the 12-item Short Form Health Survey (SF-12)19. This instrument has been widely used to generate utility values and is psychometrically sound in measuring health-related quality of life outcomes in both general and specific populations21. The SF-6D health state is defined by selecting 1 level from each domain, which results in a total of 18,000 possible health states21. The SF-6D scoring algorithm was developed using the standard gamble (SG) method from a sample of 249 SF-6D health states from a representative sample of the UK population21. Utility scores generated by the SF-6D range from 0.29 to 1.00, with 1.00 representing full health and 0.29 representing the worst possible health state defined by the SF-6D 21. The utility scores derived from English and Chinese versions of the SF-6D have been demonstrated to be equivalent in Singapore’s multi-ethnic general population22 and the instrument performs well in patients with mental illnesses23.We used the SF-1224. Only selected diagnostic modules for 12-month prevalence of mood disorders (major depressive disorder (MDD), dysthymia and bipolar disorder), anxiety disorders  and obsessive-compulsive disorder (OCD)), and alcohol abuse and dependence were included18. Diagnoses of mental disorders were based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria25. CIDI hierarchy rules were applied to all diagnoses18.Mental disorders were assessed using the World Health Organization Composite International Diagnostic Interview (WHO)-CIDI), a fully structured diagnostic interview to assess mental disorders and their treatment26. The question was read as ‘I’m going to read to you a list of health problems some people have. Has a doctor ever told you that you have any of the following…’ This was followed by a list of chronic physical disorders which were considered prevalent in Singapore’s population26. Eighteen individual disorders included in the current survey were re-classified into 11 types of common chronic physical conditions: (1) asthma, (2) diabetes, (3) hypertension and high blood pressure, (4) chronic pain , (5) cancer, (6) cardiovascular disease , (7) ulcer and chronic inflamed bowel disease , (8) thyroid disease, (9) neurological condition , (10) chronic lung disease (chronic bronchitis or emphysema (excluding asthma)), and, (11) hyperlipidaemia. Those who gave a positive answer to the list of chronic physical disorders were routed to the following question “Did you receive any treatment for it at any time during the past 12-month.” Those who answered positively to both the questions were then identified as having a chronic physical condition for the past 12-months in this study26.Information on chronic physical disorders was obtained using a modified version of the CIDI checklist of chronic medical disordersData on gender, age group , ethnicity , marital status , educational level , employment status  and average household income per month (below Singapore Dollar (SGD)2000, SGD2,000-SGD3,999, SGD4,000 - SGD5999, SGD6,000 - SGD9,999, SGD10,000 and above) in the past 12 months were collected.18. Descriptive analyses were performed to describe socio-demographic profiles and mean SF-6D score according to different levels of sociodemographic factors in the study population. Multiple linear regression analyses were used to estimate the impact of 12-month chronic physical conditions and mental disorders on SF-6D score after controlling for all chronic physical conditions, mental disorders, and sociodemographic variables including age group, gender, ethnicity, marital status, education, employment and income status. The reduction in SF-6D scores due to each 12-month mental disorder and 12-month chronic physical condition was defined as the difference in SF-6D scores between persons with and without such conditions based on marginal effects of multivariate linear regression model. The QALY loss in the population associated with each health condition was estimated by multiplying the marginal effects from regression model by the prevalence of the disorder. This is interpreted as the annual loss in QALYs resulting from the disorder, without considering mortality15. A separate analysis by subpopulation at different age groups were also performed. Standard errors (SE) and significance tests were estimated using the Taylor series linearization method. All statistically significant differences were evaluated at the 0.05 level using two-sided tests.All estimates were weighted to adjust for over sampling, non-response and post-stratified for age and ethnicity distributions between the survey sample and the Singapore resident populationThe authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration 1975, as revised in 2008. The study was approved by the National Healthcare Groups’ Domain Specific Review Board. Written informed consent was obtained from all participants and parents or legally acceptable representatives of those aged below 21 years.A total of 6126 adult Singapore residents aged 18 years and above were recruited, which yielded a response rate of 69.5%. A total of 6,113 respondents who completed the SF-12 were included in the current study. The sample comprised 50.5% female and 49.5% male respondents. Majority of the respondents were aged between 18 and 49 years (60%), of Chinese ethnicity (75.7%), employed (72%) and currently married (60%) , male gender , employed (vs. unemployed) and higher monthly household income (vs. less than SGD2000) were significantly associated with higher SF-6D scores  criterion. The CID can be defined as the smallest difference in the score that patients perceive as important that could lead a clinician to consider the change in the patient’s management28. In the current study, we adopted CID as additional information beyond the p - value in order to interpret the meaningfulness of the marginal effect of each disorder on utility scores from the patient’s perspective. Previous studies have reported that the CID values for SF-6D as 0.051 using an anchor-based method and the range as 0.01 to 0.48 using a distribution-based method29. It seems that different methods used can lead to different findings. Thus, if we use the anchor-based method to define CID, it seems that all mental disorders and only three chronic physical conditions can be considered as clinically important. However, if the lower range (0.01) of the distribution-based method is used, a number of physical conditions can be considered as achieving CID.This study has revealed that a number of mental and chronic physical conditions in Singapore were significantly associated with substantial QALY loss at the societal level and a significant decrease in health utility scores at an individual level. GAD had a greater impact on utility scores at the individual level as compared to other mental disorders and chronic physical conditions. It is possibly due to the fact that in our population, the proportion of those with GAD who reported ‘severe’ impact on functioning was highest among those with mental disorders, 7% of GAD cases were assessed to have had a severe disorder in the past one year using the Sheehan’s Disability Scale criteria9. The changes in the magnitude of the impact of GAD on utility scores between 2010 and 2016 are not easy to explain, because many factors may have played a significant role between these two time-points. It is possible that a significant increase in the lifetime  and 12-month  prevalence of GAD between SMHS 2010 and SMHS 201618 may be associated with a substantial impact of GAD on the person’s physical and mental health31. It is also possible that the changes are due to the different instruments employed in the two surveys as the utility scores were measured based on EQ-5D in the SMHS-2010.The finding that GAD had the highest impact on utility scores in the SMHS-2016 came as a surprise in light of the fact that GAD was associated with a smaller and insignificant reduction in health utility scores in the SMHS 20109. Higher prevalence of MDD in the general population32 with significantly impaired role functioning as well as increased days out of role could explain the significant impact of MDD on health utilities and QALYs32.MDD was the second largest contributor to the reduction in utility scores at an individual-level after GAD. It was also the second largest contributor to the loss of QALY at the population level after chronic pain and the leading cause of QALY loss among younger age groups. This finding is partly consistent with our previous study which reported MDD as the largest contributor to the reduction of HRQoL at individual-level in SMHS 20101.When we multiplied the marginal effects of each condition on utility scores with the prevalence, chronic pain was associated with the largest loss of QALYs in our population. Although the impact of GAD and MDD on utility scores was much higher than chronic pain at an individual level, chronic pain remained associated with the largest QALY loss at the societal level. This finding could be explained due to the higher prevalence of chronic pain in our population, which was almost 9 and 3 times higher than GAD and MDD, respectively. In this study, the prevalence of chronic pain was 7.6%, while the prevalence of GAD and MDD was 0.8% and 2.3%, respectively. To ensure comparability with previous studies, the current study defined chronic pain as those who experienced migraine, arthritis or rheumatism and back problems including “disc or spine problems”. The greater impact of chronic pain on QALYs at societal level was in line with recent epidemiological data that has shown that low back pain was the strongest contributor of non-fatal loss in terms of YLD globally18 and mental disorders represented the largest single contributor to the disease burden of disability-adjusted life years for Singaporeans between the ages of 10 and 34 years33. In Australia too mental disorders were found to be the leading cause of burden among those belonging to the younger age group followed by neonatal causes and unintentional injuries34. These evidence support the finding that the burden of mental disorders among those belonging to the younger age group is significant. Hence, initiatives to promote mental wellbeing and development of effective treatment strategies to improve young people’s mental health are needed.After controlling for sociodemographic factors, mental disorders and chronic physical conditions in multiple linear regression model, we found younger age, female gender, unemployment, and lower income were significantly associated with lower utility scores. Our findings are consistent with local data which found that the prevalence of mental disorders was higher among those of younger ageet al.35 also found younger age and unemployment to be significantly associated with lower quality of life among local psychiatric outpatients. Similar findings were also reported among patients with mental disorders36 as well as in population-based studies conducted in other countries39. However, findings on the relationship between age and quality of life between SMHS 2010 and SMHS 2016 were mixed. In the SMHS 2010, those belonging to the younger age-group were significantly associated with a higher quality of life while these findings were reversed in 2016. Those in the younger age group in the current cohort may have been more vulnerable to economic and psychological stresses as compared to the previous cohort; however this needs further research.In line with this, Sagayadevan et al. has shown that the chronic physical conditions checklist provides useful and accurate information about both treated and untreated chronic conditions40. Ye et al. have similarly reported that the self-reporting of chronic physical conditions provided information similar to that available from medical records41. Secondly, cross-sectional studies cannot ascertain causality, so longitudinal research is warranted. These limitations notwithstanding, this study was conducted on a nationally representative multi-ethnic population. The study had a good response rate, making the estimates highly generalizable to the multi-ethnic local population. All field interviewers were trained and stringent quality control was implemented throughout the study to ensure the data is reliable and valid. In all, 66 field interviewers were recruited for the study. All field interviewers underwent a structured training programme over a three-week period and were evaluated individually on all study- related procedures before being allowed to conduct the interviews in the field. The core research team members underwent training and were certified by the WMH-CIDI Training and Research Centre at the University of Michigan, USA. Stringent quality control measures were implemented throughout the study. For example, the number of completed interviews and time taken for each interview by each interviewer was closely tracked, on average two direct observation of each interviewer’s actual interviews at respondent’s household was conducted by core research team members, as well as 20% of completed interviews were randomly selected for verification via telephone calls/home visits to detect any falsification of the data.The study has some limitations that need to be acknowledged. Firstly, the study used self-report to assess chronic physical conditions, and administrative data were not used for confirmation. A study by Knight In conclusion, the current study provides important evidence of the reduction of quality-adjusted life years in people with mental disorders and chronic physical conditions in Singapore. These findings highlight chronic pain, MDD, OCD, cardiovascular disease and GAD as five leading contributors of QALYs lost in the general population, which deserve prioritisation in public health prevention programmes.


The aim of the study was to determine the effects of robotic-assisted gait training on ambulation and functional capacity in patients with spinal cord injury. In total, 88 patients were included and were randomly divided into two groups. The first group underwent 16 sessions of robotic therapy training for 8 weeks and conventional therapy for 5 days a week. The second group underwent conventional treatment. The Walking Index for Spinal Cord Injury II was used to evaluate functional ambulation, and the functional independence measure score was used to assess patients’ functional independence levels in a blind manner. A significant improvement was observed in both groups according to Walking Index for Spinal Cord Injury II and functional independence measure scores (P < 0.001). However, a significantly higher improvement according to the Walking Index for Spinal Cord Injury II (P = 0.011) and functional independence measure scores (P = 0.022) was seen in the robotic group than in the control group.Robotic-assisted gait training combined with conventional therapy was found to be superior to the conventional therapy in terms of gait function and level of disability. The biggest expectation and concern of patients with spinal cord injuries (SCIs) and their families after an acute period is the regeneration of the gait ability [1]. Therefore, the main strategy in SCI rehabilitation constitutes therapies for improving motor function [2]. The postinjury spinal cord has been reported to be useful for repetitive and relative functional training in terms of self-repair and sensory integration [3]. Studies have shown that repetitive and intensive applications can induce plasticity in the relevant motor centers. Sensory motor stimulation at a sufficient intensity is necessary to optimize neural plasticity. However, since patients are easily fatigued due to severe motor impairment, intensive and repetitive exercises are difficult to perform for a long period, making fatigue an important limiting factor for the conventional rehabilitation program. To overcome this limitation, automatic electromechanical devices have been developed. Robotic-assisted gait training (RAGT) provides many advantages, including maintaining a physiological gait pattern and increasing the intensity of training and the overall duration [4–8]. Robotic systems can be classified as fixed exercise robots for the upper and lower extremities, robots that can be worn on the body (robotic orthoses), auxiliary robots in daily life activities, and robotic walkers. Robots reported in clinical studies ranged from single-jointed simple systems to multimotion systems. Rehabilitation robots can be examined in two groups as end-effector  and exoskeleton devices  [9–10]. Lokomat has axes aligned with the patient’s anatomical axes and provides direct control of the joints. It reduces the likelihood of abnormal posture and movement. In a preprogrammed walking pattern, the device guides the patient’s legs. It consists of a combination of three joints that can be moved in two hips and one knee with a freely rotatable and two‑dimensional motion pelvis segment [11].In conventional rehabilitation, while conventional physical therapies, such as stretching, strengthening, and manual-assisted gait training, are practiced by physiotherapists, additional locomotor robotic devices increase the efficiency and performance of physiotherapists [12]. In patients with SCI, beneficial effects on rehabilitation outcomes from locomotor robotic devices cannot be convincingly demonstrated. While some studies show that RAGT is beneficial for improving gait speed, durability, and overall gait ability in patients with SCI, other studies suggest that there is no benefit [13–16]. Moreover, there is insufficient evidence to support the efficacy of RAGT on factors such as gait speed, gait distance, lower extremity motor score, spasticity level, and functional level of independence in patients with SCI[17].RAGT can facilitate intensive and repetitive exercises without fatigue. Therefore, we hypothesized that RAGT has positive effects on the recovery of ambulation and lowering of disability level in patients with SCI. The aim of this study was to determine the short-term effects of RAGT on the recovery of ambulation and lowering of disability level in these patients. The authors have no financial conflicts of interest.The study protocol was approved by Bakırköy Sadi Konuk Training and Research Hospital Ethics Committee (No. 2018/50). The study was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from each patient.The inclusion criteria were as follows:-American Spinal Injury Association Impairment Scale (AIS) patients with SCI with complete and incomplete levels A, B, C, and D-Patients aged 18–65 years-Patients whose injury occurred up to 6 months ago-Patients who could walk independently before the injuryThe exclusion criteria were as follows:-Patients who had previously received robotic therapy-Severe spasticity in the lower extremity, rigidity, and presence of contracture and fracture-Presence of severe osteoporosis-Lower extremity and pelvic pressure ulcers-Other neurological disorders that affect gait-Uncontrolled heart disorders, pregnancy, and severe cognitive and/or communicative disordersA total of 121 patients with complete and incomplete SCIs who were treated in the neurological rehabilitation clinic at our hospital were evaluated. Patients with a neurological level of ≥T6 and above were AIS-C and AIS-D. Patients with complete motor and sensory symptoms above the T6 level were excluded from the study due to autonomic problems. The study was completed with a total of 88 patients who met the inclusion criteria .Participants were randomized into 2 groups: the RAGT and control groups. The RAGT group underwent 16 sessions of robotic therapy training for 8 weeks twice a week and conventional therapy  for 5 days a week (twice a day). The control group underwent conventional treatment for 5 days a week (twice a day).Only one investigator was involved in the randomization process, which was performed through coin flipping. The groups were homogeneous in terms of demographic and clinical features .A computer-controlled exoskeleton system  was used for RAGT . The robotic gait orthosis has a force control computer that controls four motors and operates in real time and a feedback monitor that provides motivation and participation to the patient. In addition, the ergonomic structure that holds patients has grip bars, which can be easily adjusted with respect to height and width as well as during treatment. The system and the weight of the patient are taken at the desired amount, and this feature is a dynamic amount that is adjusted in each gait phase of the patient. At the beginning of the gait training, one-half of the body weight was supported, and thereafter the support was reduced. At the end of the rehabilitation program, gait training was completed with full body weight. Patients were gait trained for 30 min in each session. RAGT was performed by a trained physiotherapist.The patients’ motor impairment was determined using the neurological level and AIS score [18]. The functional ambulation level was evaluated using the Walking Index SCI II (WISCI-II) score, which is a 20-point scale (0 points: no ambulation to 20 points: independent ambulation) [19]. The functional independence levels of the patients were evaluated using the functional independence measure (FIM) score. The FIM includes 13 motor and five social–cognitive measures. The headings include self-care, sphincter care, transfer, locomotion, communication, social interaction, and cognitive activity. A 7-point scale was used to score total independence, wherein 1 indicates complete dependency and 7 indicates full independence. The intermediate levels are 6: modified independent, 5: under supervision, 4: minimal help or >75% of effort consumption, 3: medium-level help or 50%–75% effort consumption, and 2: maximal help or 25%–49% effort consumption [20]. All patients were evaluated by a blind researcher (Kadriye Öneş) at the beginning and end of treatment (single-blind study). The healing rate according to the FIM scale was standardized according to the total FIM score and calculated using the following formula:Similarly, the healing rate according to the WISCI-II scale was standardized according to the total WISCI-II score and calculated using the following formula:The normal distribution suitability of the variables was tested using the Kolmogorov–Smirnov test with Lilliefors Significance Correction. Quantitative data were represented as median and interquartile range and categorical data as frequency. The distribution of categorical variables to groups was compared using the Pearson chi-square and likelihood ratio chi-square tests. The two groups were compared using the Mann–Whitney U test. The pre- and postrehabilitation averages were compared using the Wilcoxon test for both groups. The SPSS version 18.0 for Windows was used in the analysis. Post hoc power analysis was performed using G*Power version 3.1.9.2 .Demographic data, etiology, AIS score, and motor level are shown in Table 1. There was no statistically significant (P ˃ 0.05) difference between the RAGT and control groups in terms of age; sex; etiology; and percentage of traumatic, nontraumatic, and incomplete patients with spinal cord injury. The FIM score for the robotic group at entry and after treatment was 69 and 85, respectively, whereas for the control group, it was 67 and 77, respectively.At the end of rehabilitation, a significant improvement was observed in both groups according to the WISCI-II and FIM scores (P < 0.001). The improvement according to the WISCI-II score was significantly higher in the robotic group (5.0%) than in the control group . Moreover, the improvement according to the FIM score was significantly higher in the robotic group (4.0%) than in the control group .The primary purpose of the study was to examine the effect of robotic rehabilitation on the level of ambulation assessed using the WISCI-II scale. As per the WISCI-II scale, the post hoc power was calculated as 93.1% based on the recovery data: effect size: 0.76, RAGT group mean (standard deviation): 16.9 (20.5), n = 44; control group mean (standard deviation): 5.0 (8.3), n =4 4; and alpha error level: 0.05, bidirectional judgment.In our study, robotic treatment in patients with SCIs showed a significant improvement in ambulation and functional status. A significant improvement was also observed in the group receiving conventional therapy, but when compared with the group receiving conventional exercise therapy, the recovery in the WISCI-II and FIM values in the RAGT group was found to be more significant. Due to a limited number of randomized controlled trials evaluating the ambulation and functional independence in robot therapy, the effects of robot therapy on walking and functional status in patients with SCI are unclear. Additionally, there is no consensus on the time of treatment initiation and the optimal duration of treatment.Our study was conducted on complete and incomplete SCI cases in the subacute phase of neurological recovery and a control group with similar age, sex, duration of injury, and lesion level. In a similar study involving complete and incomplete patients, 28 patients were treated with robotic‑assisted treatment for 1 h, 2–3 times per week, but the increase in Functional Ambulation Classification and WISCI scores was not significant compared with that of the conventional group; a significant increase in the Spinal Cord Independence Measure scores was observed [15]. The recovery of motor damages often occurs within the first 2 months after injury and continues in the 3–6-month period. In this period, neuroplasticity continues and is rapid [21]. Our patients were in the first 6 months of motor healing. A total of 16 sessions of RAGT were given to the patients twice a week for 8 weeks. In a systematic review, Morawietz et al. evaluated studies of the acute subacute and chronic periods, in the range of 2–5 sessions per week and of 4–13 weeks in duration; they showed potentials for ambulatory function improvement without superiority compared to another aspect [22]. In two studies using only incomplete patients undergoing RAGT and using parameters similar to the present study, the increase in FIM and WISCI-II scores was found to be more significant compared to the conventional group .The limited number of patients, presence of complete and incomplete groups, presence of paraplegic and tetraplegic patients, examination of only early period results, and evaluation with few parameters are the limitations of our study.In conclusion, we believe that it is beneficial to apply robotic gait training of our study data with other adequate rehabilitation methods and that RAGT is not an alternative but an adjunctive to the conventional therapy. There is still a need for more controlled studies with a larger sample size to assess the different effects of robotic locomotor therapy on different SCI patient populations and to determine the appropriate protocol.


Modulation of cortical beta rhythm (15–30 Hz) is present during preparation for and execution of voluntary movements as well as during somatosensory stimulation. A rebound in beta synchronization is observed after the end of voluntary movements as well as after somatosensory stimulation and is believed to describe the return to baseline of sensorimotor networks. However, the contribution of efferent and afferent signals to the beta rebound remains poorly understood. Here, we applied electrical median nerve stimulation (MNS) to the right side followed by transcranial magnetic stimulation (TMS) on the left primary motor cortex after either 15 or 25 ms. Because the afferent volley reaches the somatosensory cortex after about 20 ms, TMS on the motor cortex was either anticipating or following the cortical arrival of the peripheral stimulus. We show modulations in different beta sub-bands and in both hemispheres, following a pattern of greater resynchronization when motor signals are paired with a peripheral one. The beta rebound in the left hemisphere (stimulated) is modulated in its lower frequency range when TMS precedes the cortical arrival of the afferent volley. In the right hemisphere (unstimulated), instead, the increase is limited to higher beta frequencies when TMS is delivered after the arrival of the afferent signal. In general, we demonstrate that the temporal integration of afferent and efferent signals plays a key role in the genesis of the beta rebound and that these signals may be carried in parallel by different beta sub-bands. In the beta range, the ERD is observed immediately before movement onset and is sustained throughout the movement  and electrical MNS at the wrist. In the two main experimental conditions, the MNS was followed by TMS with either 15-ms (TMS15) or 25-ms (TMS25) delays. Two other control conditions are MNS alone (MNS_A) and TMS alone (TMS_A). The dependent variable was the beta rebound of the electroencephalographic (EEG) recording . The twoComitato Etico Unico della Provincia di Ferrara  and was conducted in accordance with the ethical standards of the latest updated version of the 1964 Declaration of Helsinki.Ten subjects  participated in the experiment. All subjects were right-handed as assessed by the Edinburgh Handedness Inventory : 15 ms (TMS15) or 25 ms (TMS25). The other two conditions were used as controls: TMS_A or MNS_A . The expSubjects’ right median nerves were stimulated by means of a constant-current stimulator  delivering monophasic square waves of 100 μs to the volar aspect of the wrist  in agreeopponens pollicis (OP) at the lowest possible intensity. The OSP was marked on a cap, and coil position was fixed by a mechanical support (Fisso Swiss Made) and continuously monitored by the experimenter. The resting motor threshold (rMT) was assessed by using standard protocols  of the maximal stimulator output.A 70-mm figure-eight coil connected to a Magstim monophasic stimulator  was placed over the left primary motor cortex with the handle pointing backward at 45° from the midline. The optimum scalp position (OSP) was found by moving the coil in 0.5-cm steps around the left primary motor cortex hand area in order to produce maximum-amplitude motor evoked potentials (MEPs) in the right MEPs were recorded with a wireless EMG system  from the right OP by using a standard tendon-belly montage with Ag/AgCl electrodes. EMG traces were band-pass filtered , digitized (2 kHz), acquired by a CED Power1401 board , visualized, and stored on a PC for off-line analysis by means of Signal 3.09 software .EEG was recorded using a wireless EEG sensor cap  with eight EEG channels mounted according to the International 10-20 system. During acquisition, the ground and reference channel was positioned at the ear clip. EEG data were sampled at a frequency of 500 Hz (24 bit) and were acquired in continuous mode using dedicated software  and exported for further analysis in the EEGLAB toolbox  or with an outlier (2 SD) pre-TMS EMG activity . MEP amplitude values were measured as peak-to-peak amplitude (in mV). The Shapiro–Wilk test was applied to test the normality of the data. Given the nonnormal distribution, we performed nonparametric statistics. To evaluate whether MEPs differed between the three protocols, we ran a two-tailed group-level permutation test . We discarded from the analysis all trials  with no visible MEP , middle (20–25 Hz), and high (25–30 Hz) beta bands, we examined the power spectra in these three bands of interest for the same epoch. Because the time–frequency energy values are likely nonnormally distributed, the differences in beta-band power, frequency, and latency between conditions were compared using FDR-corrected nonparametric paired samples permutation t-tests , the cycles were nine. Inferential statistics have been conducted on C3 and C4 electrodes, which have already shown to be of interest when investigating the beta rebound . MEP amplitudes when TMS was delivered 15 ms after MNS instead showed no significant difference with respect to TMS_A  were significantly larger compared to those obtained when TMS was delivered 25 ms after MNS  and TMS25  for both electrodes C3 and C4 in all conditions.In both C3 and C4, the beta rebound (15–30 Hz) increased significantly in TMS15 , TMS25  and MNS_A  were significantly larger than in TMS_A . In the middle beta band, power in TMS15 (1.76 ± 0.54 dB) was larger than in TMS25  and TMS_A . No differences were found in the high beta band , the power spectra were significantly modulated by conditions on the low . In the high beta band, power in TMS25 (0.64 ± 0.25 dB) was larger than in TMS . SD values for both electrodes (C3 and C4) for the three beta bands recorded in all conditions.In C4 , no differences were found in the low beta band . The nature of the beta rebound is not fully understood, but it is believed to reflect the balance of inhibition in motor networks  in healthy participants. We applied TMS to the hand motor cortex and an electrical stimulation to the contralateral median nerve (MNS). When MNS precedes the TMS by 25 ms, the ascending signal reaches its cortical targets before the stimulation of the motor cortex. In this case, somatosensory to motor conditioning is also visible in MEP size as a reduction of corticospinal excitability  did not show any modulation to the four stimulation protocols. In the lower range (15–20 Hz), the rebound was significantly reduced in the TMS_A condition. This effect suggests that an important drive in generating the low-beta rebound is provided by the afferent signal regardless of its integration with the efferent one. In the middle range (20–25 Hz), the rebound was modulated by the delay in the paired MNS + TMS stimulations. Specifically, the rebound increased when cortical processing of the afferent signal was preceded by a stimulation to the motor cortex (TMS15). This result suggests that, in the mid-beta range, it is possible to observe state-dependency effects such that processing of the afferent volley is modulated by the preconditioning of the motor cortex. Beta rebound modulation is also induced in the right hemisphere, probably mediated by transcallosal connectivity between homolog areas. As for the motor system in which the transcallosal segment connects the motor areas  and the effect of afference on efferent signal generation . Considering that our results were obtained at rest, our work shows what would be the functional relevance of having different sensorimotor integration timing across different beta sub-bands and across hemispheres during movement preparation and control.In this study, we show that the temporal coordination of afferent and efferent signals plays a key role in the genesis of the beta rebound. Our data suggests that motor and somatosensory areas communicate via the thalamus) and indirectly via S1  abolishes the SAI (Guerra et al., However, the exact mechanism by which sensorimotor signals are integrated in time and how this is reflected in beta oscillations in different sub-bands is far from being understood. By devising a novel and relatively simple paradigm, the present study intends to provide a new tool that may be effective in clinical populations. In fact, several neurological conditions have shown altered patterns of rhythmic beta activities. For instance, multiple sclerosis patients show abnormal beta rebound (Barratt et al., The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by Comitato Etico Unico della Provincia di Ferrara. The patients/participants provided their written informed consent to participate in this study.PC, PH, LF and AD’A had the idea and designed the experiments. PC, PH and AD’A prepared the experimental setup and collected the data. PC, ED and PH analyzed the data. All authors participated in interpretation of data and helped draft the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Acute encephalopathy with reduced subcortical diffusion (AED), characterised by seizure onset and widespread reduced apparent diffusion coefficient in the cortex/subcortical white matter, is one of the most common acute encephalopathies in children in East Asia. This 14-year single-centre retrospective study on 34 patients with AED showed that therapeutic hypothermia was used for patients with more severe consciousness disturbance after the first seizure or second phase initiation, extrapolating from neonatal hypoxic encephalopathy and adult post-cardiac arrest syndrome. The basal ganglia/thalamus lesions and the Tada score were the poor outcome determinants in the multivariate analysis. The correlation between the worse outcomes and the duration from the first seizure to the initiation of therapeutic hypothermia was observed only in the patients with AED cooled before the second phase. This correlation was not observed in the overall AED population. There was a moderate negative association between the worse outcomes and the duration between the first seizure and the second phase. Therefore, the basal ganglia/thalamus lesions and the Tada score were the outcome determinants for patients with AED. Further investigation is required to examine the efficacy of therapeutic hypothermia in this population while considering the timing of the therapeutic hypothermia initiation and the second phase. The first seizure is mostly associated with a febrile infection. Reduced ADC in diffuse white matter lesions, primarily affecting the subcortical white matter, is referred to as bright tree appearance on diffusion weighted images (DWI) and appears within 9 days of the first seizure. Usually, no abnormal MRI findings are observed within a few days from the first seizure2. Although the underlying mechanisms remain to be fully elucidated, an association with delayed neuronal cell death triggered by extracellular glutamate stimulation has been proposed, as the underlying cause for the first seizure3. AED encompasses a spectrum and includes the acute encephalopathy with biphasic seizures and late reduced diffusion, which was originally proposed by Takanashi, and only includes patients with intractable and repetitive seizures, and deteriorating consciousness levels (second phase) after a transient improvement in consciousness post febrile status epilepticus (first phase)4. The outcomes of patients with AED are mild than in patients with other acute encephalopathy types, such as acute necrotising encephalopathy, haemorrhagic shock, and encephalopathy syndrome6. Nevertheless, the majority of the affected children present permanent neurological deficits5.Acute encephalopathy with reduced subcortical diffusion (AED) is one of the most common childhood acute encephalopathies in East Asia. AED was defined by Hayashi as an acute encephalopathy type presenting with (1) seizure onset and (2) widespread reduced apparent diffusion coefficient (ADC) on brain magnetic resonance imaging (MRI), in the cortex and/or subcortical white matter involving unilateral or bilateral hemispheres7. Patients with AED without an injury around the perirolandic regions, which has been referred to as central sparing in previous reports, represent a relatively mild phenotype of acute encephalopathy2. The presence of basal ganglia lesions is associated with severe outcomes7.Diffuse lesions with an injury around the perirolandic regions, prolonged seizures at onset, coma, or involuntary movements have been associated with poor outcomes in AED according to previous retrospective studies11. However, there is still a paucity of evidence-based treatments for this disease, including therapeutic hypothermia, and the 2016 Japanese practice guideline for acute encephalopathy in childhood12 did not recommend any treatment other than conventional respiratory, haemodynamic management, and seizure control.Therapeutic hypothermia has been used for managing brain oedema in paediatric patients with brain injuries, in several Japanese tertiary centres including our facility, including post-cardiac arrest syndrome and acute encephalopathy associated with febrile infection, which has been extrapolated from neonatal hypoxic encephalopathy and adult post-cardiac arrest syndromeTo examine the determinants of disease outcomes in patients with AED, we performed a retrospective study on 34 such patients admitted to our facility over a period of 14 years. Therapeutic hypothermia was performed for those who were in a more severe condition, following our institutional protocol for managing patients with consciousness disturbance.We identified 34 patients with AED , after excluding two patients with a neurodevelopmental delay before onset, from the total 36 patients admitted to our facility from February 2004 and August 2018. The febrile seizure was the first neurological symptom in all patients with AED. Eight patients who underwent therapeutic hypothermia by the attending physicians before the second phase, following our institutional protocol for managing patients with consciousness disturbance (Supplementary Data) were included in the Early-Hypo group. Sixteen patients who started therapeutic hypothermia after the initiation of the second phase were included in the Late-Hypo group Fig. . The worThe cerebral spinal fluid (CSF) was examined in 28 out of 34 patients with AED  who received therapeutic hypothermia had abnormal MRI findings and that there were no patients without abnormal findings on MRI after therapeutic hypothermia . Time1st-2nd among patients with the second phase in the unfavourable group was significantly shorter than that in the favourable group     or 34 °C  in the Late-Hypo group.A strong association was found between the worse outcomes and the Time1st-2nd . There was a significant association between the Time1st-2nd and the Time1st-cooling in the Late-Hypo group  and 83.75 h  in the hippocampus23. Connexin 43 forms the major component protein in astrocytic gap junction and is thought to participate in the pathophysiological processes of brain damage24. Glutamate is a major excitatory neurotransmitter in the central nervous system; however, high concentration of glutamate in the extracellular space caused by status epilepticus, hypoxic-ischaemic insult, or traumatic brain injury is neurotoxic. GLT-1, predominantly expressed in astrocytes, provides the majority of glutamate clearance capacity by transporting it into the intracellular space25. The pathological findings of a patient with acute encephalopathy with biphasic seizures and late reduced diffusion at 6 days after the second phase onset, revealed a reduction of the myelinated axons and an increased number of gemistocytic astrocytes at the corticomedullary junction, where bright tree appearance was observed26. Axonal degeneration is postulated to stem from an increased influx of Ca2+ into the axons leading to impaired axonal mitochondrial metabolism, glutamate-mediated oligodendrocyte damage, decreased production of N-acetylaspartate by axonal mitochondria, and glutamate excitotoxicity. The astrocytic gap junctions and glutamate uptake in astrocytes are speculated to be already disrupted to some point in these gemistocytic astrocytes. This might explain the association between the earlier therapeutic hypothermia initiation and the better outcomes only in the Early-Hypo group.The strong association between worse outcomes and the longer Time28. Glutamate release from astrocytes to the adjacent neurons triggered by the Ca2+ release from the astrocytes’ intracellular stores following extracellular glutamate stimulation was demonstrated29. The effect of delayed and prolonged therapeutic hypothermia may oppositely contribute to releasing glutamate from the astrocytes through upregulation of GLT-130. This may explain why the relationship between the earlier therapeutic hypothermia initiation and the better outcomes was not observed in the Late-Hypo group in this study. Therapeutic hypothermia after the second phase may not be as effective compared to that initiated before the second phase. The role of astrocytes should be further clarified in the time course in patients with AED to maximise the benefit of the available treatment options, including therapeutic hypothermia as the astrocytes are known to have dual functions of glutamate uptake and release31. Diagnosis of AED before the second phase is challenging because of the absence of abnormal neuroradiological, characteristic laboratory, or clinical findings. The Yokochi score is another AED prediction score, similar to the Tada score, that was proposed to recognise this disease before the second phase; however, its positive predictive value was 47% and the initiation of therapeutic hypothermia according to this score has yet to be justified32. Thus, in the most AED cases in other facilities, therapeutic hypothermia could be initiated after the second phase, similar to our institution. Considering that the Japanese guideline for acute encephalopathy in childhood do not recommend therapeutic hypothermia for patients with acute encephalopathy12, the relationship between early therapeutic hypothermia and superior outcomes in the Early-Hypo group may justify future investigations on whether early induction of therapeutic hypothermia in patients after a febrile seizure is effective with a more refined AED prediction score.In contrast, delayed and prolonged therapeutic hypothermia are reported to be associated with white matter apoptosis in preclinical reportsThe initiation of therapeutic hypothermia inevitably implies the initiation of adequate sedation, necessitating the continuous infusion of midazolam or thiamylal sodium. Thus, we cannot exclude the possibility that early initiation of these medications suppressed the second phase and played a role in alleviating outcomes instead of the direct effects of therapeutic hypothermia for brain damage. However, this does not explain the time-dependent effects of therapeutic hypothermia in the Early-Hypo group, as all patients in this group had equally suppressed seizures in the second phase.1st-2nd compared to those with favourable outcomes in the univariate analysis. This result coincided with that of a preclinical study on newborn piglets reporting that more severe hypoxia-ischaemia insults were associated with a shorter latent-phase, worse secondary energy failure, and more cortical-grey-matter neuronal death33. This association between the Time1st-2nd and the outcomes may also explain why the relationship between the earlier therapeutic hypothermia initiation and the better outcomes was not observed in the Late-Hypo group in this study. Previous clinical studies with AED7 did not examine this relationship between the outcomes and the Time1st-2nd in patients with AED. We were unable to clarify which factors were attributed to the early arrival of the second phase in patients with AED as no variables except the presence of therapeutic hypothermia treatment, catecholamine use, or thrombocytopenia correlated with the Time1st-2nd.The patients with unfavourable outcomes had a shorter Time7. Thus, we did not examine episodes of involuntary movement. Prospective studies with more patients are required to examine the association of various clinical symptoms and features with outcomes. Third, we did not routinely check for the anti-N-methyl-D-aspartate receptor (NMDA) and the anti-myelin oligodendrocyte glycoprotein (MOG) antibodies, and we did not conduct a polymerase chain reaction (PCR) test for microorganisms in the CSF in each patient. Therefore, we were unable to examine the possible contribution of these mechanisms to AED and the outcome of patients with AED.This study was a retrospective observational study with a small number of patients with AED in a single centre. One limitation was that this study did not intend to evaluate the efficacy of therapeutic hypothermia, as the decision to whether to cool patients or not was based on the attending physicians’ judgement when the patients had a GCS score > 8. All patients with AED that received therapeutic hypothermia were cooled to 34 °C for 72–84 h according to the protocol in our facility, occluding us from examining the difference in efficacy between the temperature management at 34°C or 36 °C. Future prospective randomised trials are warranted to directly compare the neurodevelopment scores, such as the Wechsler Preschool and the Primary Scale of Intelligence-fourth edition or the Wechsler Intelligence Scale for Children (WISC) -fifth edition, which are considered to be more sensitive than the PCPC scale when examining the difference in efficacy between the temperature management at 34 °C or 36 °C in patients with AED. Second, all data were retrospectively collected from medical records and it is unlikely that every involuntary movement before the second phase was recorded for each patient, which has been reported to be associated with poor outcomesThis study revealed that the presence of basal ganglia/thalamus lesions on the MRI and the Tada score were the determinants of poor outcomes in patients with AED. Further investigation is needed to examine the efficacy of therapeutic hypothermia in this population while considering the timing of the therapeutic hypothermia initiation and the second phase.34, posterior reversible encephalopathy syndrome, and hepatic encephalopathy, we had a total of 50 children aged between 1 month and 14 years, who developed or were suspected of having acute encephalopathy. They were admitted in our hospital between February 2004 and August 2018. Especially, 37 patients received therapeutic hypothermia of whom 26 were diagnosed as having AED. Thirteen patients were not provided therapeutic hypothermia, of whom 10 were diagnosed as having AED , multiple sclerosis (MS), autoimmune encephalitis, traumatic encephalopathy, mild encephalitis/encephalopathy with a reversible splenial lesion21. Nineteen patients were included in the previous study for prediction of acute encephalopathy with biphasic seizures and late reduced diffusion in patients with febrile status epilepticus32. This study was approved by the institutional review board of St Mary’s Hospital, Fukuoka, Japan (IRB number: 18–1110). All methods were performed following the relevant guidelines and regulations. Informed consent was obtained from all participants and/or their legal guardians.We defined the first seizure of the second phase as its initiation, as the initiation of consciousness disturbances is typically challenging to be detected, especially in young children and during night, even though consciousness disturbances are often accompanied by precede repetitive seizures. Additionally, the timing of seizures was recorded precisely in all patients compared to the time of the initiation of consciousness disturbances, providing more accurate and objective data considering the nature of this retrospective study. All patients visited our or referring hospitals immediately after the first seizure, apart from one patient . According to the protocol, therapeutic hypothermia should be indicated for patients with continuous consciousness disturbance and haemodynamically stable condition. The contraindication is patients with head trauma or severe coagulation disorders. When a patient continuously had a GCS score <9, therapeutic hypothermia was applied. When a patient had a GCS score> 8, the decision to apply therapeutic hypothermia or not was taken according to the attending physicians’ judgement on the severity of neurological symptoms for that patient. All patients with therapeutic hypothermia were intubated, mechanically ventilated, and cooled with mattresses with temperature-adjustable water circulating over the ventral and dorsal trunks following our institute’s protocol. The core temperature was monitored using more than two of the oesophageal, nasopharyngeal, rectal, or urinary bladder temperature probes. The target temperature was 34 °C and the cooling duration was 72 h. During cooling, continuous infusion of thiamylal sodium (up to 3 mg/kg/h) or midazolam (up to 0.6 mg/kg/h) and fentanyl (1–3 μg/kg/h) were administered. Continuous midazolam infusion was administered in two patients ; days when the first MR image was taken from the first seizure; days when reduced subcortical ADC confirmed on the MRI from the first seizure; presence of diffuse lesions with injury around the perirolandic regions on the MRI at least in a hemisphere; presence of basal ganglia/thalamus lesions on MRI; potential serum markers of tissue damage such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase after the first seizure; blood urea nitrogen, Crea, Glu, platelet count after the first seizure; cell count and protein level in the CSF and the timing of the CSF examination; the Tada and the Yokochi scores after the first seizure33; preceding infections; additional treatments, such as intravenous methylprednisolone pulse (30 mg/kg/dose × 3 days) therapy, or immunoglobulin (1 g/kg/dose); the presence and the dosage of dexmedetomidine use during therapeutic hypothermia, and outcomes. The Tada score is a predicting score for acute encephalopathy with biphasic seizures and late reduced diffusion among patients with febrile seizure in the acute phase and consisted of consciousness level, duration of convulsions, enforcement of mechanical intubation, AST, Glu, and Crea13. The Yokochi score is another similar predicting score for acute encephalopathy with biphasic seizures and late reduced diffusion33. Events of hypotension  regardless of the catecholamine use, catecholamine use requirement, pneumonia, thrombocytopenia , coagulation disorders , arrhythmias (excluding bradycardia), and hypokalaemia (<3.5 mEq/L) were also recorded. Regarding thrombocytopenia, the minimal platelet count within 10 days after the first seizure and minimal platelet count within 10 days excluding the cooling period were recorded.Clinical variables were collected for each patient including age; sex; associated infections; first seizure duration; GCS scores between 12 and 24 h after the first seizure; the worst GCS score after 24 h from the first seizure in the Late-hypo and Non-Hypo groups excluding the ictal and postictal states, and at 3 h after intravenous administration of antiepileptic medication; treatment option ; TimeAdditionally, we collected the data regarding the presence of abnormal MRI findings in patients with acute encephalopathy types other than AED, who were performed therapeutic hypothermia during this study period, to check for those in whom therapeutic hypothermia could have prevented the appearance of abnormal MRI findings  for numerous variables or the Fisher’s exact test (two-tailed) for numerous variables with effect sizes of r and phi for 2 × 2 tables or the Cramer’s V for those other than 2 × 2 tables. The statistically significant level was set at p < 0.05.SPSS ver.25  was used for all statistical analyses. First, to examine the differences between the favourable and unfavourable outcome groups, clinical variables and treatment options were examined using the Mann-Whitney p-values equal to or less than 0.06 in the univariate intergroup analysis except for The Time1st-2nd, the worst GCS scores after 24 h from the first seizure, the presence of catecholamine use, and thrombocytopenia, were included in a multivariate logistic regression analysis with backwards stepwise elimination of non-significant variables. The Time1st-2nd and the worst GCS scores after 24 h from the first seizure were excluded from the analysis, as these variables were not for all the patients, such as the Time1st-2nd only for patients with AED with second phase, the later one only for the patients in the Late-Hypo group and Non-Hypo group (Supplementary Table U test (two-tailed) was used for the associations between the Tada score and other categorical variables and Fisher’s exact test (two-tailed) was used for the other associations. The statistical significance was adjusted by the Benjamini–Hochberg procedure to correct for multiple comparisons of 10 times, using a false discovery rate of 0.05. The statistical significant associations were found between therapeutic hypothermia and catecholamine use, therapeutic hypothermia and thrombocytopenia, catecholamine use and thrombocytopenia, and the Tada score and catecholamine use . Thus, the presence of catecholamine use and thrombocytopenia were also excluded from the multivariate logistic analysis.Second, we performed the multivariate logistic regression analysis for worse outcomes within the patients with AED. In addition to the treatment option (therapeutic hypothermia or not), the variables that showed 1st-cooling, or the Time1st-35 °C, and the Time1st-34 °C; outcomes in patients with AED with therapeutic hypothermia and the Time1st-cooling; outcomes in the Late-Hypo group and the Time2nd-cooling; outcomes in patients with AED with second phase and the Time1st-2nd using the Spearman’s rank correlation coefficient. The relationship between the Time1st-cooling and the Time1st-2nd in the Late-Hypo group was also examined to investigate the influence of second phase initiation on the timing of therapeutic hypothermia using the Pearson’s correlation coefficient as the two variables were normally distributed. Here, the statistical significance was adjusted by the Benjamini–Hochberg procedure to correct multiple comparisons of seven times, using a false discovery rate of 0.05. Additionally, the threshold of Time1st-cooling in the Early-Hypo group and Time1st-2nd in patients with AED with second phase were determined to predict unfavourable outcomes from the ROC curve.Third, we performed subgroup analysis examining the relationships between outcomes in the Early-Hypo group and the TimeAll data generated or analysed during this study are available from the corresponding author on reasonable request.Supplementary Information.