Forecasting Schizophrenia Incidence Frequencies Using Time Series Approach
Background: Learning the prevalence of schizophrenia has main insinuations for both health service preparation and risk factor epidemiology. The aims of this research in order to systematically classify and collate studies explaining the prevalence of schizophrenia, to sum up the findings of these s...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Shahid Beheshti University of Medical Sciences
2017-10-01
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| Series: | International Clinical Neuroscience Journal |
| Online Access: | http://journals.sbmu.ac.ir/Neuroscience/article/download/17960/6 |
| Summary: | Background: Learning the prevalence of schizophrenia has main insinuations for both health service preparation and risk factor epidemiology. The aims of this research in order to systematically classify and collate studies explaining the prevalence of schizophrenia, to sum up the findings of these studies, also to survey selected factors that may influence occurrence approximates. Methods: This historical cohort study was done on schizophrenia patients in Farshchian psychiatric hospital from April 2008 to April 2016. To analyze the data, the Holt-Winters Exponential Smoothing (HWES) method was applied. All the analyses were done by R.3.2.3. software using the packages "forecast" and "tseries." The statistical considerable level was simulated as 0.05. Results: Our investigation shows that a constant frequency of schizophrenia incidence happens every month from August 2008 to February 2015 while a considerable increase occurs in March 2015. The high frequency of schizophrenia incidence remains constant to the end of 2015 and a decrease is shown in 2016. Also, data demonstrate the development of schizophrenia in the next 24 months with 95% CI. Conclusion: Our study showed that a significant increase happens in the frequency of schizophrenia from 2016. Although the development is not constant and the same for all months, the amount of increase is considerably high compared to before 2016. |
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| ISSN: | 2383-1871 2383-2096 |