Analysis of rheumatic heart disease mortality in the Chinese population: A JoinPoint and age-period-cohort study

Abstract

Background: The aim of this study was to analyze rheumatic heart disease (RHD) mortality trends in China’s urban and rural areas and to determine the roles of age, period, and cohort effects. Methods: Based on mortality data extracted from the Chinese Health Statistics Yearbook, we calculated the crude mortality rate of RHD in China. Age-adjusted rates were computed by the direct method using the 2020 census as the standard population. The annual percentage change (APC) and average annual percentage change (AAPC) were determined by the JoinPoint regression model. The age-period-cohort model and the intrinsic estimator (IE) algorithm were used to estimate the effects of age, period, and cohort. Results: From 2006 to 2020, the general trend in RHD standardized mortality declined. The RHD mortality rate was higher in rural than in urban areas and among females than males. JoinPoint regression showed that the elderly (over 60 years old) were at high risk for RHD deaths in China. The age effect increased with age, and the cohort effect showed an overall declining trend as chronology grew, but the period effect was not significant. Conclusions: China has achieved great success in RHD, but RHD mortality may increase with age. We should focus on publicity and education about RHD among the elderly. Compared with the period effect, age and cohort effects dominated the risk of RHD deaths.

Spatial‐temporal dynamics and time series prediction of HFRS in mainland China: A long‐term retrospective study

“…After segmentation, the researchers can clearly see the change trend and then analyze the background and causes of the segment points. In recent years, JPR has been increasingly used by researchers to determine the trend of tumors, COVID‐19, AIDS, smoking, and other diseases 14–17 . Further, the cluster analysis used in the above literatures (such as hotspot analysis and spatial correlation analysis) give more insights into the spatial distribution characteristics of HFRS, 10,11 but ignore its temporal evolution pattern.…”

“…In recent years, JPR has been increasingly used by researchers to determine the trend of tumors, COVID-19, AIDS, smoking, and other diseases. [14][15][16][17] Further, the cluster analysis used in the above literatures (such as hotspot analysis and spatial correlation analysis) give more insights into the spatial distribution characteristics of HFRS, 10,11 but ignore its temporal evolution pattern. To this end, wavelet cluster analysis provides an efficient tool to character the internal variation of HFRS in a space−time−frequency domain together with the series from various spatial locations.…”