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Background Cancer poses a significant public health challenge in India, making it crucial to predict its future impact for effective healthcare planning. This study forecast cancer incidence, mortality, and disability-adjusted life years (DALYs) in India from 2022 to 2031. Methods We extracted age-standardized data on incidence, prevalence, DALYs, and mortality from 1990 to 2021 from the Global Burden of Disease (GBD) study. We used Decadal Average Percentage Change techniques to identify trends in cancer burden over decades and the Autoregressive Integrated Moving Average (ARIMA) method were used for forecasting. The ARIMA (2,2,2) model was identified as the best for predicting cancer incidence, ARIMA (0,3,3) for DALYs, and ARIMA (0,2,2) for mortality. Results The cancer incidence rate is expected to rise from 529.40 (95% CI: 525.41-533.38) in 2022 to 549.17 (95% CI: 487.43-610.92) per 100,000 population in 2031. The DALYs rate is projected to decrease from 2001.53 (95% CI: 1964.24-2038.82) in 2022 to 1842.08 (95% CI: 1273.57-2410.60) per 100,000 population in 2031, indicating improvements in cancer burden management. Mortality rates are forecasted to increase slightly, from 71.52 (95% CI: 69.91–73.12) in 2022 to 73.00 (95% CI: 60.88–85.11) per 100,000 population in 2031. Overall, while incidence and mortality rates show a slight upward trend, the DALYs rate is projected to decrease, reflecting potential advancements in cancer management and treatment over the forecast period. Conclusions Over the next decade, cancer incidence and mortality are expected to increase in India, highlighting the need for enhanced prevention, early detection, and proper treatment strategies. Despite these increases, the anticipated decrease in DALYs suggests potential advancements in cancer management, warranting further investigation into the drivers of this positive trend and measures to sustain it.
Background Cancer poses a significant public health challenge in India, making it crucial to predict its future impact for effective healthcare planning. This study forecast cancer incidence, mortality, and disability-adjusted life years (DALYs) in India from 2022 to 2031. Methods We extracted age-standardized data on incidence, prevalence, DALYs, and mortality from 1990 to 2021 from the Global Burden of Disease (GBD) study. We used Decadal Average Percentage Change techniques to identify trends in cancer burden over decades and the Autoregressive Integrated Moving Average (ARIMA) method were used for forecasting. The ARIMA (2,2,2) model was identified as the best for predicting cancer incidence, ARIMA (0,3,3) for DALYs, and ARIMA (0,2,2) for mortality. Results The cancer incidence rate is expected to rise from 529.40 (95% CI: 525.41-533.38) in 2022 to 549.17 (95% CI: 487.43-610.92) per 100,000 population in 2031. The DALYs rate is projected to decrease from 2001.53 (95% CI: 1964.24-2038.82) in 2022 to 1842.08 (95% CI: 1273.57-2410.60) per 100,000 population in 2031, indicating improvements in cancer burden management. Mortality rates are forecasted to increase slightly, from 71.52 (95% CI: 69.91–73.12) in 2022 to 73.00 (95% CI: 60.88–85.11) per 100,000 population in 2031. Overall, while incidence and mortality rates show a slight upward trend, the DALYs rate is projected to decrease, reflecting potential advancements in cancer management and treatment over the forecast period. Conclusions Over the next decade, cancer incidence and mortality are expected to increase in India, highlighting the need for enhanced prevention, early detection, and proper treatment strategies. Despite these increases, the anticipated decrease in DALYs suggests potential advancements in cancer management, warranting further investigation into the drivers of this positive trend and measures to sustain it.
Background By analysing the deaths of inpatients in a tertiary hospital in Hangzhou, this study aimed to understand the epidemiological distribution characteristics and the composition of the causes of death. Additionally, this study aimed to predict the changing trend in the number of deaths, providing valuable insights for hospitals to formulate relevant strategies and measures aimed at reducing mortality rates. Methods In this study, data on inpatient mortality at a tertiary hospital in Hangzhou from 2015 to 2022 were obtained via the population information registration system of the Chinese Center for Disease Control and Prevention. The death data of inpatients were described and analysed through a retrospective study. Excel 2016 was utilized for data sorting, and SPSS 22.0 software was employed for data analysis. The statistical inference of single factor differences was conducted via χ2 tests. The SARIMA model was established via the forecast, aTSA, and tseries software packages (version 4.3.0) to forecast future changes in the number of deaths. Results A total of 1938 inpatients died at the tertiary hospital in Hangzhou, with the greatest number of deaths occurring in 2022 (262, 13.52%). The sex ratio was 2.22:1, and there were significant differences between sexes in terms of age, marital status, educational level, and place of residence ( P < 0.05). The percentage of males in the groups aged of 20 to 29 and 30 to 39 years was significantly greater than that of females (χ 2 = 46.905, P < 0.001). More females than males died in the widowed group, and divorced and married males experienced a greater number of deaths than divorced and married females did (χ 2 = 61.130, P < 0.001). The proportions of male students with a junior college and senior high school education were significantly greater than that of female students (χ 2 = 12.310, P < 0.05). The primary causes of mortality within the hospital setting included circulatory system diseases, injury, poisoning, tumours, and respiratory system diseases. These leading factors accounted for 86.12% of all recorded deaths. Finally, the SARIMA (2, 1, 1) (1, 1, 1) 12 model was determined to be the optimal model, with an AIC of 380.23, a BIC of 392.79, and an AICc of 381.81. The MAPE was 14.99%, indicating a satisfactory overall fit of this model. The relative error between the predicted and actual number of deaths in 2022 was 8.02%. Therefore, the SARIMA (2, 1, 1) (1, 1, 1) 12 model demonstrates good predictive performance. Conclusions Hospitals should enhance the management of sudden cardiac death, acute myocardial infarction, severe craniocerebral injury, lung cancer, and lung infection to reduce the mortality rate. The ...
Background The statistical analysis of death cases has important clinical research value. Our study aims to describe the epidemiology of death cases in a tertiary comprehensive hospital in Hangzhou from 2015 to 2022 and predict the number of future deaths, providing a reference basis for hospitals to formulate relevant strategies and measures. Methods Death data of inpatients and non-inpatients in the hospital from 2015 to 2022 were obtained through the CDC-DSP system. The data of death cases were described and analyzed by retrospective study, and the single factor difference was statistically inferred by χ2 tests. P < 0.05 was considered statistically significant. According to International Classification of Diseases 10th revision (ICD-10), the main causes of death of patients were obtained. SARIMA model was established by R 4.3.0 (forecast, aTSA, tseries) software for time series analysis. Results A total of 1938 death cases from 2015 to 2022, including 287 inpatients and 1651 non- inpatients. Among them, the highest was in 2022 (262, 13.52%), and the lowest was in 2019 (223, 11.51%). The gender ratio is 2.22:1, and there are differences (P < 0.05) between different genders in the age, marital status, educational level, and distribution of place of residence. The main cause of death were circulatory system diseases (32.66%), injury-poisoning (28.22%), tumors (14.76%), and respiratory system diseases (10.47%), with a cumulative proportion of 86.12%. Furthermore, the SARIMA (2,1,1)(1,1,1)12 model was ultimately determined to predict the number of deaths among patients, AIC = 380.23, BIC = 392.79, AICc = 381.81, MAPE = 14.99%. Conclusions The hospital should focus on improving the pre-hospital emergency treatment and the ability of multi-disciplinary cooperation in the hospital to reduce the number of deaths of hospital patients. the SARIMA model is suitable for predicting the number of death cases and provide reference value for the rational allocation of medical resources.
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