nach oben

BMC Public Health

Erschienen in:

Open Access 01.12.2021 | Research

‘Trend in premature mortality from four major NCDs in Nanjing, China, 2007–2018’

verfasst von: Huafeng Yang, Yali Fu, Xin Hong, Hao Yu, Weiwei Wang, Fengxia Sun, Jinyi Zhou, Nan Zhou

Erschienen in: BMC Public Health | Ausgabe 1/2021

Abstract

Background

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.

Huafeng Yang and Yali Fu contributed equally as co-first authors of this manuscript.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

NCDs

Non-communicable diseases

CVD

cardiovascular disease

AAPC

Average annual percentage changes

WHO

World Health Organization

ICD-10

International Classification of Diseases 10th Edition

CDC

Center for Disease Control and Prevention

ASPMR

Age-standardized premature mortality rates

Confidence interval

APC

Annual percent change

COPD

Chronic obstructive pulmonary disorder

HPV

Human papillomavirus

HBV

Hepatitis B virus

Computed tomography

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 [3‐5]. 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 [10‐13]. 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^*M_x) 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^*q_x) 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 < 0 means that the rate has decreased annually in a certain period. If there is no joinpoint, then APC = AAPC, which means the rate fluctuated during the total study period. A two-sided P value of less than 0.05 was considered statistically significant.

Result

Over the 12-year study period from 2007 to 2018, a total of 105,761 premature deaths of four major NCDs were recorded in Nanjing. Cancers were found to be the most common causes among those four major NCDs (64,996, 61.5% of the total), followed by CVD (34,128, 32.3%), chronic respiratory diseases (3328, 3.2%) and diabetes (3309, 3.1%). The number of deaths in males were almost twice than in females (70,695 vs. 35,066, M/F: 2.0), but 20% lower in urban areas than in rural areas (45,514 vs. 60,247, U/R: 0.8).

As shown in Fig. 1, continuing declining in the age-standardized premature mortality rates and probability of premature mortality were observed. ASPMR changed from 265.6 per 100,000 people in 2007 to 155.6 per 100,000 people in 2018 (Table 1). Joinpoint analysis showed a significant decrease in ASPMR, with AAPC of − 4.6% (95% confidence interval [CI], − 6.0% to − 3.2%), and identified three patterns. Specifically, it slightly decreased from 2007 to 2010 (APC − 0.5%) before a sharp decreasing from 2007 to 2013 (APC − 12.1%) and 2013 to 2018 (APC − 2.3%) (Table 3). The premature mortality decreased from 15.5% in 2007 to 9.5% in 2018 (Table 1, Fig. 1). For premature mortality, joinpoint analysis showed a significant decrease, with AAPC of − 4.3% (95% CI, − 5.3% to − 3.4%). From 2007 to 2010, there was a non-significant decrease of 0.07%. From 2010 onwards, the probability significantly decreased, with APC of − 11.9%, followed by another significantly decreasing trend of − 2.2%. (Table 4).

Table 1

Age-standardized premature mortality rates (per 100,000) and premature mortality (%) of four major NCDs and its subcategory in Nanjing, 2007–2018

Year	Four major NCDs		Cancers		Diabetes		Cardiovascular disease		Chronic respiratory diseases
Year	ASPMR(95%CI)	premature mortality	ASPMR(95%CI)	premature mortality	ASPMR(95%CI)	premature mortality	ASPMR(95%CI)	premature mortality	ASPMR(95%CI)	premature mortality
2007	265.6 (265.2–266.0)	15.5	164.2 (163.9–164.5)	9.7	7.2 (7.2–7.3)	0.5	85.9 (85.7–86.1)	5.4	8.3 (8.2–8.3)	0.6
2008	262.1 (261.7–262.5)	15.4	158.4 (158.1–158.7)	9.5	7.8 (7.7–7.8)	0.5	87.7 (87.5–88.0)	5.5	8.2 (8.1–8.3)	0.6
2009	267.5 (267.1–268.0)	15.7	164.7 (164.3–165.0)	9.9	8.4 (8.4–8.5)	0.6	86.6 (86.4–86.8)	5.5	7.9 (7.8–8.0)	0.5
2010	262.8 (262.4–263.2)	15.6	159.4 (159.1–159.7)	9.6	7.6 (7.6–7.7)	0.5	88.6 (88.3–88.8)	5.7	7.2 (7.2–7.3)	0.5
2011	230.9 (230.5–231.3)	13.9	141.9 (141.6–142.2)	8.7	5.7 (5.7–5.8)	0.4	75.4 (75.2–75.6)	4.8	7.8 (7.7–7.9)	0.6
2012	192.3 (192.0–192.7)	11.4	115.3 (115.0–115.6)	7.0	5.6 (5.6–5.7)	0.4	64.8 (64.6–65.0)	4.0	6.6 (6.5–6.7)	0.5
2013	181.7 (181.3–182.0)	10.9	108.8 (108.6–109.1)	6.6	5.3 (5.3–5.4)	0.4	61.6 (61.4–61.8)	3.9	5.9 (5.8–5.9)	0.4
2014	171.9 (171.6–172.2)	10.4	107.1 (106.9–107.4)	6.6	4.8 (4.7–4.8)	0.3	53.8 (53.6–53.9)	3.4	6.2 (6.2–6.3)	0.4
2015	172.0 (171.7–172.3)	10.3	107.1 (106.8–107.4)	6.6	5.5 (5.4–5.5)	0.4	53.8 (53.7–54.0)	3.4	5.6 (5.5–5.6)	0.4
2016	168.9 (168.6–169.3)	10.1	104.7 (104.4–104.9)	6.4	5.6 (5.5–5.6)	0.4	53.8 (53.6–53.9)	3.3	4.9 (4.9–5.0)	0.3
2017	161.2 (160.9–161.6)	9.7	100.1 (99.9–100.4)	6.2	6.2 (6.2–6.3)	0.4	50.2 (50.0–50.4)	3.1	4.7 (4.7–4.8)	0.3
2018	155.6 (155.2–155.9)	9.5	96.0 (95.8–96.29)	5.9	6.2 (6.1–6.3)	0.4	48.8 (48.6–48.3)	3.0	4.6 (4.5–4.6)	0.3

Abbreviations: ASPMR, Age-standardized premature mortality rates; NCDs, Non-communicable diseases

Trend in premature mortality of four major NCDs in different subcategories

From 2007 to 2018, the ASPMR from four major NCDs showed a downward trend annually (Table 1). Joinpoint regression analysis indicated that the ASPMR decrease significantly in cancers, CVD and chronic respiratory diseases, with the AAPC of − 4.5, − 5.0% and − 5.7%, respectively, showing significant changes, without detectable joinpoint in chronic respiratory diseases (Table 3 and Figure1). Trends of premature mortality were similarly illustrating continuously downward trends in cancers, CVD and chronic respiratory diseases with AAPC of − 4.2, − 5.0% and − 5.9%, respectively (Table 4). For diabetes, the ASPMR continuously increased until 2009 (APC 7.1%), then, a decreasing trend from 2009 to 2013 (APC − 12.3%), whereas an increasing trend from 2013 to 2018 (APC 5.1%) was observed without significance (AAPC -1.3, 95% CI [− 6.1 to 3.8%]) (Table 3). Similarly, the premature mortality of diabetes remained relatively stable over the period (AAPC -1.6, 95% CI [− 6.7 to 3.8%]) (Table 4).

Trend in premature mortality of four major NCDs with different genders

From 2007 to 2018, the ASPMR in both males and females showed a downtrend annually, decreasing from 362.3 per 100,000 people to 210.0 per 100,000 people, and from 173.0 per 100,000 people to 101.0 per 100,000 people, respectively (Table 2). Remarkably decreased trends of ASPMR of four major NCDs in both males (AAPC − 4.7%) and females (AAPC − 4.7%) were observed (Table 3). For premature mortality, the declines were slightly larger in males (20.8 to 12.7%) than in females (10.2 to 6.0%) (Table 2 and Fig. 2). While a significant decrease in the premature mortalities were observed in both genders during the study period (AAPC − 4.2% for males and − 4.6% for females, respectively), with three trends. During 2010 to 2013, the premature mortality in both males and females significantly decreased with AAPC of − 12.5% and − 11.4% (Table 4).

Table 2

Age-standardized premature mortality rates (per 100,000) and premature mortality (%) of four major NCDs by gender and geographic in Nanjing, 2007–2018

Year	Male		Female		Urban		Rural
Year	ASPMR(95%CI)	premature mortality	ASPMR(95%CI)	premature mortality	ASPMR(95%CI)	premature mortality	ASPMR(95%CI)	premature mortality
2007	362.3 (361.6–362.9)	20.8	173.0 (172.5–173.4)	10.2	209.9 (209.3–210.5)	13.1	321.1 (320.5–321.6)	18.0
2008	350.7 (350.1–351.4)	20.2	177.5 (177.0–177.9)	10.6	206.7 (206.1–207.3)	13.0	313.5 (313.0–314.1)	17.6
2009	366.0 (365.3–366.6)	21.2	175.1 (174.6–175.6)	10.4	202.5 (201.9–203.1)	12.7	331.5 (330.9–332.1)	18.7
2010	358.0 (357.4–358.7)	20.8	174.3 (173.9–174.8)	10.6	203.1 (202.5–203.7)	13.0	319.1 (318.5–319.7)	18.1
2011	304.3 (303.7–304.9)	18.1	156.6 (156.1–157.0)	9.4	183.5 (182.9–184.0)	11.7	274.6 (274.1–275.1)	15.9
2012	250.7 (250.2–251.3)	14.7	132.3 (131.9–132.7)	8.0	161.4 (160.9–162.0)	10.1	220.4 (219.9–220.8)	12.7
2013	239.7 (239.1–240.2)	14.2	122.2 (121.8–122.6)	7.4	149.2 (148.7–149.7)	9.4	211.5 (211.0–211.9)	12.3
2014	226.5 (226.0–227.1)	13.5	115.9 (115.5–116.3)	7.0	144.2 (143.7–144.7)	9.1	197.1 (196.7–197.6)	11.5
2015	229.2 (228.7–229.8)	13.7	113.7 (113.3–114.1)	6.9	148.9 (148.4–149.4)	9.5	192.5 (192.1–193.0)	11.3
2016	224.0 (223.4–224.5)	13.4	113.2 (112.8–113.5)	6.8	149.9 (149.4–150.4)	9.5	185.4 (185.0–185.8)	10.9
2017	214.7 (214.1–215.2)	13.0	107.3 (106.9–107.7)	6.4	142.4 (141.9–142.9)	9.0	177.2 (176.7–177.6)	10.5
2018	209.9 (209.4–210.4)	12.7	101.0 (100.6–101.4)	6.0	138.8 (138.3–139.3)	8.9	168.3 (167.9–168.7)	9.9

Abbreviations: ASPMR, Age-standardized premature mortality rates; NCDs, Non-communicable diseases

Table 3

Joinpoint analysis of trends in age-standardized premature mortality rates (ASPMR) of four major NCDs in Nanjing (2007–2018)

	Total Study Period	Trend 1		Trend 2		Trend 3
	AAPC (%, 95 CI)	years	APC (%)	years	APC (%)	years	APC (%)
All	−4.6*(−6.0 ~ −3.2)	2007–2010	−0.5	2010–2013	−12.1*	2013–2018	−2.3*
Subcategory
Cancers	−4.5*(−6.3 ~ −2.7)	2007–2010	− 0.9	2010–2013	− 12.1*	2013–2018	− 1.9*
Diabetes	− 1.3(− 6.1 ~ 3.8)	2007–2009	7.1	2009–2013	− 12.3*	2013–2018	5.1
Cardiovascular disease	−5.0*(− 6.7 ~ − 3.2)	2007–2010	− 0.3	2010–2014	− 10.9*	2014–2018	− 2.2
Chronic respiratory diseases	−5.7*(− 6.7 ~ − 4.8)	2007–2018
Gender
Male	−4.7*(− 6.5 ~ − 2.9)	2007–2010	− 0.7	2010–2013	− 13.0*	2013–2018	− 1.8
Female	−4.7*(− 6.4 ~ − 2.9)	2007–2010	0.0	2010–2013	− 11.6*	2013–2018	− 3.1*
Geographic
Urban	−3.5*(− 5.6 ~ − 1.3)	2007–2010	− 1.3	2010–2013	−9.6*	2013–2018	−1.0
Rural	− 5.6*(−7.1 ~ − 4.0)	2007–2010	− 0.3	2010–2013	−13.7*	2013–2018	− 3.5*

Abbreviations: AAPC, the average annual percent change; APC, the annual percent change. * indicates that AAPC or APC significantly different from 0 (two-sided p < 0.05)

Table 4

Joinpoint analysis of trends in the premature mortality of four major NCDs in Nanjing (2007–2018)

	Total Study Period	Trend 1		Trend 2		Trend 3
	AAPC (%, 95 CI)	years	APC (%)	years	APC (%)	years	APC (%)
All	− 4.3*(− 5.3 ~ − 3.4)	2007–2010	0.1	2010–2013	−11.9*	2013–2018	− 2.2*
Subcategory
Cancers	−4.2*(− 5.9 ~ − 2.5)	2007–2010	− 0.2	2010–2013	− 11.8*	2013–2018	−1.7
Diabetes	−1.6(− 6.7 ~ 3.8)	2007–2009	6.3	2009–2013	− 12.4	2013–2018	4.7
Cardiovascular disease	− 5.0*(− 6.9 ~ − 3.2)	2007–2010	0.1	2010–2014	−11.4*	2014–2018	−2.2
Chronic respiratory diseases	−5.9*(− 6.9 ~ − 4.8)	2007–2018
Gender
Male	− 4.2*(− 6.0 ~ −2.5)	2007–2010	0.2	2010–2013	− 12.5*	2013–2018	− 1.4
Female	−4.6*(− 6.4 ~ − 2.7)	2007–2010	0.7	2010–2013	− 11.4*	2013–2018	− 3.4*
Geographic
Urban	−3.3*(−5.4 ~ −1.1)	2007–2010	− 0.5	2010–2013	−9.9*	2013–2018	−0.7
Rural	− 5.2*(− 6.8 ~ − 3.4)	2007–2010	0.2	2010–2013	− 12.9*	2013–2018	− 3.4*

Abbreviations: AAPC, the average annual percent change; APC, the annual percent change. * indicates that AAPC or APC significantly different from 0 (two-sided p < 0.05)

Trend in premature mortality of four major NCDs with different geographics

A downtrend in ASPMR in both urban and rural areas was observed. The ASPMR decreased by 3.5% from 209.9 to 138.8 per 100,000 people in urban areas, and decreased by 5.6% from 321.1 to 168.3 per 100,000 people in rural areas (Tables 2, 3). Joinpoint analysis showed a significantly decreasing trend of premature mortality by 3.3% per year in urban areas. As in rural areas, it decreased with the AAPC of − 5.2% (Table 4).

Prediction and analysis of joinpoint regression model

Table 5 presents the prediction of the premature mortality from four major NCDs to reach the “Healthy China 2030” target in Nanjing. It is possible to achieve the target from four major NCDs combined, with the relative reduction 28.6%. Among the subcategories, diabetes is the least likely to achieve the target, which even showed a 94.2% increase. Another disease failing to meet the target would be cancer, with a 24.7% relative reduction. Both CVD and chronic respiratory diseases shared a more than 30% relative reduction in premature mortality, with the latter showing the greater reduction at 61.0%.

Table 5

Observed AMPMR and premature mortality of four major NCDs in 2015 and predicted values for 2030 in Nanjing (2007–2018)

	AMPMR			premature mortality
	Observed 2015	Predicted 2030	Relative reduction (%)	Observed 2015	Predicted 2030	Relative reduction (%)
All	172	118.8	−30.9	10.3	7.3	− 28.6
Subcategory
Cancers	107.1	78.5	−26.7	6.6	4.9	−24.7
Diabetes	5.5	11.4	107.1	0.4	0.7	94.2
Cardiovascular disease	53.8	37.9	−29.7	3.4	2.4	−30.3
Chronic respiratory diseases	5.6	2.3	−59.5	0.4	0.2	−61.0
Gender
Male	229.2	171.7	−25.1	13.7	10.9	−21.0
Female	113.7	70.8	−37.7	6.9	4.1	−40.6
Geographic
Urban	148.9	126.3	−15.2	9.5	8.3	−12.8
Rural	192.5	110.7	−42.5	11.3	6.6	−41.2

PS: Relative reduction (%) = (projected premature mortality 2030-observed premature mortality 2015) / (observed premature mortality 2015)*100%

A difference was seen in the possibility to reach the target from four major NCDs for both gender and geographic. A relative reduction of 40.6% in females was projected, but only 21.0% in males. In urban areas, a slightly smaller reduction (12.8%) was projected compared to a greater than 30% reduction in rural areas.

Discussion

This study confirmed a significant downward trend on both ASPMR and premature mortality from four major NCDs combined from 2007 to 2018 in Nanjing, with a rapid declining since 2010. This finding was in line with the global trend (22 to 18%) and national trend (30.7 to 18%) scenarios [4, 13]. Previously published data indicated that the premature mortality of four major NCDs in Nanjing in 2015 was 10.3%, which was lower than the nation level (18.5%), Jiangsu Province (13.4%), Beijing (11.1%), Tianjin (12.9%), and Chongqing (16.0%, in 2016), but higher than Shanghai (8.4%). It was also comparable to the level of developed countries [1, 10, 11, 13, 20]. In this study, we found that premature mortality from NCDs in Nanjing has continuously declined since 2007. If this decline trend continues, it will be possible to reach the “Healthy China 2030” target.

The premature mortalities from four major NCDs in Nanjing in 2015 respectively, were all lower than the levels of national and some other regional [20‐24]. The premature mortalities of four major NCDs in western regions are higher than the eastern regions in China, and they are lower in the economically developed regions. Our results showed that the declines of the premature mortality rates from four major NCDs rank in following order: chronic respiratory diseases (AAPC -5.9%), CVD (AAPC − 5.0%), cancer (AAPC − 4.2%), and diabetes (AAPC − 1.6%), which is consistent with the national trend [13]. In general, the chronic respiratory diseases and CVD may reach the 2030 target by reducing the premature mortality rates by 30.3 and 61.0%, however, there would be 5.3% short of the target for cancer. In addition, it seems unlikely to reach the target for diabetes.

Inconsistent with the national data, our study indicated that the greatest contributor to premature mortality was cancer [13]. Multiple factors can contribute occurrence and development of malignant tumors, among them smoking and chronic infections are considered as the most important risk factors [25, 26]. Previous studies reported that the top 5 mortality rates caused by tumor in Nanjing were similar to the Jiangsu Province and the national data, with slight differences in order. The mortality rate of lung cancer ranks first, mainly due to tobacco smoking behavior, air pollution and other environmental factors including decoration, cooking oil fume [27].

In line with the global and the national level, CVD ranks first in mortality rates among all the deaths in Nanjing. The control of hypertension has a great impact on the reduction of premature cardiovascular death in the world. Smoking control was reported as an efficient way to reduction of early cardiovascular death in males, whilst obesity control can be critical to reduce early cardiovascular death in females [28].

For diabetes, joinpoint analysis showed a non-significant decrease in the premature mortality. It might be explained by the elevated incidence of diabetes [29]. In Jiangsu Province, mortality rate of diabetes is at a lower level among people younger than 55 years old, and it increases significantly after 55 [30]. Accompanied by lifestyle changes, long duration of sedentary behavior, unhealthy diet and obesity were all considered as risk factors to diabetes. Furthermore, diabetes increased the risk of CVD and premature death [31].

In general, chronic respiratory diseases mainly include tuberculosis, diffuse pulmonary fibrosis, chronic obstructive pulmonary disease (COPD), bronchiectasis, and bronchial asthma. The incidence, prevalence and the mortality of COPD in Jiangsu Province were higher than the global average [32, 33]. Genetic susceptibility, smoking, air pollution, occupational exposure, infection, socioeconomic conditions are all recognized risk factors for COPD [34].

Furthermore, the ASPMR of four major NCDs showed a downward trend in both males and females aged 30 to 70 during the study period. The premature mortality of males was about twice that of females, and the gap was consistent with that of Beijing and the national data [10, 13], but higher than that of the global average level [35]. The monitoring report of NCDs in Nanjing in 2017 showed the rates of smoking (37.2%), drinking (47.7%), obesity (11.1%), hypertension (28.7%) and diabetes mellitus (10.30%) in males were higher than those in females. It might explain the gender differences in premature mortality rates. The prediction suggested that by 2030, the premature mortality in females can reach the target, while that in males still have a 9% gap from the target.

The ASPMR of four major NCDs showed downward trends in urban and rural areas. The trend of premature mortality in rural areas was consistent with that in urban areas, but the decline range of probability was higher than that in urban areas. The prediction suggested that by 2030, the premature mortality in rural areas may reach the target, while that in urban areas may not yet reached. The higher mortality rates in rural areas might be due to lower education and income levels, tobacco or unhealthy habits and lack access to medical and health care services [36]. The development of non-communicable diseases can be influenced by social, environmental, behavioral, nutritional and clinical factors [37]. Therefore, to prevent premature mortality from four major NCDs, active interventions to major risk factors are needed such as early prevention, early detection and early treatment through health promotion and related programs.

It is widely acknowledged that early prevention is important to prevent and control the NCDs. Interventions should focus on risk factors that are modifiable, namely tobacco smoking, alcohol drinking, excessive salt intake, obesity, elevated blood pressure and elevated blood sugar [17]. Tobacco Surveillance in Nanjing showed that the current smoking rate among local residents over 15 years old was 23.8% in average, but one in two males were smokers. In addition, the smoking rate of people at 15–24 years old in China was significantly higher than that of other age groups [38]. Therefore, it is important to enhance the enforcement of existing tobacco control regulations, increase tobacco and alcohol taxes, and improve the ability of medical and health institutions to provide smoking cessation assistance to people desired to quit smoking. Furthermore, interventions can also be promoting healthy diet, providing supportive environment such as healthy theme parks, public sports facilities, and strengthening environmental protection and supervision. In order to reduce the prevalence of related chronic infections in the population, increasing vaccination against human papillomavirus (HPV) and hepatitis B virus (HBV) among adolescents and general population can be effective. Medical and health care institutions should be encouraged to carry out public health education and health promotion at the population level. At the same time, more researches are needed to eliminate or reduce potential risk factors that affect patients’ morbidity and death.

Early detection including establishing a screening and early diagnosis mechanism, and standardizing the treatment of early cases are urgently needed. Early lung cancer screening advocates the application of low-dose spiral CT, which can reduce the mortality rate of early lung cancer to 20% [39]. Moreover, we can learn from Japanese and Korean regarding using early gastric cancer screening technology to improve the 5-year relative survival rate for patients [40, 41]. Some early diagnosis and treatment of gastrointestinal cancer and other mature screening technologies is currently carrying out in high-risk groups in Jiangsu Province. However, limited financial funds, low-level screening technology, and poor awareness of screening among the masses are noted.

In order to substantially reduce CVD and respiratory disease mortality, early treatment is also required at first-level (e.g. district), regional, and specialist hospitals including appropriate and effective referrals and high-quality long-term care, for acute cardiovascular disease, acute exacerbations of asthma and chronic obstructive pulmonary disease, and acute complications of diabetes.

Limitation

According to the existing standards [16], the death cause monitoring data in Nanjing are real-time and reliable. However, this study was limited by a lack of comparison groups from other districts and counties. In addition, we didn’t perform separate analysis of the four types NCDs in males and females or in urban and rural areas. This may prevent us to a deep understanding of the differences in mortality. Another limitation was that although the data were subject to the three-level quality control, there might still be unavoidable underreporting and misclassification of diseases.

Conclusion

Our study indicated that the “Healthy China 2030” target of reducing the probability of premature mortality by 30% due to four major NCDs in Nanjing can potentially be achieved. Despite this, the total number of premature mortalities from NCDs is still large. For these regions with a low probability of early death and a rapid decline in the past, it may be challenging maintain the decline trend to achieve the target. Unhealthy lifestyle are still highly prevalent in the population despite the behavior change efforts made by public health practitioners to reduce such risk exposures in the past decades [42, 43]. To achieve further decline in the premature mortality, an integrated approach should be taken focusing on the modifiable risk factors across different sectors and disciplines in Nanjing. The out control of those risk factors will lead to an increased disease burden of NCDs [44]. Prevention and treatment of cancers and diabetes may require additional efforts, especially scientific researches, optimization, and promotion of appropriate technologies for early diagnosis and treatment, as well as strengthening pre-hospital first aid. The health management should be prioritized to key populations such as males and people in rural areas.

Acknowledgements

We would like to thank all the participants involved in the survey.

Declarations

Not applicable.

Competing interests

The authors declared that they have no conflicts of interest to this work.

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

World Health Organization. Noncommunicable diseases country profiles 2018. https://www.who.int/nmh/publications/ncd-profiles-2018/en/. Accessed 24 Sep 2018.

Confortin SC, Andrade SR, Draeger VM, Meneghini V, Schneider IJC, et al. Premature mortality caused by the main chronic noncommunicable diseases in the Brazilian states. Rev Bras Enferm. 2019;72(6):1588–94. https://doi.org/10.1590/0034-7167-2018-0701.CrossRefPubMed

Bonita R, Magnusson R, Bovet P, Zhao D, Malta DC, Geneau R, et al. Country actions to meet UN commitments on non-communicable diseases: a stepwise approach. Lancet. 2013;381(9866):575–84. https://doi.org/10.1016/S0140-6736(12)61993-X.CrossRefPubMed

World Health Organization. 65th world health assembly closes with new global health measures. Central Eur J Public Health. 2012;20(2):163–4 http://apps.who.int/iris/bitstream/10665/246223/1/9789241565363-eng.pdf?ua=1. .

World Health Organization. Assessing national capacity for the prevention and control of noncommunicable diseases: report of the 2015 global survey. http://apps.who.int/iris/bitstream/10665/246223/1/9789241565363-eng.pdf?ua=1. Accessed 1 Sep 2016.

World Health Organization. Global action plan for the prevention and control of noncommunicable diseases 2013–2020. https://apps.who.int/iris/bitstream/10665/94384/1/9789241506236_eng.pdf?ua=1. Accessed 1 Sep 2016.

Kontis V, Mathers CD, Rehm J, Stevens GA, Shield KD, Bonita R, et al. Contribution of six risk factors to achieving the 25×25 non-communicable disease mortality reduction target: a modelling study. Lancet. 2014;384(9941):427–37. https://doi.org/10.1016/S0140-6736(14)60616-4.CrossRefPubMed

United Nations. Sustainable development goals. Goal 3: Ensure healthy lives and promote well-being for all at all ages. http://www.un.org/sustainabledevelopment/health/. .

CPC Central Committee and State Council. Healthy China 2030 Plan. http://www.gov.cn/zhengce/2016-10/25/content_5124174.htm. Accessed 1 Nov 2016.

10.

Li G, Su JT, Wei ZH, Gao YL, Deng Y. Probability of premature mortality caused by non-communicable diseases in Beijing, 2010-2015 (in Chinese). Chin J Epidemiol. 2016;37(9):1268–71.

11.

Wang DZ, Zhang H, Xu ZL, Song GD, Zhang Y, Shen CF, et al. Trend of premature mortality from chronic and non-communicable diseases in Tianjin,1999–2015 (in Chinese). Chin J Epidemiol. 2017;38(12):1672–6.

12.

Cheng L, Tan L, Zhang L, Wei S, Liu L, Long L, et al. Chronic disease mortality in rural and urban residents in Hubei Province, China, 2008-2010. BMC Public Health. 2013;2(13):713. https://doi.org/10.1186/1471-2458-13-713.CrossRef

13.

Zeng XY, Li YC, Liu SW, Wang LJ, Liu YN, Liu JM, et al. Subnational analysis of probability of premature mortality caused by four main non-communicable diseases in China during 1990-2015 and "health China 2030"reduction target (in Chinese). Chin J Prev Med. 2017;51(3):209–14.

14.

Yang HF, Chen XP, Hong X, Wang ZY, Xu F. Influence of epidemiological trend of disease mortality on life expectancy among residents in Nanjing from 2011 to 2014 (in Chinese). Jiangsu J Prev Med. 2017;28(05):512–4.

15.

World Health Organization. International Statistical Classification of Diseases and Related Health Problems, 10th Revision. 2017. http://apps.who.int/classifications/icd10/browse/2010/en

16.

Wang LJ, Dong JW, Song GX, Wei ZH, Lin H, etc. Guidance manual for population death cause monitoring (in Chinese). Beijing: China Population Press, 2017:1–333.

17.

World Health Organization. Global status report on noncommunicable diseases 2014. http://www.who.int/nmh/publications/ncd-status-report-2014/en/. Accessed 9 Aug 2016.

18.

Clegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual per cent change in trend analysis. Stat Med. 2009;28(29):3670–82. https://doi.org/10.1002/sim.3733.CrossRefPubMedPubMedCentral

19.

Huriye T, Sinan S. Joinpoint regression analysis and an application on Istanbul stock-exchange. Alphanumeric Jouma. 2014;2(1):44–9.

20.

Ding XB, Tang WG, Mao DQ, Jiao Y, Shen ZZ. Analysis on probability of premature death and cause eliminated life expectancy of major non-communicable diseases in Chongqing municipality, 2016 (in Chinese). Chin J Prev Med. 2017;51(11):1033–7.

21.

Jiang XC, Lai SM, Jiang YM, Jiao SL, Wang SX. Analysis of deaths from cardiovascular and cerebrovascular diseases in Quzhou residents in 2018 (in Chinese). Cardio-Cerebrovasc Dis Prev Treat. 2020;20(5):518–20.

22.

Ding XB, Jiao Y, Mao DQ, Xu J, Lyu XY, Yang XX, et al. Changing trends in the probability of premature death due to diabetes in Chongqing municipality, 2010-2018 (in Chinese). Pract Prev Med. 2019;26(22):1284–8.

23.

Li ZL, Zheng XY, Xu XJ, Li LX, Xu YJ. Probability of premature mortality caused by chronic respiratory disease and its changing trend during 2005-2015 in Guangdong province (in Chinese). Med J Chin PLA. 2020;45(2):118–24.

24.

Liu J, Yan W, Liu M, Fan WM, Zhu LP, Zhao J, et al. Analysis on the probability of premature death from major chronic diseases in the death-cause monitoring areas of Jiangxi Province from 2014 to 2018 (in Chinese). Chinese Journal of Health Statistics. 2020;37(2):253–5.

25.

Katzke VA, Kaaks R, Kuhn T. Lifestyle and cancer risk. Cancer J. 2015;21(2):104–10. https://doi.org/10.1097/PPO.0000000000000101.CrossRefPubMed

26.

Hattori N, Ushijima T. Epigenetic impact of infection on carcinogenesis: mechanisms and applications. Genome Medicine. 2016;8(1):10. https://doi.org/10.1186/s13073-016-0267-2.CrossRefPubMedPubMedCentral

27.

Zhou HR, Yang HF, Chen XP, Hong X. Analysis on disease burden of malignant tumor in residents of Nanjing from 2007 to 2018 (in Chinese). Chin J Prev Contr Chron Dis. 2020;28(5):350–4.

28.

Roth GA, Nguyen G, Forouzanfar MH, Mokdad AH, Naghavi M, Murray CJL. Estimates of global and regional premature cardiovascular mortality in 2025. Circulation. 2015;132(13):1270–82. https://doi.org/10.1161/CIRCULATIONAHA.115.016021.CrossRefPubMed

29.

Diabetes Branch of Chinese Medical Association. Guidelines for the prevention and treatment of type 2 diabetes in China (2017 edition) (in Chinese). Chin J Diabetes Mellitus. 2018;38(04):292–344.

30.

Chen YJ. Analysis of diabetes mortality trend in Jiangsu province, and association between physical activity, sedentary behavior and sleep duration with risk of mortality in type 2 diabetic patients (in Chinese). Nanjing, Jiangsu: Southeast University. 2019:1–103.

31.

Yan S, Li J, Li S, Zhang B, Du S, Gordon-Larsen P, et al. The expanding burden of cardiometabolic risk in China: the China health and nutrition. Obes Rev. 2012;13(9):810–21. https://doi.org/10.1111/j.1467-789X.2012.01016.x.CrossRefPubMedPubMedCentral

32.

GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1736–88.CrossRef

33.

GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858.CrossRef

34.

Aspiration Society of Chinese Medical Association, Chronic Obstructive Pulmonary Disease Group. Guidelines for diagnosis and treatment of chronic obstructive pulmonary disease (2013 revision) (in Chinese). Chin J Front Med Sci: Electr Ver. 2014;6(2):67–80.

35.

Kontis V, Mathers CD, Bonita R, Stevens GA, Rehm J, Shield KD, et al. Regional contributions of six preventable risk factors to achieving the 25 × 25 non-communicable disease mortality reduction target: a modelling study. Lancet Glob Health. 2015;3(12):e746–57.CrossRef

36.

World Health Organization. Noncommunicable diseases. https://www.who.int/en/news-room/fact-sheets/detail/noncommunicable-diseases. Accessed 1 Jun 2018.

37.

GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioral, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1659–724.CrossRef

38.

Wang MH, Luo X, Xu SB, Liu WH, Ding FF, Zhang XX, et al. Trends in smoking prevalence and implication for chronic diseases in China: serial national cross-sectional surveys from 2003 to 2013. Lancet Resp Med. 2019;7(1):35–45. https://doi.org/10.1016/S2213-2600(18)30432-6.CrossRef

39.

National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung -cancer mortality with low-dose computed tomographic screening. N Engl J Med. 201; 365(5):395–409.

40.

Jung KW, Won YJ, Kong HJ, Lee ES. Cancer statistics in Korea:incidence, mortality, survival, and prevalence in 2016. Cancer Res Treat. 2019;51(2):417–30. https://doi.org/10.4143/crt.2019.138.CrossRefPubMedPubMedCentral

41.

Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3):analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018;391(10125):1023–75. https://doi.org/10.1016/S0140-6736(17)33326-3.CrossRefPubMedPubMedCentral

42.

Li Y, Zhang M, Jiang Y, Wu F. Co-variations and clustering of chronic disease behavioral risk factors in China: China chronic disease and risk factor surveillance, 2007. PLoS One. 2012;7(3):e33881. https://doi.org/10.1371/journal.pone.0033881.CrossRefPubMedPubMedCentral

43.

Li Y, Wang L, Jiang Y, Zhang M, Wang L. Risk factors for noncommunicable chronic diseases in women in China: surveillance efforts. Bull World Health Organ. 2013;91(9):650–60. https://doi.org/10.2471/BLT.13.117549.CrossRefPubMedPubMedCentral

44.

Li YC, Zeng XY, Liu JM, Liu YN, Liu SW, Yin P, et al. Can China achieve a one-third reduction in premature mortality from non-communicable diseases by 2030? BMC Med. 2017;15(1):132. https://doi.org/10.1186/s12916-017-0894-5.CrossRefPubMedPubMedCentral

Titel: ‘Trend in premature mortality from four major NCDs in Nanjing, China, 2007–2018’
verfasst von: Huafeng Yang
Yali Fu
Xin Hong
Hao Yu
Weiwei Wang
Fengxia Sun
Jinyi Zhou
Nan Zhou
Publikationsdatum: 01.12.2021
Verlag: BioMed Central
Erschienen in: BMC Public Health / Ausgabe 1/2021
Elektronische ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-021-12018-7

Springer Medizin

Abstract

Background

Methods

Results

Conclusion

Publisher’s Note

Background

Methods

Data collection

Statistical analysis

Result

Trend in premature mortality of four major NCDs in different subcategories

Trend in premature mortality of four major NCDs with different genders

Trend in premature mortality of four major NCDs with different geographics

Prediction and analysis of joinpoint regression model

Discussion

Limitation

Conclusion

Acknowledgements

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Publisher’s Note

Weitere Artikel der Ausgabe 1/2021

Facilitators and barriers to antiretroviral therapy adherence among HIV-positive adolescents living in Tanzania

Perceptions, behaviours and attitudes towards smoking held by the male partners of Chinese pregnant women: a qualitative study

Evaluating the effectiveness and cost effectiveness of the ‘strengthening families, strengthening communities’ group-based parenting programme: study protocol and initial insights

Indoor nocturnal noise is associated with body mass index and blood pressure: a cross-sectional study

Correction to: Implementation of nationwide screening of pregnant women for HTLV-1 infection in Japan: analysis of a repeated cross-sectional study

Impact of COVID-19 on health-related behaviours, well-being and weight management