Assessing Effect Modification of Excess Winter Death by Causes of Death and Individual Characteristics in Zhejiang Province, China: A Multi-Community Case-Only Analysis

How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders

OBJECTIVES

Since emergency departments (EDs) are responsible for providing initial care for patients who may need urgent medical care, they are highly sensitive to increased patient delays. A key factor that increases patient delays is ordering diagnostic tests. Therefore, understanding the factors increasing diagnostic test orders and proposing efficient models may facilitate decision making in EDs.

METHODS

Month and week of the year, day of the week, and daily numbers of patients encoded based on 21 different ICD-10 codes were used as input variables. Daily test frequencies of patients requiring tests from laboratory and imaging services were modelled separately by linear regression models. Although significance of the input variables was identified based on these models, obtained forecasts and residuals were further processed by machine learning techniques to obtain hybrid models.

RESULTS

Day of the week, and number of patients with ICD-10 codes of 'A00-B99', 'I00-I99', 'J00-J99', 'M00-M99' and 'R00-R99' were significant in both test types. In addition to these, although daily patient frequencies with 'H60-H95', 'N00-N99' and 'O00-O9A' were significant for laboratory services, 'L00-L99', 'S00-T88' and 'Z00-Z99' were significant for imaging services. Although prediction accuracies of regression models were, respectively, as 93.658% and 95.028% for laboratory and imaging services modelling, they increased to 99.997% and 99.995% with the machine learning-integrated hybrid model.

CONCLUSION

The significant factors identified here can predict increases in use of laboratory and imaging services. This could enable these services to be prepared in advance to reduce ED patient delays, thereby reducing ED overcrowding. The proposed model may also be efficiently used for decision making.

The effect of temperature on cause-specific mental disorders in three subtropical cities: A case-crossover study in China

BACKGROUND

Little is known about the association between ambient temperature and cause-specific mental disorders, especially in subtropical areas.

OBJECTIVE

To investigate the effect of ambient temperature on mental disorders in subtropical cities.

METHOD

Daily morbidity data for mental disorders in three Chinese cities (Shenzhen, Zhaoqing, and Huizhou) were collected from medical record systems of local psychiatric specialist hospitals, covering patients of all ages. Case-crossover design combined with a distributed lag nonlinear model (DLNM) was used to assess the nonlinear and delayed effects of temperatures on five specific mental disorders (affective disorders, anxiety, depressive disorders, schizophrenia, and organic mental disorders), with analyses stratified by gender and age. The temperature of minimum effect was used as the reference value to calculate estimates.

RESULTS

We observed inversed J-shaped exposure-response curves between temperature and mental morbidity and observed that low temperatures had a significant and prolonged effect on most types of mental disorders in the three cities. For example, the effect of the cold (2.5th percentile) on anxiety was consistently observed in the three cities with an odds ratio (OR) of 1.29 (95% CI: 1.06-1.57) in Zhaoqing, 1.26 (95% CI: 1.18-1.34) in Shenzhen, and 1.45 (95% CI: 1.17-1.81) in Huizhou. Low temperature was also associated with an increased risk of depressive disorders and schizophrenia. For the high temperature exposure (97.5th percentile), we only observed a significant, harmful effect on anxiety [OR = 1.30 (95% CI: 1.08, 1.58) in Shenzhen, OR = 1.16 (95% CI: 1.00, 1.34) in Zhaoqing], affective disorders [OR = 1.32 (95% CI: 1.08, 1.62) in Shenzhen], and schizophrenia [OR = 1.24 (95% CI: 1.03, 1.48) in Zhaoqing, OR = 1.03 (95% CI: 1.00, 1.06) in Huizhou].

CONCLUSIONS

Our study suggests that both low and high temperatures might be important drivers of morbidity from mental disorders, and low temperature may have a more general and wide-spread effect on this cause-specific morbidity than high temperature.