Long-term trends of nasopharyngeal carcinoma mortality in China from 2006 to 2020 by region and sex: An age-period-cohort analysis

Long-term trends of nasopharyngeal carcinoma mortality in China from 2006 to 2020 by region and sex: An age-period-cohort analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-term trends of nasopharyngeal carcinoma mortality in China from 2006 to 2020 by region and sex: An age-period-cohort analysis Xinru Guo, Jiameng Cui, Xin Yuan, Zibo Gao, Ge Yu, Hao Wu, Changgui Kou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3029867/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Oct, 2023 Read the published version in BMC Public Health → Version 1 posted 8 You are reading this latest preprint version Abstract Background: China has a high mortality from nasopharyngeal carcinoma (NPC). The NPC mortality trends in China from 2006 to 2020 were described and analyzed to understand its epidemiological characteristics by region and sexand to explore age, period, and cohort effects. Methods: A joinpoint regression model was used to fit the standardized NPC mortality and age-specific mortality. The age-period-cohort model was used to investigate age, period, and cohort effects on the NPC mortality risk. Results: The results showed that the NPC mortality rate in China has been declining steadily. From 2006 to 2020, the standardized NPC mortality rate in most age groups showed a significant downward trend. The annual percentage change was smaller in rural areas than in urban areas. The mortality risks of rural malesand rural females from 2016 to 2020 were 1.139 times and 1.080 times those from 2011 to 2015, respectively. The risk of NPC death in urban males born in 1984-1988 and in rural males born in 1979-1983 both showed an increasing trend. Conclusions: The strategies for preventing and treating NPC in China were effective from 2006 to 2020, but the findings indicate that people in rural areas need more targeted interventions to reduce NPC deaths. age-period-cohort model China joinpoint model mortality nasopharyngeal carcinoma Figures Figure 1 Figure 2 Background Nasopharyngeal carcinoma (NPC) is a rare malignancy that originates from nasopharyngeal epithelial cells [ 1 ]. In 2020, a total of 133,354 new cases and 80,008 deaths from NPC were reported globally, mainly in East and Southeast Asia [ 2 ]. In China, the NPC incidence and mortality have become the highest among patients with otorhinolaryngologic tumors [ 3 ]. The NPC incidence and mortality in China are relatively high, globally ranking 16th and 32nd, respectively [ 4 ]. NPC in China is mainly concentrated in Guangdong and Guangxi Provinces, and the incidence of NPC in some regions, such as Sihui and Zhongshan in Guangdong Province, is particularly high [ 5 ]. The 5-year relative survival rate of NPC in China is low, at only 43.8% [ 6 ]. In general, the incidence of NPC in China is relatively high, and with population aging and the increase in risk factors, it may continue to rise. Furthermore, NPC has a poor prognosis and high mortality rate. Therefore, there is a heavy mortality burden from NPC in China. There have been few urban‒rural comparative reports on the mortality trends of NPC in China in recent years [ 7 ]. To fill this knowledge gap, we studied the time trends and age-period-cohort effects on NPC mortality by region and sex in China from 2006 to 2020. In this study, the mortality data of NPC in China from 2006 to 2020 were grouped as follows: urban males, urban females, rural males, and rural females. A joinpoint regression model was used to explore the time trends of NPC mortality, and an age-period-cohort model was used to analyze age, period, and cohort effects on NPC mortality risk in the four groups. The purpose of this study was to summarize the trends of NPC mortality, analyze the influencing factors, and provide a scientific basis for the next steps of NPC prevention and treatment. Methods Data Source The NPC mortality data used in this study were obtained from the 2007–2021 “China Health Statistical Yearbook”, which collects statistical data on the health of residents across 31 provinces, autonomous regions, and municipalities in China and uses the ICD-10 International Classification of Diseases statistical standard. The population data of the corresponding years came from the "China Population & Employment Statistical Yearbook". The mortality data were classified by the death causes monitoring system, which is managed by the Health Information and Statistics Center of the National Health Commission, which conducts a nationally representative death monitoring project. The monitored population has increased to 24% of the Chinese population, and the number of monitoring points has increased to 605. Each monitoring point covers one district or county. Therefore, the data are well representative urban and rural areas throughout the country. In this study, the 2006–2020 NPC mortality data were statistically analyzed by region and sex (urban male, urban female, rural male, and rural female). Considering that in actual clinical practice, the number of deaths from NPC is low among people under the age of 30, and the cause of death is often complicated for people over 80 years, this study conducted a statistical analysis of the 30-79-year-old population in China. The patients were divided into 10 age groups: 30–34, 35–39 … 75–79. Using data from the seventh national census in 2020 as a reference, the direct standardization method was used to calculate the age-standardized mortality rates from 2006 to 2020. Joinpoint regression model In this study, JoinPoint Regression 4.9.0.0 was used to fit the joinpoint regression model for the NPC mortality trends and calculate the annual percentage change (APC) and the average annual percentage change (AAPC). The number of turning points was determined by the weighted Bayesian information criterion. If there is no turning point in the graph, then the trend is flat. APC > 0 indicates that the mortality rate increases each year during the observation period; otherwise, it decreases. The inspection level α was 0.05. Age-period-cohort model The age-period-cohort model was implemented using Stata 17.0 (StataCorp, College Station TX, USA). The age-period-cohort model is based on the Poisson distribution. By controlling for the interactions among age, period, and cohort, the age-period-cohort model can more clearly reflect the disease change trends by age, period, and cohort. Therefore, in recent years, the age-period-cohort model has been widely used to study the epidemiological characteristics of malignant tumors [ 8 , 9 ]. Because there is complete linear dependence among age, period, and cohort (period = age + cohort), a unique solution cannot be obtained. To solve this problem, Fu [ 10 ] and Yang et al. [ 11 ] proposed the intrinsic estimator (IE) method to provide unbiased and relatively efficient parameter estimation results. Therefore, this study used age-period-cohort analysis combined with the IE method. According to the linear relationship between age, period, and cohort, we divided the cohort into 12 cohort groups (1929–1933, 1934–1938 … 1984–1988) and divided the period into 3 periods (2006–2010, 2011–2015, and 2016–2020). Results Descriptive analysis of the mortality rate Overall (Fig. 1 ), the crude mortality rate and the standardized mortality rate of NPC in each group showed a continuous downward trend from 2006 to 2020, with obvious differences between urban and rural areas and between males and females. The mortality rate was generally higher in rural areas than in urban areas and higher in males than in females. The sex difference was more obvious. From 2006 to 2020, the standardized mortality rate of NPC in each group showed a significant downward trend (Table 1 ), and there was no turning point in any group. The APCs of urban males, urban females, rural males, and rural females were − 4.5%, -5.6%, -3.0%, and − 3.1%, respectively. The decline in NPC in urban females was the largest, and the decline in urban areas was generally higher than that in rural areas. Table 1 APC and AAPC of the standardized mortality rate of NPC Years APC 95%CI AAPC 95%CI Urban male 2006–2020 -4.5* (-5.6, -3.4) -4.5* (-5.6, -3.4) Urban female 2006–2020 -5.6* (-6.8, -4.3) -5.6* (-6.8, -4.3) Rural male 2006–2020 -3.0* (-4.6, -1.3) -3.0* (-4.6, -1.3) Rural female 2006–2020 -3.1* (-4.6, -1.6) -3.1* (-4.6, -1.6) APC: annual percentage change; AAPC: average annual percentage change; CI: confidence interval. * Significantly different from 0 at alpha = 0.05 (P < 0.05). Trends in the age-specific mortality rate using joinpoint regression analysis Urban males and urban females showed an overall downward trend in all age groups (Table 2 ). From 2006 to 2020, urban males aged 35–39 had the most obvious downward trend (APC = -7.5%, P < 0.05), and urban females aged 40–44 had the most obvious downward trend (APC = -8.1%, P < 0.05). NPC mortality in rural males aged 30–34 increased at a rate of 2.9%, but most age groups showed a downward trend (ages 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, and 75–79), and all had statistical significance. Among them, the age group 40–44 had the most obvious downward trend, with an AAPC of -6.7% (P < 0.05). In rural females, the age groups 30–34, 35–39, 40–44, 55–59, 60–64, 65–69, and 70–74 had statistical significance, all showing a downward trend. Among them, the age group 40–44 was the most obvious (APC = -5.8%, P < 0.05). Table 2 AAPC and APC of the age-specific mortality of NPC Age Urban male Urban female Period APC(%, 95%CI) AAPC(%, 95%CI) Period APC (%, 95%CI) AAPC(%, 95%CI) 30–34 2006–2020 -1.1 (-5.1, 3.1) -1.1 (-5.1, 3.1) 2006–2020 -3.7 (-9.1, 2.1) -3.7* (-9.1, 2.1) 35–39 2006–2020 -7.5* (-11.2, -3.7) -7.5* (-11.2, -3.7) 2006–2020 -7.9*(-11.9, -3.7) -7.9* (-11.9, -3.7) 40–44 2006–2020 -5.4* (-7.8, -2.9) -5.4* (-7.8, -2.9) 2006–2020 -8.1* (-9.9, -6.2) -8.1* (-9.9, -6.2) 45–49 2006–2020 -6.8* (-8.8, -4.7) -6.8* (-8.8, -4.7) 2006–2020 -7.4* (-9.7, -4.9) -7.4* (-9.7, -4.9) 50–54 2006–2020 -4.8* (-6.9, -2.5) -4.8* (-6.9, -2.5) 2006–2020 -5.6* (-8.3, -2.8) -5.6* (-8.3, -2.8) 55–59 2006–2020 -5.4* (-7.5, -3.3) -5.4* (-7.5, -3.3) 2006–2020 -6.0* (-8.3, -3.6) -6.0* (-8.3, -3.6) 60–64 2006–2020 -3.6* (-5.2, -2) -3.6* (-5.2, -2) 2006–2020 -4.8* (-6.9, -2.6) -4.8* (-6.9, -2.6) 65–69 2006–2020 -2.5* (-3.9, -0.9) -2.5* (-3.9, -0.9) 2006–2020 -5.3* (-6.2, -4.3) -5.3* (-6.2, -4.3) 70–74 2006–2020 -3.4* (-5.1, -1.6) -3.4* (-5.1, -1.6) 2006–2020 -4.3* (-6.4, -2.2) -4.3* (-6.4, -2.2) 75–79 2006–2020 -4.8* (-10.5, 1.2) 2006–2020 -4.6* (-6.4, -2.7) -4.6* (-6.4, -2.7) 2006–2008 -20.3 (-42.3, 10.2) 2008–2018 -5.3* (-8.7, -1.9) 2018–2020 16.6 (-18.9, 67.6) Age Rural male Rural female Period APC(%, 95%CI) AAPC(%, 95%CI) Period APC (%, 95%CI) AAPC(%, 95%CI) 30–34 2006–2020 2.9 (-1.3, 7.3) 2.9* (-1.3, 7.3) 2006–2020 -1.1 (-4.4, 2.3) -1.1* (-4.4, 2.3) 35–39 2006–2020 0.0 (-3.0, 3.1) 0.0 (-3.0, 3.1) 2006–2020 -3.6* (-6.9, -0.3) -3.6* (-6.9, -0.3) 40–44 2006–2020 -6.7* (-12.9, 0) 2006–2020 -5.8* (-8.6, -3) -5.8* (-8.6, -3) 2006–2008 -25.5 (-54.2, 21.1) 2008–2020 -3.1 (-7.1, 1) 45–49 2006–2020 -1.9 (-4.8, 1.1) -1.9* (-4.8, 1.1) 2006–2020 0.5 (-6.2, 7.6) 2006–2009 32.2 (-6.2, 86.4) 2009–2020 -6.8* (-9.9, -3.5) 50–54 2006–2020 -4.6* (-9.5, 0.5) 2006–2020 -2.3 (-9.1, 5) 2006–2011 -10.3* (-16.2, -3.9) 2006–2008 -23.8 (-55, 28.9) 2011–2018 5.2 (-0.8, 11.5) 2008–2020 1.8 (-2, 5.8) 2018–2020 -21.1 (-44.4, 12) 55–59 2006–2020 -4.1* (-6.9, -1.2) 2006–2020 -5.7* (-10.1, -1.1) 2006–2017 -7.9* (-9.5, -6.3) 2006–2009 -19.2* (-34.6, -0.2) 2017–2020 11.3 (-3.5, 28.5) 2009–2020 -1.6 (-5.3, 2.2) 60–64 2006–2020 -3.2* (-5.0, -1.2) -3.2* (-5.0, -1.2) 2006–2020 -4.8* (-7.7, -1.9) -4.8* (-7.7, -1.9) 65–69 2006–2020 -2.5 (-5.3, 0.5) -2.5* (-5.3, 0.5) 2006–2020 -3.0 (-6.0, 0.0) -3.0* (-6.0, 0.0) 70–74 2006–2020 -2.9* (-5.1, -0.7) -2.9* (-5.1, -0.7) 2006–2020 -3.7* (-6.3, -1.1) -3.7* (-6.3, -1.1) 75–79 2006–2020 -4.0* (-8.4, 0.6) 2006–2020 -1.1 (-7.9, 6.1) 2006–2008 -18.6 (-42.6, 15.3) 2006–2010 3.6 (-6.4, 14.6) 2008–2020 -1.3 (-3.5, 0.8) 2010–2013 -15.4 (-40.9, 21.1) 2013–2020 2.9 (-1.9, 7.9) APC: annual percentage change; AAPC: average annual percentage change; CI: confidence interval. *Significantly different from 0 at alpha = 0.05 (P < 0.05). Effect of variations in age, period, and cohort on the mortality rate Overall, the age effect coefficients of NPC mortality risk in the four groups increased with age (Table 3 ). The peak age effect coefficients of urban males, urban females, rural males, and rural females were in the age group 65–69, 75–79, 60–64, and 70–74, which increased by 2.067, 1.928, 2.235, and 1.722 times compared with the coefficient of the age group 30–34, and the mortality risk increased by 7.901, 6.876, 9.346, and 5.596 times, respectively. With the advancement of the time period, the period effect coefficients of NPC mortality risk in urban males and urban females decreased, while those in rural males and rural females decreased initially and then increased. The effect coefficients of urban males and urban females were the smallest in the period from 2016 to 2020, and the risk of death in 2006–2010 was 1.208 times and 1.402 times that in 2016–2020, respectively. In rural males and females, the time period effect coefficients were the smallest in 2011–2015, and the mortality risk in 2016–2020 was 1.139 times and 1.080 times that in 2011–2015, respectively. The cohort effect on the mortality risk of NPC in the four groups showed a decreasing trend overall. The cohort effect coefficient in people born in 1929–1933 was the highest and that of urban males, urban females, rural males, and rural females was the lowest in 1979–1983, 1984–1988, 1974–1978, and 1984–1988, respectively. From the overall comparison of the three effects, the span of the age effect was the largest. The NPC mortality risk in urban males born in 1984–1988 and in rural males born in 1979–1983 both showed an increasing trend. Regarding later-born cohorts, the decline in the NPC mortality rate in urban females slowed, and that in rural females fluctuated (Fig. 2 ). Table 3 Effect coefficients of the NPC mortality of age, period, and cohort factors Factor Urban male Urban female Effect coefficient 95%CI P-value Effect coefficient 95%CI P-value Age 30–34 -1.432 (-1.577, -1.286) 0.000 -1.185 (-1.411, -0.959) 0.000 35–39 -0.838 (-0.926, -0.750) 0.000 -0.803 (-0.951, -0.654) 0.000 40–44 -0.357 (-0.433, -0.281) 0.000 -0.273 (-0.398, -0.147) 0.000 45–49 -0.128 (-0.200, -0.055) 0.001 -0.260 (-0.387, -0.132) 0.000 50–54 0.271 (0.205, 0.338) 0.000 0.051 (-0.067, 0.169) 0.398 55–59 0.266 (0.203, 0.329) 0.000 0.127 (0.014, 0.240) 0.028 60–64 0.465 (0.407, 0.523) 0.000 0.386 (0.286, 0.486) 0.000 65–69 0.635 (0.580, 0.689) 0.000 0.512 (0.420, 0.605) 0.000 70–74 0.585 (0.525, 0.645) 0.000 0.701 (0.605, 0.798) 0.000 75–79 0.532 (0.450, 0.614) 0.000 0.743 (0.615, 0.870) 0.000 Period 2006–2010 0.088 (0.062, 0.114) 0.000 0.172 (0.128, 0.217) 0.000 2011–2015 0.012 (-0.013, 0.037) 0.341 -0.006 (-0.050, 0.038) 0.787 2016–2020 -0.101 (-0.126, -0.075) 0.000 -0.166 (-0.212, -0.121) 0.000 Cohort 1929–1933 0.691 (0.576, 0.806) 0.000 0.499 (0.316, 0.682) 0.000 1934–1938 0.455 (0.377, 0.532) 0.000 0.485 (0.364, 0.605) 0.000 1939–1943 0.371 (0.310, 0.432) 0.000 0.427 (0.330, 0.525) 0.000 1944–1948 0.364 (0.304, 0.425) 0.000 0.335 (0.232, 0.437) 0.000 1949–1953 0.282 (0.218, 0.347) 0.000 0.299 (0.187, 0.411) 0.000 1954–1958 0.203 (0.133, 0.273) 0.000 0.203 (0.079, 0.328) 0.001 1959–1963 0.044 (-0.033, 0.121) 0.260 0.026 (-0.112, 0.164) 0.709 1964–1968 -0.183 (-0.262, -0.103) 0.000 -0.033 (-0.169, 0.103) 0.632 1969–1973 -0.293 (-0.374, -0.211) 0.000 -0.250 (-0.390, -0.111) 0.000 1974–1978 -0.588 (-0.679, -0.497) 0.000 -0.635 (-0.788, -0.482) 0.000 1979–1983 -0.723 (-0.846, -0.599) 0.000 -0.676 (-0.881, -0.471) 0.000 1984–1988 -0.625 (-0.855, -0.394) 0.000 -0.679 (-1.052, -0.307) 0.000 Factor Rural male Rural female Effect coefficient 95%CI P-value Effect coefficient 95%CI P-value Age 30–34 -1.720 (-1.826, -1.613) 0.000 -1.135 (-1.273, -0.997) 0.000 35–39 -0.891 (-0.951, -0.830) 0.000 -0.810 (-0.903, -0.718) 0.000 40–44 -0.143 (-0.192, -0.094) 0.000 -0.371 (-0.450, -0.293) 0.000 45–49 0.052 (0.005, 0.098) 0.030 -0.172 (-0.250, -0.095) 0.000 50–54 0.415 (0.373, 0.456) 0.000 0.148 (0.077, 0.219) 0.000 55–59 0.460 (0.422, 0.499) 0.000 0.325 (0.258, 0.392) 0.000 60–64 0.515 (0.481, 0.549) 0.000 0.453 (0.394, 0.512) 0.000 65–69 0.505 (0.474, 0.537) 0.000 0.474 (0.419, 0.529) 0.000 70–74 0.428 (0.392, 0.464) 0.000 0.587 (0.529, 0.645) 0.000 75–79 0.379 (0.329, 0.428) 0.000 0.502 (0.423, 0.581) 0.000 Period 2006–2010 0.052 (0.037, 0.068) 0.000 0.067 (0.040, 0.094) 0.000 2011–2015 -0.091 (-0.107, -0.076) 0.000 -0.072 (-0.099, -0.045) 0.000 2016–2020 0.039 (0.023, 0.055) 0.000 0.005 (-0.023, 0.033) 0.726 Cohort 1929–1933 0.684 (0.615, 0.754) 0.000 0.695 (0.587, 0.803) 0.000 1934–1938 0.608 (0.562, 0.654) 0.000 0.540 (0.467, 0.613) 0.000 1939–1943 0.440 (0.404, 0.476) 0.000 0.420 (0.362, 0.479) 0.000 1944–1948 0.290 (0.253, 0.326) 0.000 0.255 (0.195, 0.315) 0.000 1949–1953 0.222 (0.184, 0.261) 0.000 0.165 (0.099, 0.232) 0.000 1954–1958 -0.025 (-0.069, 0.018) 0.254 -0.043 (-0.118, 0.032) 0.262 1959–1963 -0.272 (-0.322, -0.222) 0.000 -0.183 (-0.268, -0.098) 0.000 1964–1968 -0.188 (-0.238, -0.138) 0.000 -0.055 (-0.138, 0.028) 0.195 1969–1973 -0.450 (-0.505, -0.395) 0.000 -0.294 (-0.382, -0.206) 0.000 1974–1978 -0.648 (-0.712, -0.585) 0.000 -0.534 (-0.632, -0.435) 0.000 1979–1983 -0.411 (-0.496, -0.326) 0.000 -0.410 (-0.539, -0.281) 0.000 1984–1988 -0.250 (-0.418, -0.082) 0.004 -0.557 (-0.796, -0.317) 0.000 CI: confidence interval. Discussion To the best of our knowledge, this is the first investigation in which NPC mortality data have been grouped by region and sex to compare and describe time trends and age-period-cohort effects. As a common head and neck cancer, NPC can be characterized by geographical distribution and familial aggregation [ 12 , 13 ]. The pathogenesis of NPC is not completely clear, which may be due to the complex interactions between risk factors. NPC is associated with many risk factors, including Epstein–Barr virus (EBV) infection [ 14 , 15 ], genetic susceptibility, and environmental factors. In particular, EBV-DNA is regularly associated with the occurrence and development of undifferentiated NPC [ 16 ]. Smoking has been shown to increase the risk of NPC [ 17 ]. The consumption of salted fish and other preserved foods is closely related to the risk of NPC, and they are common foods in some areas with a high incidence of NPC [ 18 ]. Another factor that increases the risk of NPC is exposure to fumes, smoke, dust, and chemicals [ 19 ]. It has been reported that the consumption of fresh fruits and vegetables plays a protective role against NPC [ 20 , 21 ]. In this study, the NPC mortality rate in males was much higher than that in females, which is consistent with previous studies [ 7 , 22 ]. Regarding the higher NPC mortality rate in rural areas than in urban areas, in addition to inconsistencies in medical conditions, lifestyle factors may also play a role. This may be related to the fact that migrant workers are more likely to work in unhealthy working conditions and be exposed to smoke and chemicals [ 23 ]. Another possibility is the increased presence of household air pollution (HAP) in rural areas, which is discussed below. Additionally, compared with rural residents, urban residents have a significantly higher intake of fruits and vegetables [ 24 ]. These factors may contribute to the urban‒rural gap in NPC mortality in China. In this study, the crude and standardized NPC mortality rates in the four groups (urban males, urban females, rural males and rural females) from 2006 to 2020 showed a stable downward trend. Among them, the rate of decrease was largest in urban females, followed by urban males, and that of rural males was the smallest. Improvements in living conditions, medical care, and screening technology may be related to the decline. In 2009, China launched a major health care reform, promising to provide all citizens with equal access to basic health care [ 25 ]. The government has invested substantial amounts of money to establish a universal primary health care system. Since the etiology of NPC has not yet been fully defined, secondary prevention is highly important. China has carried out several screening programs in areas with a high incidence of NPC. Since 1986, 98,180 residents have participated in an NPC screening program for high-risk groups in Guangdong Province, and the 5-year survival rate of NPC patients among the participants was 79.87%, which was much higher than the 58.43% of hospitalized cases during the same interval [ 26 ]. From 2006 to 2020, in urban males, the most obvious downward trend in NPC mortality was in the age group 35–39; the other three groups had the most obvious downward trend in the age group 40–44. These results show that there have been good improvements in the prevention and treatment of NPC in China in young and middle-aged people. Among the age-period-cohort effects, the age effect coefficient span was the largest, indicating that the age effect has the greatest impact on the NPC mortality risk. The NPC mortality risk increased with age but leveled off in the older age groups or even decreased slightly. This finding is consistent with previous studies [ 27 ]. Most studies on the prognostic survival of NPC have shown that the overall NPC survival is higher in young patients [ 28 ]. The reason for this may be that elderly individuals have lower immunity, more comorbidities, and higher nutritional needs [ 29 ]. It has been reported that fruit and vegetable intake by elderly individuals in China is seriously insufficient. In 2015, 85.2% and 49.6% of elderly individuals did not meet dietary recommendations for the intake of fruits and vegetables, respectively [ 30 ]. Improving dietary education in elderly individuals could be an effective measure to prevent and treat NPC. The period effect in rural areas increased during the period 2016–2020, indicating that the NPC mortality risk in rural areas increased during this time. The results also showed that the standardized mortality gap between urban and rural areas has widened in recent years. This might be related to HAP. HAP is typically caused by household activities that use solid fuels, such as cooking and heating. Solid fuels such as coal and biomass are commonly used in poorly ventilated households. The consumption of solid fuels in China has been decreasing over the past decade, but the reason behind this trend is mainly attributed to increased urbanization rather than specific household control policies [ 31 ]. As of 2014, approximately 48% of rural households used solid fuels for cooking, 72% of rural households used solid fuels for heating, and more than half often did not have adequate ventilation, compared with less than 5% of urban households [ 32 ]. The levels of particulate matter and nitrogen dioxide in the indoor air of these rural households often exceed the World Health Organization (WHO) recommendations and Chinese national standards. These two pollutants have been shown to be significantly associated with the risk of NPC [ 33 ]. Notably, rural females are more likely to be exposed to HAP from solid fuels for cooking and heating, which increases their exposure to air pollution [ 34 ]. This may explain why rural females had a relatively small reduction in standardized NPC mortality. However, the harm of HAP goes far beyond NPC risk; research shows that 2.8 billion people use solid fuels for cooking, resulting in nearly 3 million premature deaths [ 35 ]. Therefore, it is very important to increase the construction of infrastructure and promote energy upgrades in rural households. Although China signed the WHO Framework Convention on Tobacco Control in 2003, tobacco control policies implemented in China have been unsuccessful in reducing the smoking rate. The smoking rate among adolescents of both genders has risen sharply, and among young females, it has risen steadily [ 36 ]. The prevalence of secondhand smoke exposure in households with school-aged children remains high [ 37 ]. Studies have found that the earlier people start smoking, the higher their risk of developing NPC [ 38 ]. This may explain why the risk coefficients of males and females in the later-born cohorts increased or decreased at slower rates in this study. With economic development, education and income gaps between urban and rural areas are gradually revealed, and an increasing number of problems among homeschooled children have arisen. Adolescents living in rural areas with lower levels of social connectedness and more emotional problems may tend to use tobacco as a way to release stress [ 39 ]. There has been an increase in smoking among lower educated males born after 1980, and this increase is significantly higher than that in highly educated males [ 40 ]. This may be why the risk coefficients of rural males in the later-born cohorts increased the most. This indicates the importance of monitoring smoking behavior in young cohorts, with a particular focus on those with a low level of education. Moreover, some studies have found that with the rapid economic growth in China, the cancer mortality rate in rural residents is higher than that in urban residents, and the gap is widening [ 41 ]. To narrow the health gap between urban and rural areas, it is necessary to strengthen rural medical facility construction and allocate more educational resources to rural areas. Limitations The findings of this study provide epidemiological evidence of NPC mortality from 2006 to 2020, along with urban‒rural differences and age-period-cohort effects. However, this study has some limitations. First, the database used does not provide data on the NPC incidence rate, which makes it impossible to systematically analyze the epidemic trends of NPC. Second, age-period-cohort analysis inevitably leads to ecological fallacies, and the results of the study may not be generalizable to individuals. Conclusions Overall, the NPC mortality rate in China showed a steadily decreasing trend and indicates that the strategies used to prevent and treat NPC in China were effective from 2006 to 2020. However, there is an urban‒rural gap in NPC mortality, and the mortality risk in rural areas increased during 2016–2020. This can be mainly attributed to the fact that people in rural areas generally have a poorer medical condition and diet and are exposed to higher levels of indoor air pollution. Abbreviations NPC – Nasopharyngeal carcinoma APC – Annual percentage change AAPC – Average annual percentage change IE – Intrinsic estimator EBV – Epstein–Barr virus HAP – Household air pollution WHO – World Health Organization Declarations Ethics approval and consent to participate Ethics approval and participant consent were not applicable for this study as the data used were obtained from a publicly available database. Consent for publication Not applicable. Availability of data and materials All the data was sourced from the China Health Statistics Yearbook, which was publicly available at the website http://www.nhc.gov.cn/mohwsbwstjxxzx/new_index.shtml. If someone wants to request the data from this study, also can contact the corresponding author by email [email protected] . Competing interests The authors declare that they have no competing interest. Funding None. Authors’ contributions XG conceived the idea for this study, performed analysis and wrote the first draft. JC, XY, GY, ZG, HW, and CK collected the data. All the authors have read, revised, and approved the final manuscript. Acknowledgements We would like to acknowledge the China Health Statistics Yearbook and the National Bureau of Statistics for permitting us to use the open data. References Feng Y, et al. Outcomes of Recurrent Nasopharyngeal Carcinoma Patients Treated With Salvage Surgery: A Meta-Analysis. Front Oncol. 2021;11:720418. 10.3389/fonc.2021.720418 . Sung H, "Global Cancer Statistics. 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries," CA Cancer J Clin , vol. 71, no. 3, pp. 209–249, May 2021, doi: 10.3322/caac.21660 . Chen YP, Chan ATC, Le QT, Blanchard P, Sun Y, Ma J. "Nasopharyngeal carcinoma," (in English), Lancet , vol. 394, no. 10192, pp. 64–80, Jul 6 2019, doi: 10.1016/S0140-6736(19)30956-0 . Wei KR, Zheng RS, Zhang SW, Liang ZH, Li ZM, Chen WQ. "Nasopharyngeal carcinoma incidence and mortality in China 2013 " Chin J Cancer. 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Xiao G, Cao Y, Qiu X, Wang W, Wang Y. Influence of gender and age on the survival of patients with nasopharyngeal carcinoma. BMC Cancer. May 4 2013;13:226. 10.1186/1471-2407-13-226 . Wu SG et al. "Demographic and clinicopathological characteristics of nasopharyngeal carcinoma and survival outcomes according to age at diagnosis: A population-based analysis," (in eng), Oral Oncol , vol. 73, pp. 83–87, Oct 2017, doi: 10.1016/j.oraloncology.2017.08.006 . Guo Q, Yu D, Zhao L, Fang H, Ju L, Li S. "[Status of fresh vegetables and fruit consumption among Chinese aged 60 years old and above in 2015]," (in chi), Wei Sheng Yan Jiu , vol. 50, no. 3, pp. 401–408, May 2021, doi: 10.19813/j.cnki.weishengyanjiu.2021.03.009 . Zhao B et al. "Change in household fuels dominates the decrease in PM(2.5) exposure and premature mortality in China in 2005–2015," Proc Natl Acad Sci U S A, vol. 115, no. 49, pp. 12401–6, Dec 4 2018, doi: 10.1073/pnas.1812955115 . Chan KH, et al. Trans-generational changes and rural-urban inequality in household fuel use and cookstove ventilation in China: A multi-region study of 0.5 million adults. Int J Hyg Environ Health. Nov 2017;220(8):1370–81. 10.1016/j.ijheh.2017.09.010 . Fan HC, et al. Increased risk of incident nasopharyngeal carcinoma with exposure to air pollution. PLoS ONE. 2018;13(9):e0204568. 10.1371/journal.pone.0204568 . Liu T, et al. Urban-rural disparity of the short-term association of PM(2.5) with mortality and its attributable burden. Innov (Camb). Nov 28 2021;2(4):100171. 10.1016/j.xinn.2021.100171 . Pope D, Bruce N, Dherani M, Jagoe K, Rehfuess E. Real-life effectiveness of 'improved' stoves and clean fuels in reducing PM(2.5) and CO: Systematic review and meta-analysis. Environ Int. Apr 2017;101:7–18. 10.1016/j.envint.2017.01.012 . Wang M, et al. Trends in smoking prevalence and implication for chronic diseases in China: serial national cross-sectional surveys from 2003 to 2013. Lancet Respir Med. Jan 2019;7(1):35–45. 10.1016/S2213-2600(18)30432-6 . Xie M, et al. Household Exposure to Secondhand Smoke among Chinese Children: Status, Determinants, and Co-Exposures," (in eng). Int J Environ Res Public Health. Jul 30 2020;17(15). 10.3390/ijerph17155524 . Long M, Fu Z, Li P, Nie Z. "Cigarette smoking and the risk of nasopharyngeal carcinoma: a meta-analysis of epidemiological studies," (in eng), BMJ Open , vol. 7, no. 10, p. e016582, Oct 5 2017, doi: 10.1136/bmjopen-2017-016582 . Mistry R, McCarthy WJ, de Vogli R, Crespi CM, Wu Q, Patel M. "Adolescent smoking risk increases with wider income gaps between rich and poor," (in eng), Health Place , vol. 17, no. 1, pp. 222-9, Jan 2011, doi: 10.1016/j.healthplace.2010.10.004 . Jin L, Tao L, Lao X. Diverging Trends and Expanding Educational Gaps in Smoking in China. Int J Environ Res Public Health. Apr 18 2022;19(8). 10.3390/ijerph19084917 . Liu L. Rural–urban inequities in deaths and cancer mortality amid rapid economic and environmental changes in China. Int J Public Health. 2019;64(1):39–48. 10.1007/s00038-018-1109-3 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Oct, 2023 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Major revision 04 Sep, 2023 Reviews received at journal 17 Jul, 2023 Reviewers agreed at journal 17 Jul, 2023 Reviewers invited by journal 17 Jul, 2023 Editor assigned by journal 17 Jul, 2023 Editor invited by journal 28 Jun, 2023 Submission checks completed at journal 28 Jun, 2023 First submitted to journal 06 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3029867","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":214005300,"identity":"b970d1db-3db4-4947-929e-a99c85794d14","order_by":0,"name":"Xinru Guo","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinru","middleName":"","lastName":"Guo","suffix":""},{"id":214005301,"identity":"1beb4f55-0d4d-4f6b-89be-d980e5197676","order_by":1,"name":"Jiameng Cui","email":"","orcid":"","institution":"Jilin 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University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Changgui","middleName":"","lastName":"Kou","suffix":""}],"badges":[],"createdAt":"2023-06-06 14:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3029867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3029867/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-023-16892-1","type":"published","date":"2023-10-20T15:01:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39463113,"identity":"6da4e517-10b1-44ca-9c97-24af122b8328","added_by":"auto","created_at":"2023-07-03 13:50:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108417,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the crude and standardized mortality rates of NPC by region and sex in China (1/100,000)\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3029867/v1/b48da8437a95928f0f776875.jpg"},{"id":39463114,"identity":"9382ba91-fa0f-4191-a3d0-51c423e6c692","added_by":"auto","created_at":"2023-07-03 13:50:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39979,"visible":true,"origin":"","legend":"\u003cp\u003eEffect coefficients of NPC mortality of age, period, and cohort factors\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3029867/v1/fe868cbf7e4082bfd2c65e02.jpg"},{"id":45090934,"identity":"ca18c379-7ef7-491d-8ffe-d330b3dc68da","added_by":"auto","created_at":"2023-10-23 15:07:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":550115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3029867/v1/135a0bd4-c84d-4872-b3ac-76f408ebd8d6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term trends of nasopharyngeal carcinoma mortality in China from 2006 to 2020 by region and sex: An age-period-cohort analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eNasopharyngeal carcinoma (NPC) is a rare malignancy that originates from nasopharyngeal epithelial cells [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2020, a total of 133,354 new cases and 80,008 deaths from NPC were reported globally, mainly in East and Southeast Asia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In China, the NPC incidence and mortality have become the highest among patients with otorhinolaryngologic tumors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The NPC incidence and mortality in China are relatively high, globally ranking 16th and 32nd, respectively [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. NPC in China is mainly concentrated in Guangdong and Guangxi Provinces, and the incidence of NPC in some regions, such as Sihui and Zhongshan in Guangdong Province, is particularly high [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The 5-year relative survival rate of NPC in China is low, at only 43.8% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In general, the incidence of NPC in China is relatively high, and with population aging and the increase in risk factors, it may continue to rise. Furthermore, NPC has a poor prognosis and high mortality rate. Therefore, there is a heavy mortality burden from NPC in China. There have been few urban‒rural comparative reports on the mortality trends of NPC in China in recent years [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To fill this knowledge gap, we studied the time trends and age-period-cohort effects on NPC mortality by region and sex in China from 2006 to 2020.\u003c/p\u003e \u003cp\u003eIn this study, the mortality data of NPC in China from 2006 to 2020 were grouped as follows: urban males, urban females, rural males, and rural females. A joinpoint regression model was used to explore the time trends of NPC mortality, and an age-period-cohort model was used to analyze age, period, and cohort effects on NPC mortality risk in the four groups. The purpose of this study was to summarize the trends of NPC mortality, analyze the influencing factors, and provide a scientific basis for the next steps of NPC prevention and treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eThe NPC mortality data used in this study were obtained from the 2007\u0026ndash;2021 \u0026ldquo;China Health Statistical Yearbook\u0026rdquo;, which collects statistical data on the health of residents across 31 provinces, autonomous regions, and municipalities in China and uses the ICD-10 International Classification of Diseases statistical standard. The population data of the corresponding years came from the \"China Population \u0026amp; Employment Statistical Yearbook\". The mortality data were classified by the death causes monitoring system, which is managed by the Health Information and Statistics Center of the National Health Commission, which conducts a nationally representative death monitoring project. The monitored population has increased to 24% of the Chinese population, and the number of monitoring points has increased to 605. Each monitoring point covers one district or county. Therefore, the data are well representative urban and rural areas throughout the country. In this study, the 2006\u0026ndash;2020 NPC mortality data were statistically analyzed by region and sex (urban male, urban female, rural male, and rural female). Considering that in actual clinical practice, the number of deaths from NPC is low among people under the age of 30, and the cause of death is often complicated for people over 80 years, this study conducted a statistical analysis of the 30-79-year-old population in China. The patients were divided into 10 age groups: 30\u0026ndash;34, 35\u0026ndash;39 \u0026hellip; 75\u0026ndash;79. Using data from the seventh national census in 2020 as a reference, the direct standardization method was used to calculate the age-standardized mortality rates from 2006 to 2020.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eJoinpoint regression model\u003c/h2\u003e \u003cp\u003eIn this study, JoinPoint Regression 4.9.0.0 was used to fit the joinpoint regression model for the NPC mortality trends and calculate the annual percentage change (APC) and the average annual percentage change (AAPC). The number of turning points was determined by the weighted Bayesian information criterion. If there is no turning point in the graph, then the trend is flat. APC\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates that the mortality rate increases each year during the observation period; otherwise, it decreases. The inspection level α was 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAge-period-cohort model\u003c/h2\u003e \u003cp\u003eThe age-period-cohort model was implemented using Stata 17.0 (StataCorp, College Station TX, USA). The age-period-cohort model is based on the Poisson distribution. By controlling for the interactions among age, period, and cohort, the age-period-cohort model can more clearly reflect the disease change trends by age, period, and cohort. Therefore, in recent years, the age-period-cohort model has been widely used to study the epidemiological characteristics of malignant tumors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Because there is complete linear dependence among age, period, and cohort (period\u0026thinsp;=\u0026thinsp;age\u0026thinsp;+\u0026thinsp;cohort), a unique solution cannot be obtained. To solve this problem, Fu [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and Yang et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] proposed the intrinsic estimator (IE) method to provide unbiased and relatively efficient parameter estimation results. Therefore, this study used age-period-cohort analysis combined with the IE method. According to the linear relationship between age, period, and cohort, we divided the cohort into 12 cohort groups (1929\u0026ndash;1933, 1934\u0026ndash;1938 \u0026hellip; 1984\u0026ndash;1988) and divided the period into 3 periods (2006\u0026ndash;2010, 2011\u0026ndash;2015, and 2016\u0026ndash;2020).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis of the mortality rate\u003c/h2\u003e \u003cp\u003eOverall (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the crude mortality rate and the standardized mortality rate of NPC in each group showed a continuous downward trend from 2006 to 2020, with obvious differences between urban and rural areas and between males and females. The mortality rate was generally higher in rural areas than in urban areas and higher in males than in females. The sex difference was more obvious.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom 2006 to 2020, the standardized mortality rate of NPC in each group showed a significant downward trend (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and there was no turning point in any group. The APCs of urban males, urban females, rural males, and rural females were \u0026minus;\u0026thinsp;4.5%, -5.6%, -3.0%, and \u0026minus;\u0026thinsp;3.1%, respectively. The decline in NPC in urban females was the largest, and the decline in urban areas was generally higher than that in rural areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAPC and AAPC of the standardized mortality rate of NPC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAAPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.5*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-5.6, -3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.5*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(-5.6, -3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.6*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-6.8, -4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.6*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(-6.8, -4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.0*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-4.6, -1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.0*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(-4.6, -1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-4.6, -1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e(-4.6, -1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAPC: annual percentage change; AAPC: average annual percentage change; CI: confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* Significantly different from 0 at alpha\u0026thinsp;=\u0026thinsp;0.05 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTrends in the age-specific mortality rate using joinpoint regression analysis\u003c/h2\u003e \u003cp\u003eUrban males and urban females showed an overall downward trend in all age groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). From 2006 to 2020, urban males aged 35\u0026ndash;39 had the most obvious downward trend (APC = -7.5%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and urban females aged 40\u0026ndash;44 had the most obvious downward trend (APC = -8.1%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). NPC mortality in rural males aged 30\u0026ndash;34 increased at a rate of 2.9%, but most age groups showed a downward trend (ages 40\u0026ndash;44, 45\u0026ndash;49, 50\u0026ndash;54, 55\u0026ndash;59, 60\u0026ndash;64, 65\u0026ndash;69, 70\u0026ndash;74, and 75\u0026ndash;79), and all had statistical significance. Among them, the age group 40\u0026ndash;44 had the most obvious downward trend, with an AAPC of -6.7% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In rural females, the age groups 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, 55\u0026ndash;59, 60\u0026ndash;64, 65\u0026ndash;69, and 70\u0026ndash;74 had statistical significance, all showing a downward trend. Among them, the age group 40\u0026ndash;44 was the most obvious (APC = -5.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAAPC and APC of the age-specific mortality of NPC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUrban male\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eUrban female\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPC(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAPC(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPC (%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAAPC(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-1.1 (-5.1, 3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-1.1 (-5.1, 3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.7 (-9.1, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-3.7* (-9.1, 2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-7.5* (-11.2, -3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-7.5* (-11.2, -3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-7.9*(-11.9, -3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-7.9* (-11.9, -3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-5.4* (-7.8, -2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-5.4* (-7.8, -2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-8.1* (-9.9, -6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-8.1* (-9.9, -6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-6.8* (-8.8, -4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-6.8* (-8.8, -4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-7.4* (-9.7, -4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-7.4* (-9.7, -4.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-4.8* (-6.9, -2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-4.8* (-6.9, -2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-5.6* (-8.3, -2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-5.6* (-8.3, -2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-5.4* (-7.5, -3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-5.4* (-7.5, -3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-6.0* (-8.3, -3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-6.0* (-8.3, -3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-3.6* (-5.2, -2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-3.6* (-5.2, -2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-4.8* (-6.9, -2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-4.8* (-6.9, -2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-2.5* (-3.9, -0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-2.5* (-3.9, -0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-5.3* (-6.2, -4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-5.3* (-6.2, -4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-3.4* (-5.1, -1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-3.4* (-5.1, -1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-4.3* (-6.4, -2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-4.3* (-6.4, -2.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-4.8* (-10.5, 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-4.6* (-6.4, -2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-4.6* (-6.4, -2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-20.3 (-42.3, 10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2008\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-5.3* (-8.7, -1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e16.6 (-18.9, 67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRural male\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRural female\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPC(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAPC(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPC (%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAAPC(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e2.9 (-1.3, 7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2.9* (-1.3, 7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-1.1 (-4.4, 2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-1.1* (-4.4, 2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e0.0 (-3.0, 3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e0.0 (-3.0, 3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.6* (-6.9, -0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-3.6* (-6.9, -0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-6.7* (-12.9, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-5.8* (-8.6, -3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-5.8* (-8.6, -3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-25.5 (-54.2, 21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2008\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-3.1 (-7.1, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-1.9 (-4.8, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-1.9* (-4.8, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.5 (-6.2, 7.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e32.2 (-6.2, 86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2009\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-6.8* (-9.9, -3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-4.6* (-9.5, 0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-2.3 (-9.1, 5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-10.3* (-16.2, -3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-23.8 (-55, 28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2011\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e5.2 (-0.8, 11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2008\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e1.8 (-2, 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-21.1 (-44.4, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-4.1* (-6.9, -1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-5.7* (-10.1, -1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-7.9* (-9.5, -6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-19.2* (-34.6, -0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e11.3 (-3.5, 28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2009\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-1.6 (-5.3, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-3.2* (-5.0, -1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-3.2* (-5.0, -1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-4.8* (-7.7, -1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-4.8* (-7.7, -1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-2.5 (-5.3, 0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-2.5* (-5.3, 0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.0 (-6.0, 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-3.0* (-6.0, 0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-2.9* (-5.1, -0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-2.9* (-5.1, -0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.7* (-6.3, -1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-3.7* (-6.3, -1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-4.0* (-8.4, 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e-1.1 (-7.9, 6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-18.6 (-42.6, 15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e3.6 (-6.4, 14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2008\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-1.3 (-3.5, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2010\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-15.4 (-40.9, 21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2013\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e2.9 (-1.9, 7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAPC: annual percentage change; AAPC: average annual percentage change; CI: confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Significantly different from 0 at alpha\u0026thinsp;=\u0026thinsp;0.05 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEffect of variations in age, period, and cohort on the mortality rate\u003c/h2\u003e \u003cp\u003eOverall, the age effect coefficients of NPC mortality risk in the four groups increased with age (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The peak age effect coefficients of urban males, urban females, rural males, and rural females were in the age group 65\u0026ndash;69, 75\u0026ndash;79, 60\u0026ndash;64, and 70\u0026ndash;74, which increased by 2.067, 1.928, 2.235, and 1.722 times compared with the coefficient of the age group 30\u0026ndash;34, and the mortality risk increased by 7.901, 6.876, 9.346, and 5.596 times, respectively. With the advancement of the time period, the period effect coefficients of NPC mortality risk in urban males and urban females decreased, while those in rural males and rural females decreased initially and then increased. The effect coefficients of urban males and urban females were the smallest in the period from 2016 to 2020, and the risk of death in 2006\u0026ndash;2010 was 1.208 times and 1.402 times that in 2016\u0026ndash;2020, respectively. In rural males and females, the time period effect coefficients were the smallest in 2011\u0026ndash;2015, and the mortality risk in 2016\u0026ndash;2020 was 1.139 times and 1.080 times that in 2011\u0026ndash;2015, respectively. The cohort effect on the mortality risk of NPC in the four groups showed a decreasing trend overall. The cohort effect coefficient in people born in 1929\u0026ndash;1933 was the highest and that of urban males, urban females, rural males, and rural females was the lowest in 1979\u0026ndash;1983, 1984\u0026ndash;1988, 1974\u0026ndash;1978, and 1984\u0026ndash;1988, respectively. From the overall comparison of the three effects, the span of the age effect was the largest. The NPC mortality risk in urban males born in 1984\u0026ndash;1988 and in rural males born in 1979\u0026ndash;1983 both showed an increasing trend. Regarding later-born cohorts, the decline in the NPC mortality rate in urban females slowed, and that in rural females fluctuated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect coefficients of the NPC mortality of age, period, and cohort factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUrban male\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eUrban female\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.577, -1.286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.411, -0.959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.926, -0.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.951, -0.654)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.433, -0.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.398, -0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.200, -0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.387, -0.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.205, 0.338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.067, 0.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.203, 0.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.014, 0.240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.407, 0.523)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.286, 0.486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.580, 0.689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.420, 0.605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.525, 0.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.605, 0.798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.450, 0.614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.615, 0.870)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.062, 0.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.128, 0.217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.013, 0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.050, 0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.126, -0.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.212, -0.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1929\u0026ndash;1933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.576, 0.806)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.316, 0.682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1934\u0026ndash;1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.377, 0.532)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.364, 0.605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1939\u0026ndash;1943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.310, 0.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.330, 0.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1944\u0026ndash;1948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.304, 0.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.232, 0.437)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1949\u0026ndash;1953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.218, 0.347)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.187, 0.411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1954\u0026ndash;1958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.133, 0.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.079, 0.328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1959\u0026ndash;1963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.033, 0.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.112, 0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1964\u0026ndash;1968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.262, -0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.169, 0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1969\u0026ndash;1973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.374, -0.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.390, -0.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1974\u0026ndash;1978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.679, -0.497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.788, -0.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1979\u0026ndash;1983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.846, -0.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.881, -0.471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1984\u0026ndash;1988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.855, -0.394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.052, -0.307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRural male\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRural female\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.826, -1.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.273, -0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.951, -0.830)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.903, -0.718)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.192, -0.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.450, -0.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.005, 0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.250, -0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.373, 0.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.077, 0.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.422, 0.499)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.258, 0.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.481, 0.549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.394, 0.512)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.474, 0.537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.419, 0.529)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.392, 0.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.529, 0.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.329, 0.428)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.423, 0.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.037, 0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.040, 0.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.107, -0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.099, -0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.023, 0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.023, 0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1929\u0026ndash;1933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.615, 0.754)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.587, 0.803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1934\u0026ndash;1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.562, 0.654)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.467, 0.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1939\u0026ndash;1943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.404, 0.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.362, 0.479)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1944\u0026ndash;1948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.253, 0.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.195, 0.315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1949\u0026ndash;1953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.184, 0.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.099, 0.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1954\u0026ndash;1958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.069, 0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.118, 0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1959\u0026ndash;1963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.322, -0.222)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.268, -0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1964\u0026ndash;1968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.238, -0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.138, 0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1969\u0026ndash;1973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.505, -0.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.382, -0.206)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1974\u0026ndash;1978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.712, -0.585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.632, -0.435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1979\u0026ndash;1983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.496, -0.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.539, -0.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1984\u0026ndash;1988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.418, -0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.796, -0.317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCI: confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first investigation in which NPC mortality data have been grouped by region and sex to compare and describe time trends and age-period-cohort effects. As a common head and neck cancer, NPC can be characterized by geographical distribution and familial aggregation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The pathogenesis of NPC is not completely clear, which may be due to the complex interactions between risk factors. NPC is associated with many risk factors, including Epstein\u0026ndash;Barr virus (EBV) infection [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], genetic susceptibility, and environmental factors. In particular, EBV-DNA is regularly associated with the occurrence and development of undifferentiated NPC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Smoking has been shown to increase the risk of NPC [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The consumption of salted fish and other preserved foods is closely related to the risk of NPC, and they are common foods in some areas with a high incidence of NPC [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Another factor that increases the risk of NPC is exposure to fumes, smoke, dust, and chemicals [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It has been reported that the consumption of fresh fruits and vegetables plays a protective role against NPC [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, the NPC mortality rate in males was much higher than that in females, which is consistent with previous studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Regarding the higher NPC mortality rate in rural areas than in urban areas, in addition to inconsistencies in medical conditions, lifestyle factors may also play a role. This may be related to the fact that migrant workers are more likely to work in unhealthy working conditions and be exposed to smoke and chemicals [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Another possibility is the increased presence of household air pollution (HAP) in rural areas, which is discussed below. Additionally, compared with rural residents, urban residents have a significantly higher intake of fruits and vegetables [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These factors may contribute to the urban‒rural gap in NPC mortality in China.\u003c/p\u003e \u003cp\u003eIn this study, the crude and standardized NPC mortality rates in the four groups (urban males, urban females, rural males and rural females) from 2006 to 2020 showed a stable downward trend. Among them, the rate of decrease was largest in urban females, followed by urban males, and that of rural males was the smallest. Improvements in living conditions, medical care, and screening technology may be related to the decline. In 2009, China launched a major health care reform, promising to provide all citizens with equal access to basic health care [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The government has invested substantial amounts of money to establish a universal primary health care system. Since the etiology of NPC has not yet been fully defined, secondary prevention is highly important. China has carried out several screening programs in areas with a high incidence of NPC. Since 1986, 98,180 residents have participated in an NPC screening program for high-risk groups in Guangdong Province, and the 5-year survival rate of NPC patients among the participants was 79.87%, which was much higher than the 58.43% of hospitalized cases during the same interval [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom 2006 to 2020, in urban males, the most obvious downward trend in NPC mortality was in the age group 35\u0026ndash;39; the other three groups had the most obvious downward trend in the age group 40\u0026ndash;44. These results show that there have been good improvements in the prevention and treatment of NPC in China in young and middle-aged people. Among the age-period-cohort effects, the age effect coefficient span was the largest, indicating that the age effect has the greatest impact on the NPC mortality risk. The NPC mortality risk increased with age but leveled off in the older age groups or even decreased slightly. This finding is consistent with previous studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Most studies on the prognostic survival of NPC have shown that the overall NPC survival is higher in young patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The reason for this may be that elderly individuals have lower immunity, more comorbidities, and higher nutritional needs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. It has been reported that fruit and vegetable intake by elderly individuals in China is seriously insufficient. In 2015, 85.2% and 49.6% of elderly individuals did not meet dietary recommendations for the intake of fruits and vegetables, respectively [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Improving dietary education in elderly individuals could be an effective measure to prevent and treat NPC.\u003c/p\u003e \u003cp\u003eThe period effect in rural areas increased during the period 2016\u0026ndash;2020, indicating that the NPC mortality risk in rural areas increased during this time. The results also showed that the standardized mortality gap between urban and rural areas has widened in recent years. This might be related to HAP. HAP is typically caused by household activities that use solid fuels, such as cooking and heating. Solid fuels such as coal and biomass are commonly used in poorly ventilated households. The consumption of solid fuels in China has been decreasing over the past decade, but the reason behind this trend is mainly attributed to increased urbanization rather than specific household control policies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As of 2014, approximately 48% of rural households used solid fuels for cooking, 72% of rural households used solid fuels for heating, and more than half often did not have adequate ventilation, compared with less than 5% of urban households [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The levels of particulate matter and nitrogen dioxide in the indoor air of these rural households often exceed the World Health Organization (WHO) recommendations and Chinese national standards. These two pollutants have been shown to be significantly associated with the risk of NPC [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Notably, rural females are more likely to be exposed to HAP from solid fuels for cooking and heating, which increases their exposure to air pollution [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This may explain why rural females had a relatively small reduction in standardized NPC mortality. However, the harm of HAP goes far beyond NPC risk; research shows that 2.8\u0026nbsp;billion people use solid fuels for cooking, resulting in nearly 3\u0026nbsp;million premature deaths [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, it is very important to increase the construction of infrastructure and promote energy upgrades in rural households.\u003c/p\u003e \u003cp\u003eAlthough China signed the WHO Framework Convention on Tobacco Control in 2003, tobacco control policies implemented in China have been unsuccessful in reducing the smoking rate. The smoking rate among adolescents of both genders has risen sharply, and among young females, it has risen steadily [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The prevalence of secondhand smoke exposure in households with school-aged children remains high [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Studies have found that the earlier people start smoking, the higher their risk of developing NPC [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This may explain why the risk coefficients of males and females in the later-born cohorts increased or decreased at slower rates in this study. With economic development, education and income gaps between urban and rural areas are gradually revealed, and an increasing number of problems among homeschooled children have arisen. Adolescents living in rural areas with lower levels of social connectedness and more emotional problems may tend to use tobacco as a way to release stress [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. There has been an increase in smoking among lower educated males born after 1980, and this increase is significantly higher than that in highly educated males [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This may be why the risk coefficients of rural males in the later-born cohorts increased the most. This indicates the importance of monitoring smoking behavior in young cohorts, with a particular focus on those with a low level of education. Moreover, some studies have found that with the rapid economic growth in China, the cancer mortality rate in rural residents is higher than that in urban residents, and the gap is widening [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. To narrow the health gap between urban and rural areas, it is necessary to strengthen rural medical facility construction and allocate more educational resources to rural areas.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe findings of this study provide epidemiological evidence of NPC mortality from 2006 to 2020, along with urban‒rural differences and age-period-cohort effects. However, this study has some limitations. First, the database used does not provide data on the NPC incidence rate, which makes it impossible to systematically analyze the epidemic trends of NPC. Second, age-period-cohort analysis inevitably leads to ecological fallacies, and the results of the study may not be generalizable to individuals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, the NPC mortality rate in China showed a steadily decreasing trend and indicates that the strategies used to prevent and treat NPC in China were effective from 2006 to 2020. However, there is an urban‒rural gap in NPC mortality, and the mortality risk in rural areas increased during 2016\u0026ndash;2020. This can be mainly attributed to the fact that people in rural areas generally have a poorer medical condition and diet and are exposed to higher levels of indoor air pollution.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNPC \u0026ndash; Nasopharyngeal carcinoma\u003c/p\u003e\n\u003cp\u003eAPC \u0026ndash; Annual percentage change\u003c/p\u003e\n\u003cp\u003eAAPC \u0026ndash; Average annual percentage change\u003c/p\u003e\n\u003cp\u003eIE \u0026ndash; Intrinsic estimator\u003c/p\u003e\n\u003cp\u003eEBV \u0026ndash; Epstein\u0026ndash;Barr virus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHAP \u0026ndash; Household air pollution\u003c/p\u003e\n\u003cp\u003eWHO \u0026ndash; World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and participant consent were not applicable for this study as the data used were obtained from a publicly available database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data was sourced from the China Health Statistics Yearbook, which was publicly available at the website http://www.nhc.gov.cn/mohwsbwstjxxzx/new_index.shtml.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIf someone wants to request the data from this study, also can contact the corresponding author by email [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXG conceived the idea for this study, performed analysis and wrote the first draft. JC, XY, GY, ZG, HW, and CK collected the data. All the authors have read, revised, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the China Health Statistics Yearbook and the National Bureau of Statistics for permitting us to use the open data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeng Y, et al. Outcomes of Recurrent Nasopharyngeal Carcinoma Patients Treated With Salvage Surgery: A Meta-Analysis. 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Int J Public Health. 2019;64(1):39\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00038-018-1109-3\u003c/span\u003e\u003cspan address=\"10.1007/s00038-018-1109-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"age-period-cohort model, China, joinpoint model, mortality, nasopharyngeal carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-3029867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3029867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eChina has a high mortality from nasopharyngeal carcinoma (NPC). The NPC mortality trends in China from 2006 to 2020 were described and analyzed to understand its epidemiological characteristics by region and sexand to explore age, period, and cohort effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A joinpoint regression model was used to fit the standardized NPC mortality and age-specific mortality. The age-period-cohort model was used to investigate age, period, and cohort effects on the NPC mortality risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The results showed that the NPC mortality rate in China has been declining steadily. From 2006 to 2020, the standardized NPC mortality rate in most age groups showed a significant downward trend. The annual percentage change was smaller in rural areas than in urban areas. The mortality risks of rural malesand rural females from 2016 to 2020 were 1.139 times and 1.080 times those from 2011 to 2015, respectively. The risk of NPC death in urban males born in 1984-1988 and in rural males born in 1979-1983 both showed an increasing trend.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe strategies for preventing and treating NPC in China were effective from 2006 to 2020, but the findings indicate that people in rural areas need more targeted interventions to reduce NPC deaths.\u003c/p\u003e","manuscriptTitle":"Long-term trends of nasopharyngeal carcinoma mortality in China from 2006 to 2020 by region and sex: An age-period-cohort analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-03 13:50:29","doi":"10.21203/rs.3.rs-3029867/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-09-04T04:07:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-17T12:59:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177f37f8-e4ec-45e0-bfd3-603a34e0bab5","date":"2023-07-17T11:55:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-17T11:53:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-17T11:23:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-06-28T12:28:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-28T12:23:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2023-06-06T14:14:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d08ab9d2-dd8e-4be7-ac5b-922c9d106516","owner":[],"postedDate":"July 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-23T15:04:07+00:00","versionOfRecord":{"articleIdentity":"rs-3029867","link":"https://doi.org/10.1186/s12889-023-16892-1","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2023-10-20 15:01:23","publishedOnDateReadable":"October 20th, 2023"},"versionCreatedAt":"2023-07-03 13:50:29","video":"","vorDoi":"10.1186/s12889-023-16892-1","vorDoiUrl":"https://doi.org/10.1186/s12889-023-16892-1","workflowStages":[]},"version":"v1","identity":"rs-3029867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3029867","identity":"rs-3029867","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}