| Literature DB >> 29084785 |
Wenming Chen1, Shengnan Wang1, Qi Wang1, Weibing Wang1,2,3,4.
Abstract
OBJECTIVES: To provide cost burden estimates and long-term trend forecast of mental disorders that need hospitalisations in Shanghai, China.Entities:
Keywords: expenditure; forecast; mental disorders
Mesh:
Year: 2017 PMID: 29084785 PMCID: PMC5665299 DOI: 10.1136/bmjopen-2016-015652
Source DB: PubMed Journal: BMJ Open ISSN: 2044-6055 Impact factor: 2.692
Summary statistics of admissions for patients hospitalised with mental disorders in Shanghai, 2011–2015
| Number | % | |
| All | 60 306 | |
| Sex | ||
| Man | 30 292 | 50.23 |
| Woman | 30 014 | 49.77 |
| Age (year) | ||
| <45 | 14 296 | 23.71 |
| 45–65 | 24 759 | 41.06 |
| 65–75 | 7381 | 12.24 |
| ≥75 | 13 870 | 23.00 |
| Insurance type | ||
| Employed | 51 815 | 85.92 |
| Unemployed | 8491 | 14.08 |
Figure 1Yearly cumulative admissions and expenditures for patients hospitalised with mental disorder in Shanghai (US$ million). The line with symbol ‘●’ represents the yearly admissions for patients hospitalised, and the black bars represent the yearly total expenditures for total population, while grey bars represent expenditures for employed population and white bars represent expenditures for unemployed population.
Annual medical expenditures for mental disorder hospitalisations in Shanghai, 2015 (US$)
| Yearly total health expenditures (thousands of US$) | Yearly health expenditures per admission (US$) | |||||
| All | Employed | Unemployed | All | Employed | Unemployed | |
| All | 42 134.02 | 37 024.61 | 5109.41 | 2998.01 | 3021.92 | 2835.41 |
| Sex | ||||||
| Man | 22 017.05 | 19 410.02 | 2607.02 | 3108.87 | 3160.21 | 2773.43 |
| Woman | 20 116.97 | 17 614.59 | 2502.38 | 2885.39 | 2882.91 | 2903.00 |
| Age (year) | ||||||
| <45 | 8840.83 | 5758.80 | 3082.03 | 2929.37 | 2890.96 | 3003.92 |
| 45–65 | 16 945.48 | 15 873.11 | 1072.37 | 2999.73 | 3019.42 | 2735.64 |
| 65–75 | 6515.87 | 6182.60 | 333.27 | 3085.17 | 3141.56 | 2314.41 |
| ≥75 | 9831.84 | 9210.11 | 621.73 | 3002.09 | 3034.63 | 2590.56 |
Direct medical expenditures for mental disorder hospitalisations per admission in Shanghai, 2015 (US$)
| Total expenditure | Diagnostic testing | Blood transfusion and oxygen therapy | Medical | Medication | Surgical materials | Other | |
| All | 2998.01 | 579.19 | 22.83 | 1296.20 | 678.83 | 90.39 | 330.58 |
| Employed | 3021.92 | 585.41 | 25.10 | 1287.66 | 704.06 | 85.89 | 333.79 |
| Unemployed | 2835.41 | 536.88 | 7.37 | 1354.25 | 507.26 | 120.92 | 308.73 |
Medical supplies mainly included patient meals, care and supplement treatments, such as physical therapy and psychotherapy.
Performance of the forecasting models
| Model | Ljung-Box p value | MAE | MAPE | |
| Total population | ARIMA (1,0,0)×(1,1,0)12 | 0.920 | 99.971 | 3.871 |
| Employed population | ARIMA (1,0,0)×(1,1,0)12 | 0.958 | 112.404 | 4.250 |
| Unemployed population | ARIMA (1,0,1)×(1,1,0)12 | 0.426 | 157.391 | 6.599 |
For the measures of MAE and MAPE, a smaller value indicates better model performance. However, there is no absolute standard to which these values can be compared directly.
ARIMA, autoregressive integrated moving average, MAE, mean absolute error, MAPE, mean absolute percentage error.
Figure 2Forecasted costs for mental disorders per month per admission (US $). The red lines show observed values, while the blue lines show fit values (before the reference line) and projected values (after the reference line), and the light pink lines show 95% CI of the series: (A) all population; (B) employed population and (C) unemployed population. UCL, upper control limit. LCL, lower control limit.
Figure 3Prediction results of hospitalisation expenditure based on different population scheme (million US$). The line with the symbol ‘X’ represents the annual total hospitalisation expenditure of the high population scheme, while the line with the symbol ‘▲’ and the line with the symbol ‘●’ represent the expenditure of the middle scheme and the low scheme, respectively.