The impact of pertussis vaccine programme changes on pertussis disease burden in Manitoba, 1992-2017-an age-period-cohort analysis

Disease burden of COPD attributable to PM2.5 in China, Japan and South Korea from 1990 to 2019: a comparative study based on Global Burden of Disease Study 2019

OBJECTIVE

We hope to reveal the changing trends of chronic obstructive pulmonary disease (COPD) burden attributable to particulate matter pollution (PM2.5) and its age, period and cohort effects in China, Japan and Korea.

DESIGN

We analysed the trend of COPD disease burden attributable to PM2.5 from 1990 to 2019 based on the latest Global Burden of Disease Database (GBD 2019) using JoinPoint model and analysed the effect of age, period and cohort on COPD burden attributable to PM2.5 in China, Japan and Korea from 1990 to 2019 using age-period-cohort model (model).

SETTING

GBD data from 1990 to 2019.

PARTICIPANTS

Data were publicly available and individuals were not involved.

MAIN OUTCOMES

Outcomes included the age standardised mortality rate (ASMR), the age-standardised disability-adjusted life year (DALY), average annual per cent change (AAPC), net drift, local drift, longitudinal age curves, period (cohort) rate ratios, age (period, cohort) bias coefficient.

RESULTS

From 1990 to 2019, the ASMR of COPD attributable to PM2.5 in China (AAPC=-5.862), Japan (AAPC=-1.715) and Korea (AAPC=-1.831) showed a downward trend. The age-standardised DALY of COPD attributable to PM2.5 in China (AAPC=-5.821), Japan (AAPC=-1.39) and Korea (AAPC=-1.239) showed a downward trend. Mortality of COPD attributable to PM2.5 increased slowly with age in Korea and Japan. Mortality of COPD attributable to PM2.5 in China decreased after rising (95% CI: 404.66 to 466.01). Mortality of COPD attributable to PM2.5 decreased over time in China and Korea, while it increased in Japan from 2015 to 2019. In China and Japan, mortality of COPD attributable to PM2.5 was approximately lower the later the birth, while in Korea it decreased after an increase (95% CI: 2.13 to 2.40) in the 1900-1910.

CONCLUSIONS

Most COPD burden attributable to PM2.5 is on the decline; COPD mortality attributable to PM2.5 both increased with age and decreased with time and cohort. Countries with high burden should develop targeted measures to control PM2.5.

Emerging infectious disease surveillance using a hierarchical diagnosis model and the Knox algorithm

Emerging infectious diseases are a critical public health challenge in the twenty-first century. The recent proliferation of such diseases has raised major social and economic concerns. Therefore, early detection of emerging infectious diseases is essential. Subjects from five medical institutions in Beijing, China, which met the spatial-specific requirements, were analyzed. A quality control process was used to select 37,422 medical records of infectious diseases and 56,133 cases of non-infectious diseases. An emerging infectious disease detection model (EIDDM), a two-layer model that divides the problem into two sub-problems, i.e., whether a case is an infectious disease, and if so, whether it is a known infectious disease, was proposed. The first layer model adopts the binary classification model TextCNN-Attention. The second layer is a multi-classification model of LightGBM based on the one-vs-rest strategy. Based on the experimental results, a threshold of 0.5 is selected. The model results were compared with those of other models such as XGBoost and Random Forest using the following evaluation indicators: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The prediction performance of the first-layer TextCNN is better than that of other comparison models. Its average specificity for non-infectious diseases is 97.57%, with an average negative predictive value of 82.63%, indicating a low risk of misdiagnosing non-infectious diseases as infectious (i.e., a low false positive rate). Its average positive predictive value for eight selected infectious diseases is 95.07%, demonstrating the model's ability to avoid misdiagnoses. The overall average accuracy of the model is 86.11%. The average prediction accuracy of the second-layer LightGBM model for emerging infectious diseases reaches 90.44%. Furthermore, the response time of a single online reasoning using the LightGBM model is approximately 27 ms, which makes it suitable for analyzing clinical records in real time. Using the Knox method, we found that all the infectious diseases were within 2000 m in our case, and a clustering feature of spatiotemporal interactions (P < 0.05) was observed as well. Performance testing and model comparison results indicated that the EIDDM is fast and accurate and can be used to monitor the onset/outbreak of emerging infectious diseases in real-world hospitals.

Musculoskeletal Disorder Burden and Its Attributable Risk Factors in China: Estimates and Predicts from 1990 to 2044

Musculoskeletal disorders are one of the three major disabling diseases in the world. However, the current disease burden in China is not well-known. This study aimed to explore the burden and risk factors of musculoskeletal disorders in China from 1990 to 2019, predicting the incidence trend from 2020 to 2044. All data were extracted from the Global Burden of Disease Study 2019 (GBD 2019). Joinpoint regression and age-period-cohort (APC) models were selected to analyze the epidemic trend, and descriptive analyses of the time trends and age distributions of risk factors were performed. The Bayesian APC model was used to foresee the incidence trend from 2020 to 2044. The results indicated that the burden of musculoskeletal disorders is higher in women and older adults. Its attributable risk factors were found to be tobacco, a high body mass index, kidney dysfunction and occupational risks. In 2044, musculoskeletal disorders in China showed a downward trend for 35-59-year-olds and a slight upward trend for 30-34- and 65-84-year-olds. The 70-74 year age group saw the largest increase in incidence at 4.66%. Overall, the incidence increased with age. Therefore, prevention and control policies should focus on women and the elderly, and health interventions should be carried out based on risk factors.

Lip and Oral Cavity Cancer Burden and Related Risk Factors in China: Estimates and Forecasts from 1990 to 2049

Simple Summary

China’s lip and oral cavity cancer burden is rising. The elderly have a relatively heavy disease burden, mainly due to poor oral health awareness, the side effects of other diseases and delayed treatment. Moreover, the incidence of the elderly over 50 years old is predicted to increase further from 2020 to 2049 in China. Males have a heavier disease burden, mainly due to their smoking, drinking and work exposure. Early screening and health intervention policies incorporating key populations and risk factors may deserve the consideration of policy makers to reduce the disease burden.

Abstract

Lip and oral cavity cancer is a common malignancy faced by many developing countries, and the disease burden is high in China. This study explored this cancer burden and its risk factors using data from China in the GBD 2019, along with predicting the incidence trends in 2020–2049. Data on age-standardized rates (ASR), incidence, death and disability-adjusted life years (DALY), by sex, age and risk factors were collected from the Institute for Health Metrics and Evaluation (IHME). Joinpoint regression and Age–Period–Cohort (APC) models were selected to analyze the epidemic trend of this cancer in China, and descriptive analysis was used for the time trend and age distribution of risk factors. The Bayesian APC model was selected to foresee the incidence trend in 2020–2049. This cancer burden was found to be in an upward trend in China in 1990–2019. The upward trend was more pronounced among men than among women. These cancer deaths and DALYs are overwhelmingly attributable to smoking and drinking. On APC analysis, the younger generation in China demonstrated a lower cancer risk. In 2049, the incidence of this cancer is projected to be 3.99/100,000, 6.07/100,000, 7.37/100,000, 10.49/100,000, 14.82/100,000, 19.19/100,000, 20.71/100,000, 23.64/100,000, 16.42/100,000 and 9.91/100,000 among those aged 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, 80–84, 85–89, 85–89 and over 95 years, respectively. Disease control policies and early screening should focus on men and the elderly and target different risk factors.