Trends in Tuberculosis Mortality among the Elderly in China, 2004-2021: A Jointpoint Regression and Age-Period-Cohort Analysis

doi:10.21203/rs.3.rs-4703738/v1

Download PDF

Research Article

Trends in Tuberculosis Mortality among the Elderly in China, 2004-2021: A Jointpoint Regression and Age-Period-Cohort Analysis

https://doi.org/10.21203/rs.3.rs-4703738/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Tuberculosis (TB) has long placed a heavy burden on society and healthcare systems in China and worldwide. With the aging of population, TB in the elderly is becoming a significant public health concern. This study examined secular trends in TB mortality among the elderly in China and the net effects of age, period, and cohort.

Methods

The data were obtained from the National Disease Surveillance Points (DSPs) system. The joinpoint regression model was used to calculate the annual percentage change in TB mortality among people aged 60 years and over in China from 2004 to 2021. The age-period-cohort (APC) analysis based on the intrinsic estimator (IE) method was utilized to estimate the independent effects of age, period and cohort.

Results

The age-standardized mortality rate of TB was 5.68 per 100,000, with higher rates for males than for females, in rural areas than in urban areas, and in western than in central and eastern regions. TB mortality among the elderly in China showed a continuous downward trend in different demographic groups from 2004 to 2021, with a slower rate of decline in recent years. The APC analysis model showed that TB mortality among the elderly in China increased with age, with the RR increasing from 0.57 in the 60–64 age group to 1.53 in the 80–84 age group. The period effect on TB mortality among the elderly in China continuously decreased from 2007 to 2021. Stratified results showed that the risk effect was higher in rural areas (RR = 1.51, 95% CI: 1.47–1.54) than in urban areas (RR = 1.16, 95% CI: 1.08–1.25) for the period 2007–2011 and higher in urban areas (RR = 0.81, 95% CI: 0.75–0.88) than in rural areas (RR = 0.73, 95% CI: 0.71–0.75) for the period 2017–2021. The cohort effect on TB mortality presented a continuously decreasing trend, with the exception of certain demographic groups that tended to increase in the 1952–1956 and 1957–1961 birth cohorts.

Conclusions

TB mortality among the elderly continued to decline in China from 2004 to 2021, although the rate of decline had slowed in recent years. There were differences in age, period and cohort effects on TB mortality among gender, urban and rural areas and regions. This study revealed the temporal trend of TB mortality among the elderly in China and provided clues for etiologic studies, which in turn can help develop effective intervention strategies.

tuberculosis

mortality

elderly

joinpoint regression

age-period-cohort analysis

Tuberculosis (TB), a chronic infectious disease caused by infection with Mycobacterium tuberculosis, is one of the major infectious diseases in the world. The World Health Organization (WHO) reported that in 2022 alone, an estimated 10.6 million people developed TB, and 1.3 million people died from the disease^[1]. In China, TB is also a serious threat to public health and is classified as a category II notifiable disease. Despite ongoing efforts and progress, China continues to have one of the highest TB burdens in the world, ranking third in the world in several TB burden indicators^[2]. Additionally, in 2022, a total of 748,000 new TB cases and 30,000 deaths were reported in China, for an incidence of 35.0 per 100,000 and a mortality of 2.0 per 100,000 ^[1].

It is widely acknowledged that the elderly are particularly susceptible to developing and succumbing to TB diseases^[3–5]. With increasing longevity and declining fertility rates, the aging of the population is accelerating^[6], and TB in the elderly is becoming a major problem in many countries^[7]. According to estimates from WHO, the proportion of population aged 60 years and over in China is expected to increase from 12.4% in 2010 to 28.0% in 2040^[8]. The reported incidence of TB in China's elderly was 2 to 3 times that of young people^[9], and those aged 60 years and over are a risk group for TB mortality^[10]. Building a TB-free world is a goal of the WHO End TB Strategy. The key targets include a 75% reduction in TB deaths and a 50% reduction in TB incidence by 2025, and a 90% reduction in deaths and an 80% reduction in incidence by 2030, compared with 2015^[11]. To reach the goals of the WHO End TB Strategy by 2030, China still has a long way to go^[12]. Identifying the distribution of TB and high-risk groups in the Chinese elderly population is significant for TB prevention and control strategies.

Therefore, this study utilized joinpoint regression to describe the trends in TB mortality among the elderly in China. Compared with previous TB mortality studies in China among all age groups^{[10, 13]}, this study focused on the elderly population and analyzed the differences by gender, residences and regions, which is lacking in previous research papers. Furthermore, this study aimed to estimate age, period and cohort effects on TB mortality to identify high-risk groups among the elderly in China using an age-period-cohort (APC) model.

2.1 Data Source

Data on TB mortality among people aged 60 years and over in China were obtained from the National Disease Surveillance Points (DSPs) system. This population-based death registration system initially collected data only from “unrepresentative surveillance points”, then gradually expanded to 161 surveillance points across 31 provinces in 2004 and 605 points in 2013, covering 323.8 million people (24.3% of the country’s total population). TB was defined by the International Classification of Diseases, 10th Revision, codes A15-A19 or B90.

This study used aggregate annual mortality from 2004 to 2021, grouped by gender (male and female), age (60–64, 65–69, 70–74, 75–79, 80–84, and 80+), residence (urban and rural areas), and region (eastern, central and western). All counties (including county-level cities) were defined as rural, and all districts were defined as urban. The three regions were divided according to the National Bureau of Statistics: the eastern region of China included Beijing, Tianjin, Shanghai, Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central region included Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan; and the western region included Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Inner Mongolia, Guangxi, Xizang, Ningxia, and Xinjiang. The 2010 census population of China was used to determine the age-standardized mortality rate (ASMR).

2.2 Statistical Analysis

2.2.1 Joinpoint Regression

The joinpoint regression model^[14] was used to evaluate the time trends in TB mortality among people aged 60 years and over in China from 2004 to 2021. This model can divide the longitudinal variations into different segments by piecewise regression and identify the segment trends with statistical significance. Regression fitting was performed on the natural logarithm of the mortality rate in different segments, and then the annual percentage change and its 95% confidence interval (CI) were calculated for each period^[15]. The annual percentage change and the average annual percentage change were the main indicators used to describe the temporal variations in the joinpoint regression model. And they were considered statistically significant when compared to 0 with values of P < 0.05.Joinpoint (version 5.0.2; National Cancer Institute, Calverton, MD, USA) was used to create this model.

2.2.2 Age-Period-Cohort Analysis

An age-period-cohort (APC) model was applied to assess the impact of age, period, and cohort effects on TB mortality among the elderly in China from 2007 to 2021. The APC model has been commonly used in sociology and epidemiology for several decades. However, when the APC model was proposed, it was difficult to calculate the specific effects of age, period and cohort because there is a linear correlation between these three variables (cohort = period-age), also called a non-identification problem^[16]. To solve this problem, the intrinsic estimator (IE) method was proposed by Yang and Fu to distinguish these three effects and provide unbiased and relatively efficient estimation results^[17].

In this study, the age effect referred to the differences in TB mortality across age groups caused by aging-related factors. The period effect referred to the influence of human factors on TB mortality, such as medical technology. The cohort effect referred to the change in TB mortality due to different exposures to risk factors among people of different birth years.

In the APC model with the IE method, the model requires equal intervals for age, period, and cohort, and TB mortality was calculated for each 5-year age group (60–64, 65–69, 70–74, 75–79, and 80–84). Hence, the period and cohort were also divided with 5-year intervals into 3 periods (2007–2011, 2012–2016, and 2017–2021), and 7 cohorts (1927–1931, 1932–1936, 1937–1941, 1942–1946, 1947–1951, 1952–1956, and 1957–1961). In particular, data for the 80 + age group or for the years 2004–2006 were not included in the APC analysis considering the model conditions. Due to the increase in monitoring centers from 161 to 605 in 2013, the number of TB deaths in different years was weighted differently in the period 2012–2016. In calculating TB mortality for the period 2012–2016, we used the average of the latter four years in place of the data for 2012. Because neighboring birth cohorts partially overlap, the birth cohorts are usually described by their middle year^[18]. For subjects aged 75–79 and 80–84 years from 2007 to 2011, their birth cohorts were from 1928 to 1936 and from 1923 to 1931, respectively. They were denoted as from 1932 to 1936 and from 1927 to 1931. The APC model could be expressed as follows^[10]:

Y = log(R) = µ₀ + α × age_A + β × period_P + γ × cohort_C + ε

where R stands for TB mortality of different groups; α, β and γ denote the coefficients of age, period and cohort of the APC model; µ₀ and ε represent the intercept item and residual. These model coefficients (α, β, γ) were calculated to the exponential value (e^coef.) which denotes the mortality relative risk (RR) of a particular age, period, or birth cohort relative to each average level. The analyses presented in this study were conducted with the APCG1 package in R software (version 3.5.0; The R Foundation for Statistical Computing, Vienna, Austria).

3.1 Joinpoint Regression Analysis of TB Mortality

Trends in the crude mortality rate (CMR) and age-standardized mortality rate (ASMR) in the elderly by genders, urban and rural areas, and regions from 2004 to 2021 were presented in Table 1. In 2021, there were 3,240 TB deaths among the nearly 55 million surveilled population aged 60 and over, with 2,420 male and 820 female patients. The CMR of TB in the elderly was 5.90 per 100,000, and the ASMR of TB was 5.68 per 100,000, with higher rates for males (9.15 per 100,000) than for females (2.65 per 100,000), in rural areas (6.18 per 100,000) than in urban areas (4.70 per 100,000), and in western regions (9.87 per 100,000) than in central (5.12 per 100,000) and eastern (3.73 per 100,000) regions. The ASMR showed a significant decline with an average annual percentage change (AAPC) of -10.00% (P < 0.001), and decreased significantly across various gender, urban and rural, and regional categories from 2004 to 2021. The largest reduction in ASMR was observed in the female group, with an AAPC of -11.32% (p < 0.001), while the smallest reduction was observed in urban areas, with an AAPC of -8.30% (p < 0.001).

Table 1

Variation in TB mortality among people aged 60 years and over by gender, urban and rural areas, and region in China, 2004‒2021
Year	Male		Female		Urban		Rural		Eastern		Central		Western		Overall
Year	CMR	ASMR	CMR	ASMR	CMR	ASMR	CMR	ASMR	CMR	ASMR	CMR	ASMR	CMR	ASMR	CMR	ASMR
2004	45.48	47.60	20.53	20.15	18.60	19.09	40.75	40.85	18.42	18.07	30.68	31.90	56.98	57.71	32.68	32.92
2005	49.01	51.23	22.11	21.55	19.35	19.66	44.35	44.51	19.97	19.48	33.40	34.78	61.07	62.28	35.21	35.44
2006	31.14	32.44	12.04	11.77	12.91	13.07	26.13	26.13	16.13	15.69	20.96	21.81	30.31	30.46	21.32	21.36
2007	27.80	29.08	10.39	10.13	11.80	11.77	23.16	23.27	14.25	13.78	16.89	17.52	28.88	29.18	18.85	18.90
2008	25.85	26.64	8.93	8.68	10.12	9.96	21.87	21.91	12.89	12.44	14.97	15.25	26.95	27.48	17.19	17.13
2009	24.66	26.79	8.23	8.31	9.73	9.92	20.51	21.47	11.65	11.68	13.80	14.76	26.70	28.06	16.18	16.74
2010	21.98	25.20	8.56	8.84	9.77	10.59	18.87	20.22	9.61	10.03	12.03	13.24	27.97	30.82	15.05	16.16
2011	20.36	22.95	7.57	7.63	8.60	9.62	17.02	17.50	7.49	7.79	10.44	10.97	28.70	30.86	13.65	14.31
2012	18.87	19.52	8.02	7.85	7.97	7.77	16.66	16.72	7.45	7.12	9.47	9.66	28.18	28.57	13.24	13.19
2013	15.93	16.51	5.71	5.58	8.26	8.11	11.85	12.02	7.00	6.88	9.17	9.43	19.44	19.53	10.71	10.77
2014	14.64	15.08	4.75	4.61	7.97	7.85	10.34	10.42	6.50	6.33	9.16	9.35	15.70	15.73	9.59	9.61
2015	14.58	15.07	5.31	5.19	8.28	8.15	10.58	10.69	7.10	6.92	8.29	8.43	16.34	16.40	9.85	9.86
2016	13.75	13.86	5.13	4.85	7.94	7.62	9.98	9.81	6.69	6.31	7.26	7.17	16.46	16.23	9.33	9.09
2017	13.39	13.45	4.49	4.24	7.16	6.91	9.62	9.43	6.32	5.92	6.86	6.73	16.14	16.04	8.82	8.59
2018	12.88	12.81	4.68	4.35	6.95	6.61	9.53	9.23	6.21	5.73	6.53	6.34	16.14	15.90	8.67	8.34
2019	11.71	12.53	3.79	3.82	6.44	6.53	8.25	8.61	5.48	5.50	6.41	6.76	13.01	13.70	7.63	7.89
2020	10.40	10.88	3.12	3.04	5.57	5.45	7.22	7.34	5.00	4.86	5.72	5.84	10.68	10.93	6.63	6.67
2021	9.16	9.15	2.87	2.65	4.99	4.70	6.36	6.18	3.94	3.73	5.29	5.12	10.20	9.87	5.90	5.68
AAPC(%)	-9.16	-8.69	-11.18	-11.32	-7.86	-8.30	-9.97	-9.93	-9.12	-9.24	-10.33	-10.81	-8.86	-9.01	-8.189	-10.00
t	-8.760	-9.621	-6.220	-6.332	-6.987	-6.020	-9.409	-10.401	-8.668	-8.206	-11.807	-9491	-12.468	-12.447	-11.326	-7.355
P	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001

*CMR: crude mortality rate, ASMR: age-standardized mortality rate, AAPC: average annual percentage change.

Table 2 showed the temporal variation in age-specific TB mortality from 2004 to 2021. There were significant downward trends in all age groups of the elderly. The greatest reductions were observed in the 65–69 and 60–64 age groups, with AAPCs of -11.33% (P < 0.001) and − 10.56% (P < 0.001), respectively.

Table 2

Age-specific TB mortality variation with time (per 100,000), 2004–2021
Year	60–64	65–69	70–74	75–79	80–84	85–89
2004	17.66	27.14	38.34	46.74	62.49	63.22
2005	18.51	27.42	42.68	54.10	60.20	76.11
2006	11.64	16.55	27.36	30.91	36.21	40.34
2007	10.21	14.44	21.15	27.75	39.45	36.39
2008	8.56	13.55	20.39	28.12	30.34	30.71
2009	7.03	13.07	18.44	25.05	31.73	51.63
2010	6.90	12.30	15.74	22.34	32.31	62.45
2011	7.72	10.08	14.02	20.82	26.57	47.41
2012	6.88	10.06	15.08	21.34	23.26	27.32
2013	5.12	9.02	12.92	15.89	19.48	23.00
2014	4.38	7.76	11.42	15.45	18.21	18.60
2015	4.48	8.20	10.75	15.59	18.64	23.22
2016	4.69	7.10	9.61	13.48	17.29	23.33
2017	4.69	7.04	8.92	12.11	16.09	21.31
2018	4.54	6.61	8.81	11.60	14.97	23.11
2019	3.08	5.28	8.61	13.39	15.37	25.58
2020	2.96	4.52	6.90	10.75	14.56	19.14
2021	2.58	3.87	5.99	9.12	12.04	16.02
AAPC (%)	-10.56	-11.33	-10.54	-8.74	-8.51	-6.93
t	-5.857	-7.696	-9.335	-14.536	-9.682	-6.534
P	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001

3.2 The Variation of Age, Period, and Cohort on TB Mortality

Figure 1 showed the changes in TB mortality by age, period, and cohort from 2007 to 2021. In all three time periods, 2007–2011, 2012–2016 and 2017–2021, TB mortality among the elderly in China increased with age, as shown in Fig. 1A. Figure 1B showed the trends in TB mortality for different age groups from 2007 to 2021. TB mortality decreased in all age groups from 2007 to 2021, which is consistent with the results shown in Table 2. The downward trends were more pronounced before the 2012–2016 period than after it. Figure 1C showed the cohort-based variation in age-specific TB mortality. The later birth cohorts all had lower mortality than the earlier cohorts in the same age group. The decline in TB mortality was more moderate in later birth cohorts than in earlier cohorts.

3.3 Age-Period-Cohort (IE) Analysis of TB Mortality

3.3.1 Age effect

After controlling for period and cohort effects, the single age effect on TB mortality among the elderly in China was shown in Fig. 2. The risk of dying from TB increased with age, with the RR increasing from 0.57 in the 60–64 age group to 1.54 in the 80–84 age group. The age effects stratified by gender, urban and rural areas, and regions also showed upward trends as the overall effect, with a faster increase for males than for females, in urban areas than in rural areas, and in eastern regions than in central and western regions.

3.3.2 Period effect

Figure 3 showed that after controlling for age and cohort effects, the RR of the period effect on TB mortality among the elderly in China continuously decreased from 2007 to 2021, with the highest risk occurring in the period of 2007–2011 (RR = 1.40, 95% CI: 1.36–1.43) and the lowest risk occurring in the period of 2017–2021 (RR = 0.76, 95% CI: 0.74–0.78). After stratification, the period effects in different gender and region groups were similar to the overall. As shown in Fig. 3B, the risk effect was higher in rural areas (RR = 1.52, 95% CI: 1.49–1.56) than in urban areas (RR = 1.16, 95% CI: 1.06–1.26) for the period 2007–2011, while it was higher in urban areas (RR = 0.81, 95% CI: 0.74–0.89) than in rural areas (RR = 0.74, 95% CI: 0.72–0.76) for the period 2017–2021.

3.3.3 Cohort effect

After controlling for age and cohort effects, Fig. 4 showed the cohort effect, which indicated that the later the cohort was born, the lower the RR of TB mortality. The highest RR of the cohort effect on TB mortality was for the cohort 1927–1931 (RR = 1.36, 95% CI: 1.29–1.43), and the RR decreased to the lowest value for the cohort 1957–1961 (RR = 0.74, 95% CI: 0.69–0.80). In fact, the period effect for those born after 1947 was no longer higher than the overall average. Stratified analyses revealed that the cohort effects for females, rural areas and western regions also followed downward trends as the overall. In contrast, the risk in the male, eastern and central China groups rebounded after being minimized in the 1952–1956 cohort. Similarly, the RR of the cohort effect in urban areas decreased from the 1927–1931 cohort, with the lowest risk in the 1947–1951 cohort (RR = 0.77, 95% CI: 0.67–0.88), and then increased until the 1957-1961 cohort.

This study revealed a statistically significant decline in TB mortality among the elderly in China from 2004 to 2021. This finding was consistent with other studies in China^[13] and the global trend^{[1, 19]}. Economic prosperity, leading to improved socioeconomic conditions and living standards (such as better housing and nutrition), contributes to the continued decline in mortality^[20]. It may be benefiting from a series of interventions and policies that have been put in place by the government. For instance, China actively promoted the process of controlling the disease, with the full coverage of DOTS strategy being reached around 2004^[21]. TB mortality declined at a slower rate in recent years, and the trend was also found in TB incidence^[18]. TB mortality varied between gender and age groups, residences and regions. The study revealed that TB mortality was higher among males, in rural areas, and in the western region. These findings were consistent with previous studies^{[13, 22–25]}. The higher TB mortality among males may be attributed to factors such as increased participation in social activities^[26], high labor intensity^[26], excessive smoking^[27] and alcohol consumption^[28], poor resistance^[29], and incomplete antituberculosis treatment^[26]. The higher mortality in rural areas and the western region could be due to lower levels of economic development and accessibility of healthcare services^[30]. Patients with higher socioeconomic status are more likely to receive superior treatment and additional diagnostic procedures^[31]. We also found different decline rates of TB mortality after stratification by gender, residences and regions.

Due to the intricate interactions among age, period, and cohort factors, we applied the age-period-cohort model and IE algorithm to quantify their net effects on TB mortality. The age effect on TB mortality increased with advancing age. The higher TB mortality among older people may be due to waning immunity and increased comorbidities^[32]. Additionally, the older people usually experience a worse prognosis and are often under-treated^[33]. Our study showed that compared with female group, aging may have a greater impact on increasing the RR of TB mortality in males. A similar finding for TB incidence was published by Li^[34]. Furthermore, this study showed that aging increased the RR of TB mortality in urban areas more than in rural areas, and in eastern regions more than in central and western regions. This was in contrast to the order of TB mortality among the different groups, but the results were not in conflict. This phenomenon can be attributed to TB deaths in rural areas and western regions occurring in relatively younger age groups. Poor economic and educational levels, limited nutrition and insufficient health knowledge affect the survival time of TB patients^[35].

The period effect can be influenced by a set of environmental, historical, and economic factors. According to the age-period-cohort analysis, TB mortality decreased with period, consistent with the results of the joinpoint regression model and previous results^{[25, 36]}. Intriguingly, the rate of decline was more pronounced in rural areas than in urban areas. This can be attributed to fast-paced economic growth, enhanced healthcare coverage (especially in rural areas), and rigorous respiratory TB control policies^[13]. This finding was contrary to the urban-rural difference in TB incidence. Some studies found that the incidence of TB decreased more significantly in urban than in rural areas in recent years^{[18, 37]}. Moreover, the downward trend after 2012–2016 was not as pronounced as it was before 2012–2016, which reminded us that more attention should be paid to innovative research.

The cohort effect reflected influencing factors that arise earlier in life course and accumulate with life course. The cohort effect on TB mortality showed continuously decreasing trends from the 1927–1931 birth cohort to the 1957–1961 birth cohort, with the exception of certain demographic groups that exhibited a tendency to elevate at the last one or two birth cohorts. The increase in TB mortality among individuals from the 1952–1956 birth cohort and the 1957–1961 birth cohort possibly was related to their early-life exposure to malnutrition. Malnutrition had been demonstrated to be a significant risk factor for TB^{[38, 39]}, and a study found that prenatal and early-life exposure to malnutrition may increase the risk of TB in the exposed generation and their offspring^[40].

The strength of our study is the nationwide nature of the data and further analysis by gender, urban and rural, and regional categories. The difference between this study and previous national study^[13] was that we used data on TB mortality from the National Disease Surveillance Points (DSPs) system instead of the National Notifiable Disease Reporting System (NNDRS) ^[41]. TB mortality from the NNDRS reflects patients who received treatment instead of all patients, and is defined as death during treatment from any cause. TB mortality in the DSPs is determined by the physician as the cause of death is from TB. However, this study also has several limitations. First, under-reporting of TB deaths should be noted. The data in the DSPs were collected from routine surveillance records. The number of deaths from TB in China may be higher than reported. However, the DSPs system is the only national mortality surveillance system that covers all causes of death in China. And the system has expanded its surveillance population from 6–24% of the Chinese population since 2013, covering almost a quarter of the population. It is the only feasible option for obtaining reliable and valid information on TB mortality in the country. Second, with the increase in monitoring centers from 161 to 605 in 2013, there might be some inconsistency in data collection. Although we used the average of the latter four years in place of the data for 2012 when calculating TB mortality for the period 2012–2016, the findings of this study should still be treated with caution. Third, in addition to gender, urban and rural areas, and regions (eastern, central, and western), factors such as economic levels and comorbidities (such as HIV and diabetes) were not included in the analysis. These factors would also influence the epidemic state and control of TB, and should be considered in future studies.

The overall TB mortality among the elderly in China declined from 2004 to 2021, but the extent of the reduction varied among different demographic groups. The age effect on TB mortality presented a significantly increasing trend in older people. The period effects declined, with smaller decreases in recent years. Further efforts should be made to improve the surveillance, diagnosis, treatment and management of the disease. The rate of decline was more pronounced in rural areas than in urban areas. The birth cohorts exhibited a general decline in TB mortality, although there were upward trends observed in the most recent 1952–1956 and 1957–1961 birth cohort cohorts.

Tuberculosis

DSPs

Disease Surveillance Points

APC

Age-period-cohort

Intrinsic estimator

Relative risk

Confidence interval

WHO

World Health Organization

CMR

Crude mortality rate

ASMR

Age-standardized mortality rate

AAPC

Average annual percentage change

DOTS

Directly observed treatment of short-course strategy

NNDRS

National Notifiable Disease Reporting System

HIV

Human immunodeficiency virus.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets analyzed during this study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was funded by National Key R&D Program of China (2022YFC3603002).

Authors' contributions

M-D Z analyzed and interpreted the data, wrote the manuscript; X W, Y-R X, X-M G, W-S S provided support in data management and analysis; Q-Q W, F H, X R, J-Q D, Q J designed the study, provided the idea, revised the manuscript; J-J L, W-J Z, H-Y Y oversaw the conception, writing, formatting and editing. All authors read and approved the final manuscript.

Acknowledgements

We extend our gratitude to the staff of the Center for Disease Control and Prevention at different levels across China for their invaluable assistance in the field investigation, administration, and data collection.

Organization WH. Global tuberculosis report 2023 [R]. Geneva: World Health Organization, 2023.
Zhao Y, Liu J. Facing the Challenge of Tuberculosis: Towards "End TB in China by 2035". China CDC weekly, 2021, 3(12): 243-6.
SCHAAF HS, COLLINS A, BEKKER A, DAVIES PDO. Tuberculosis at extremes of age. 2010, 15(5): 747-63.
Asokan S. Immune issues in elderly with TB. Indian Journal of Tuberculosis, 2022, 69: S241-S5.
Ding C, Wang ST, Shangguan YW, Feng XW, Guo WR, Shi P, et al. Epidemic Trends of Tuberculosis in China from 1990 to 2017: Evidence from the Global Burden of Disease Study. Infection and Drug Resistance, 2020, 13: 1663-72.
Beard JR, Officer A, de Carvalho IA, Sadana R, Pot AM, Michel JP, et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet (London, England), 2016, 387(10033): 2145-54.
World Health O. Global tuberculosis control : surveillance, planning, financing : WHO report 2008 [Z]. Geneva; World Health Organization. 2008
World Health O. China country assessment report on ageing and health [M]. Geneva: World Health Organization, 2015.
Cheng J, Sun Y-N, Zhang C-Y, Yu Y-L, Tang L-H, Peng H, et al. Incidence and risk factors of tuberculosis among the elderly population in China: a prospective cohort study. Infectious Diseases of Poverty, 2020, 9(1).
Wang L, Wang W. Temporal Trends in Notification and Mortality of Tuberculosis in China, 2004-2019: A Joinpoint and Age-Period-Cohort Analysis. International Journal of Environmental Research and Public Health, 2021, 18(11).
World Health O. The end TB strategy [R]. Geneva: World Health Organization, 2015.
Long Q, Guo L, Jiang W, Huan S, Tang S. Ending tuberculosis in China: health system challenges. The Lancet Public Health, 2021, 6(12): e948-e53.
Zhao Y, Su B, Tian Y, Shao RJCCW. Trends and Patterns of Respiratory Tuberculosis Mortality in Rural and Urban — China, 1987–2021. 2023, 5: 938 - 42.
Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Statistics in medicine, 2000, 19(3): 335-51.
Zhu B, Wang Y, Zhou W, Jin S, Shen Z, Zhang H, et al. Trend dynamics of gout prevalence among the Chinese population, 1990-2019: A joinpoint and age-period-cohort analysis. Front Public Health, 2022, 10: 1008598.
Mason KO, Mason WM, Winsborough HH, Poole WK. Some Methodological Issues in Cohort Analysis of Archival Data. American Sociological Review, 1973, 38(2): 242-58.
Yang Y, Schulhofer‐Wohl S, Fu Wenjiang J, Land Kenneth C. The Intrinsic Estimator for Age‐Period‐Cohort Analysis: What It Is and How to Use It. American Journal of Sociology, 2008, 113(6): 1697-736.
Dong Z, Wang QQ, Yu SC, Huang F, Liu JJ, Yao HY, et al. Age-period-cohort analysis of pulmonary tuberculosis reported incidence, China, 2006-2020. Infect Dis Poverty, 2022, 11(1): 85.
Kibuuka D, Mpofu C, Neave P, Manda S. A Spatial Analysis of Tuberculosis Related Mortality in South Africa. Int J Environ Res Public Health, 2021, 18(22).
Singh H, Rupal A, Al Omari O, Jani C, Ahmed A, Khaliqdina S, et al. Trends in pulmonary tuberculosis mortality between 1985 and 2018: an observational analysis. BMC Pulmonary Medicine, 2023, 23(1): 184.
Liu SY, Bi YJ, Liu YW. Modeling and dynamic analysis of tuberculosis in mainland China from 1998 to 2017: the effect of DOTS strategy and further control. Theoretical Biology and Medical Modelling, 2020, 17(1).
Xie Y, Han J, Yu W, Wu J, Li X, Chen H. Survival Analysis of Risk Factors for Mortality in a Cohort of Patients with Tuberculosis. Canadian Respiratory Journal, 2020, 2020: 1654653.
Zenebe T, Tefera E. Tuberculosis treatment outcome and associated factors among smear-positive pulmonary tuberculosis patients in Afar, Eastern Ethiopia: a retrospective study. The Brazilian Journal of Infectious Diseases, 2016, 20(6): 635-6.
Chidambaram V, Tun NL, Majella MG, Castillo JR, Ayeh SK, Kumar A, et al. Male Sex Is Associated With Worse Microbiological and Clinical Outcomes Following Tuberculosis Treatment: A Retrospective Cohort Study, a Systematic Review of the Literature, and Meta-analysis. Clinical Infectious Diseases, 2021, 73(9): 1580-8.
Hui Z, Fei H, Wei C, Xin D, Geng ZM, Jia H, et al. Estimates of Tuberculosis Mortality Rates in China Using the Disease Surveillance Point System, 2004-2010 %J - Biomedical and Environmental Sciences. 2012, - 25(- 4): - 483.
Chikovore J, Pai M, Horton KC, Daftary A, Kumwenda MK, Hart G, et al. Missing men with tuberculosis: the need to address structural influences and implement targeted and multidimensional interventions. Bmj Global Health, 2020, 5(5).
O'Leary SM, Coleman MM, Chew WM, Morrow C, McLaughlin AM, Gleeson LE, et al. Cigarette smoking impairs human pulmonary immunity to Mycobacterium tuberculosis. Am J Respir Crit Care Med, 2014, 190(12): 1430-6.
Imtiaz S, Shield KD, Roerecke M, Samokhvalov AV, Lönnroth K, Rehm J. Alcohol consumption as a risk factor for tuberculosis: meta-analyses and burden of disease. The European respiratory journal, 2017, 50(1).
Neyrolles O, Quintana-Murci L. Sexual inequality in tuberculosis. PLoS Med, 2009, 6(12): e1000199.
Liu J, Chen G, Chi I, Wu J, Pei L, Song X, et al. Regional variations in and correlates of disability-free life expectancy among older adults in China [J/OL] 2010, 10(446[10.1186/1471-2458-10-446
Younis MZ, Rivers PA, Fottler MD. The impact of HMO and hospital competition on hospital costs. Journal of health care finance, 2005, 31(4): 60-74.
Caraux-Paz P, Diamantis S, de Wazieres B, Gallien S. Tuberculosis in the Elderly. Journal of Clinical Medicine, 2021, 10(24).
Zhao Y, Zhao S, Zhang L, Haregu TN, Wang H. Impacts of multimorbidity on medication treatment, primary healthcare and hospitalization among middle-aged and older adults in China: evidence from a nationwide longitudinal study. BMC Public Health, 2021, 21(1): 1380.
Li S-j, Li Y-f, Song W-m, Zhang Q-y, Liu S-q, Xu T-t, et al. Population aging and trends of pulmonary tuberculosis incidence in the elderly. BMC Infectious Diseases, 2021, 21(1): 302.
Liu K, Ai L, Pan J, Fei F, Chen S, Zhang Y, et al. Survival Analysis and Associated Factors for Pulmonary Tuberculosis Death: Evidence from the Information System of Tuberculosis Disease and Mortality Surveillance in China. Risk Manag Healthc Policy, 2022, 15: 1167-78.
Zou Z, Liu G, Hay SI, Basu S, Belgaumi UI, Dhali A, et al. Time trends in tuberculosis mortality across the BRICS: an age-period-cohort analysis for the GBD 2019. EClinicalMedicine, 2022, 53: 101646.
Zhe -D, Yan -YH, Cheng -YS, Fei -H, Jun -LJ, Lin -ZY, et al. - Changes in Notified Incidence of Pulmonary Tuberculosis in China, 2005–2020 %J - Biomedical and Environmental Sciences. 2023, - 36(- 2): - 117.
Chen JO, Zha S, Hou JL, Lu KY, Qiu YB, Yang R, et al. Dose-response relationship between body mass index and tuberculosis in China: a population-based cohort study. Bmj Open, 2022, 12(3).
Min J, Kim JS, Kim HW, Ko Y, Oh JY, Jeong YJ, et al. Effects of underweight and overweight on mortality in patients with pulmonary tuberculosis. Front Public Health, 2023, 11: 1236099.
Cheng Q, Trangucci R, Nelson KN, Fu WJ, Collender PA, Head JR, et al. Prenatal and early-life exposure to the Great Chinese Famine increased the risk of tuberculosis in adulthood across two generations. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(44): 27549-55.
Jin S, Jiang T, Ma J. Brief Introduction of Chinese Infectious Disease Detection Report Information System. China Digital Medicine, 2006, 1(1): 20-2.

Download PDF

Version 1

posted

You are reading this latest preprint version

Trends in Tuberculosis Mortality among the Elderly in China, 2004-2021: A Jointpoint Regression and Age-Period-Cohort Analysis

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1. Introduction

2. Materials and Methods

2.1 Data Source

2.2 Statistical Analysis

2.2.1 Joinpoint Regression

2.2.2 Age-Period-Cohort Analysis

3. Results

3.1 Joinpoint Regression Analysis of TB Mortality

3.2 The Variation of Age, Period, and Cohort on TB Mortality

3.3 Age-Period-Cohort (IE) Analysis of TB Mortality

3.3.1 Age effect

3.3.2 Period effect

3.3.3 Cohort effect

4. Discussions

5. Conclusions

Abbreviations

Declarations

References

Status:

Version 1