This study aims to analyze the trends of premature mortality caused from four major non-communicable diseases [NCDs], namely cardiovascular disease [CVD], cancer, chronic respiratory diseases, and diabetes in Nanjing between 2007 and 2018 and project the ability to achieve the “Healthy China 2030” reduction target.
Methods
Mortality data of four major NCDs for the period 2007–2018 were extracted from the Death Information Registration and Management System of Chinese Center for Disease Control and Prevention. Population data for Nanjing were provided by the Nanjing Bureau of Public Security. The premature mortality was calculated using the life table method. Joinpoint regression model was used to estimate the average annual percent changes [AAPC] in mortality trends.
Results
From 2007 to 2018, the premature mortality from four major NCDs combined in Nanjing decreased from 15.5 to 9.5%, with the AAPC value at − 4.3% [95% CI [− 5.2% to − 3.4%]]. Overall, it can potentially achieve the target, with a relative reduction 28.6%. The premature mortality from cancer, CVD, chronic respiratory diseases and diabetes all decreased, with AAPC values at − 4.2, − 5.0%, − 5.9% and − 1.6% respectively. A relative reduction of 40.6 and 41.2% in females and in rural areas, but only 21.0 and 12.8% in males and in urban areas were projected.
Conclusion
An integrated approach should be taken focusing on the modifiable risk factors across different sectors and disciplines in Nanjing. The prevention and treatment of cancers, diabetes, male and rural areas NCDs should be enhanced.
Peer Review reports
Background
Non-communicable diseases [NCDs] are becoming the leading cause of death worldwide, and considered as the major health challenges in the twenty-first century [1]. In 2016, NCDs collectively caused 41 million deaths worldwide, equivalent to 71% of all global deaths. Cardiovascular disease [CVD], cancers, chronic respiratory diseases and diabetes attribute to 80% of NCDs related deaths [2]. In China, death from non-communicable disease in 2016 accounted for 89% of all deaths, of them 77% were due to these four major NCDs [1]. Increased non-communicable disease burden would lead to a shortage of health resources, increased treatment costs and delayed economic growth. It was estimated that the risk of a 30-years-old person dying from any of four major NCDs before reaching the age of 70 years was 17% in China. This is lower than the global risk [18%], and with a slightly higher risk for males [20%] than for females [14%].
World Health Organization [WHO] recognizes premature mortality [defined as the probability of dying between the ages of 30 years and 70 years] as an important indicator in assessing the level of NCDs prevention and control in a region that is not affected by age composition [3,4,5]. In 2012, WHO proposes to reduce premature mortality from four major NCDs by 25% relative to 2010 levels by 2025 [6, 7]. The United Nation’s Sustainable Development Goals for 2030 includes the aim of reducing premature mortality from NCDs by one third [relative to 2015 levels] [8], while “Healthy China 2030” proposes to reduce premature mortality by 10% by 2020 and 30% by 2030 [9]. Previous studies suggested that there were significant different in premature mortality caused by four major NCDs and their change speed among provinces and the task of achieving “Healthy China 2030” reduction target would be daunting [10,11,12,13]. However, they were gender and geographic alone studies or used the annual growth rate only. However, whether their findings can be generalized to other cities in China is unknown.
Nanjing, the provincial capital city of eastern China Jiangsu Province, is one of the important researches and education bases and a critical transportation hub in the country, with a population of 8.5 million at the end of 2018. Previous studies reported that the top 3 causes of death in Nanjing were non-communicable diseases, including CVD, cancers and chronic respiratory diseases [14]. To facilitate policy makers to implement preventative strategies and achieving “Healthy China 2030” target, the present study aimed to evaluate the trends in premature mortality from four major NCDs in Nanjing in the last decade, focusing on the gender and geographic difference.
Methods
Data collection
Mortality data of four major NCDs from 2007 to 2018 in Nanjing were extracted from the Death Information Registration and Management System which is operated by Chinese Center for Disease Control and Prevention [CDC]. Household registration population data were provided by the Nanjing Bureau of Public Security.
The death information registry, implemented since 2007, records in detail of the death information, including sex, date of birth, date of death, underlying causes. All categories of causes of death are coded using the International Classification of Diseases 10th Edition [ICD-10] [15]. Four major NCDs were identified and classified according to the death cause statistics section of the WHO Global Health Report, including cardiovascular disease [ICD-10: I00-I99], cancers [ICD-10: C00-C97], chronic respiratory diseases [ICD-10: J30-J98] and diabetes [ICD-10: E10-E14]. The data were subject to the three-level quality control of medical institutions, district CDC and municipal CDC, and reviewed monthly with public security, civil affairs and other departments to ensure the accuracy of the data [16].
Statistical analysis
The primary indicator of this study was premature mortality from four major NCDs whereas the second indicator was age-standardized premature mortality rates [ASPMR]. Using the direct standardization method, ASPMR of four major NCDs were calculated as number of deaths per 100,000 residents by age groups, based on the 2000 China’s Fifth Census Data.
Referring to WHO’s definition, premature mortality was considered as death of 30–70 years old [excluding 70 years old]. Using the life table method, the premature mortality between the exact ages of 30 and 70, from any of the four causes and in the absence of other causes of death, was calculated using the equations below [17].
Mortality rates according to five-year age groups [5*Mx] were first calculated:
$$ {5}^{\ast }{\mathrm{M}}_x=\frac{\mathrm{Total}\ \mathrm{deaths}\ \mathrm{from}\ \mathrm{four}\ \mathrm{NCD}\ \mathrm{causes}\ \mathrm{between}\ \mathrm{exact}\ \mathrm{age}\ \mathrm{x}\ \mathrm{and}\ \mathrm{exact}\ \mathrm{age}\ \mathrm{x}+5}{\mathrm{Total}\ \mathrm{population}\ \mathrm{between}\ \mathrm{exact}\ \mathrm{age}\ \mathrm{x}\ \mathrm{and}\ \mathrm{exact}\ \mathrm{age}\ \mathrm{x}+5} $$
For each five-year age group, the probability of mortality from four major NCDs [5*qx] was calculated using the following formula:
$$ {{}_5{}^{\ast }q}_{\mathrm{x}}=\frac{{{}_5{}^{\ast}\mathrm{M}}_{\mathrm{x}}\ast 5}{1+{{}_5{}^{\ast}\mathrm{M}}_{\mathrm{x}}\ast 2.5} $$
The unconditional probability of death, for the 30–70 age range, was calculated last:
$$ {{}_{40}{}^{\ast }q}_{30}=1-\prod \limits_{x=30}^{65}\left[1-{{}_5{}^{\ast }q}_x\right] $$
The changes in mortality time trend were described using joinpoint regression analysis. Permutation test was used to determine the statistically significant joinpoint points in the model. According to the requirements of the model, at most 2 joinpoints can be selected for 12 data points. We reported the best model recommended by the Joinpoint Regression Program. To quantify the trend over the whole period, the average annual percent change [AAPC] and corresponding 95% confidence interval [CI] were evaluated [18]. AAPC was computed as a geometric weighted average of various annual percent change [APC] values from the regression analysis. We projected ASPRM and premature mortality from four major NCDs for 2030 by fitting non-linear analysis model by the joinpoint regression, and the formula is as follows [19].
$$ E\kern0.10em \left[\kern0.10em {y}_i\kern0.28em |\kern0.28em {x}_i\right]={e}^{\beta_0+{\beta}_1{x}_i+{\delta}_1{\left[{x}_i-{\Gamma}_1\right]}^{+}+\cdots +{\delta}_1{\left[{x}_i-{\Gamma}_k\right]}^{+}} $$
Microsoft Excel [version 2019] and Joinpoint Regression Program [Version 4.7.0.0] were used for this study. APC > 0 means that the rate has increased annually in a certain period of time, APC