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      The impact of SARS on hospital performance

      research-article
      1 , 3 , 2 , 3 , , 3 , 1
      BMC Health Services Research
      BioMed Central

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          Abstract

          Background

          During the SARS epidemic, healthcare utilization and medical services decreased significantly. However, the long-term impact of SARS on hospital performance needs to be further discussed.

          Methods

          A municipal hospital in Taipei City was shut down for a month due to SARS and then became the designated SARS and infectious disease hospital for the city. This study collected the outpatient, inpatient and emergency service volumes for every year from April to March over four years. Average monthly service amount ± standard deviation were used to compare patient volume for the whole hospital, as well as the outpatient numbers accessing different departments. The ARIMA model of outpatient volume in the pre-SARS year was developed.

          Results

          The average monthly service volume of outpatient visits for the base year 2002 was 52317 ± 4204 visits per month, and number for 2003 and the following two years were 55%, 82% and 84% of the base year respectively. The average emergency service volume was 4382 ± 356 visits per month at the base year and this became 45%, 77% and 87% of the base year for the following three years respectively. Average inpatient service volume was 8520 ± 909 inpatient days per month at the base year becoming 43%, 81% and 87% of the base year for the following three years respectively. Only the emergency service volume had recovered to the level of a non-significant difference at the second year after SARS. In addition, the departments of family medicine, metabolism and nephrology reached the 2002 patient number in 2003. The ARIMA (2,1,0) model was the most suitable for outpatient volume in pre-SARS year. The MAPE of the ARIMA (2,1,0) model for the pre-SARS year was 6.9%, and 43.2%, 10.6%, 6.2% for following 3 years.

          Conclusion

          This study demonstrates that if a hospital is completely shut down due to SARS or a similar disease, the impact is longer than previous reported and different departments may experience different recover periods. The findings of this study identify subspecialties that are particularly vulnerable in an infectious disease designated hospital and such hospitals need to consider which subspecialties should be included in their medical structure.

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          Most cited references17

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          The impact of the SARS epidemic on the utilization of medical services: SARS and the fear of SARS.

          Using interrupted time-series analysis and National Health Insurance data between January 2000 and August 2003, this study assessed the impacts of the severe acute respiratory syndrome (SARS) epidemic on medical service utilization in Taiwan. At the peak of the SARS epidemic, significant reductions in ambulatory care (23.9%), inpatient care (35.2%), and dental care (16.7%) were observed. People's fears of SARS appear to have had strong impacts on access to care. Adverse health outcomes resulting from accessibility barriers posed by the fear of SARS should not be overlooked.
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            Psychosocial impact among the public of the severe acute respiratory syndrome epidemic in Taiwan.

            During the 2003 outbreak, severe acute respiratory syndrome (SARS) spread to more than 30 countries. Not only did it cause severe health problems but it also imposed a great psychological impact on the public. SARS emerged in Taiwan during April 2003. This study investigates the psychosocial impact and the associated factors of depression of the SARS epidemic in Taiwan when the epidemic had just been controlled. A total of 1552 respondents were recruited in the study by random selection from the telephone book. Demographic data, SARS experience, self-perceived health state, neighborhood relationships, and depression were surveyed by telephone interviewing. Respondents were grouped as 'impacted group' and 'non-impacted group' according to whether they or their friends and family had been quarantined, or suspected of being infected. The psychosocial impact and associated factors were compared between the two groups. The 'impacted group' had higher depressive levels, poorer neighborhood relationships, poorer self-perceived health, and a higher economic impact than the 'non-impacted group'. The poorer self-perceived health and economic impact factors were associated with depression. The neighborhood relationship factor was negatively associated with depression for the 'impacted group', but not for the 'non-impacted group'. The 'impacted group' had experienced greater psychosocial impact possibly due to the SARS impact, the economic downturn, poor self-perceived health conditions, and decreased social support systems. An appropriate mental health intervention to improve the self-perceived health condition, to provide instrumental and psychological support for the 'impacted group', and to decrease the stigmatization and discrimination from the public could have buffered the psychological impact from this epidemic disaster.
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              Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore

              Background The main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. Methods This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. Results We found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. Conclusion ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.
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                Author and article information

                Journal
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central
                1472-6963
                2008
                6 November 2008
                : 8
                : 228
                Affiliations
                [1 ]Community Medicine Research Center and Institute of Public Health, National Yang-Ming University, Taipei, Taiwan
                [2 ]Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
                [3 ]Taipei City Hospital, 33 Sec. 2 Chun Hwa Road, Taipei, Taiwan
                Article
                1472-6963-8-228
                10.1186/1472-6963-8-228
                2613902
                18990210
                f9b2d7b0-ee62-44ac-96ca-7e120a10170c
                Copyright © 2008 Chu et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 3 June 2008
                : 6 November 2008
                Categories
                Research Article

                Health & Social care
                Health & Social care

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