OBJECTIVE To provide reference for the optimization of development policy of traditional Chinese medicine. METHODS ROSTCM text mining analysis software was adopted to screen the literature preliminarily ，the PMC policy index model was built ，and the results by PMC index and visual PMC surface were analyzed. RESULTS & CONCLUSIONS A total of 33 policies related to traditional Chinese medicine were included ，and 10 primary variables and 46 secondary variables were set. The average PMC index of 33 policies was 6.15，of which 2 were excellent policies and the rest were good policies. Among the primary variables ，the scores of policy openness ，policy evaluation and policy field were relatively high ，while the scores of release time ，policy level and policy type were low. The analysis of sink index and secondary variables showed that the macro planning of national policies was not specific enough in terms of policy objectives and contents ，with low score ，while the local policies were usually planned in detail. Generally speaking ，there is still much room for improvement in the policies related to the development of traditional Chinese medicine in terms of policy system construction ，government investment ，talent construction ， financing supporting policies ，market access rules ，responsible subjects and legal guarantee.

Background::Joint dislocations significantly impact public health. However, a comprehensive study on the incidence, distribution, and risk factors for joint dislocations in China is lacking. We conducted the China National Joint Dislocation Study, which is a part of the China National Fracture Study conducted to obtain the national incidence and risk factors for traumatic fractures, and to investigate the incidence and risk factors for joint dislocations.Methods::For this national retrospective epidemiological study, 512,187 participants were recruited using stratified random sampling and probability-proportional-to-size method from January 19 to May 16, 2015. Participants who sustained joint dislocations of the trunk, arms, or legs (skull, sternum, and ribs being excluded) in 2014 were personally interviewed to obtain data on age, educational background, ethnic origin, occupation, geographic region, and urbanization degree. The joint-dislocation incidence was calculated based on age, sex, body site, and demographic factors. The risk factors for different groups were examined using multiple logistic regression.Results::One hundred and nineteen participants sustained 121 joint dislocations in 2014. The population-weighted incidence rate of joint dislocations of the trunk, arms, or legs was 0.22 (95% confidence interval [CI]: 0.16, 0.27) per 1000 population in 2014 (men, 0.27 [0.20, 0.34]; women, 0.16 [0.10, 0.23]). For all ages, previous dislocation history (male: OR 42.33, 95% confidence interval [CI]: 12.03–148.90; female: OR 54.43, 95% CI: 17.37–170.50) and alcohol consumption (male: OR 3.50, 95% CI: 1.49–8.22; female: OR 2.65, 95% CI: 1.08–6.50) were risk factors for joint dislocation. Sleeping less than 7 h/day was a risk factor for men. Compared with children, women aged ≥15 years (female 15–64 years: OR 0.16, 95% CI: 0.04–0.61; female ≥65 years: OR 0.06, 95% CI: 0.01–0.41) were less likely to sustain joint dislocations. Women with more than three children were at higher dislocation risk than women without children (OR 6.92, 95% CI: 1.18–40.78).Conclusions::The up-to-date data on joint dislocation incidence, distribution, and risk factors can be used as a reference for national healthcare, prevention, and management in China. Specific strategies for decreasing alcohol consumption and encouraging adequate sleeping hours should be developed to prevent or reduce dislocation incidents.Trial Registration::Chinese Clinical Trial Registry, ChiCTR-EPR-15005878.

To study and establish a set technology systems of sampling, investigation, quality control, and data analysis of complex sampling for chronic diseases and risk factor surveillance in China based on the requirements of the WHO and China's national conditions, and provide evidence for the policy making and prevention and control evaluation of chronic diseases and technical support for the research of chronic diseases. Through the study of complex sampling technique, adjustment of surveillance points and evaluation of their representatives, a national and provincial representative surveillance system and a complex weighted data analysis were established. According to the relevant plans, actions, and policies in China and other countries, the surveillance content and index system were studied and constructed, which was in line with China's national conditions, "1 + X" steps surveillance, covering the content of questionnaire , physical measurement, and laboratory testing. Based on modern information technology, a three-level platform of information collection and a multi-center laboratory quality control technology system were established, including sampling, information collection, biological sample management, quality control, and result display. Relying on the above research techniques, a national epidemiological investigation was conducted in China, which covered cerebrovascular disease, mental disorders, digestive system disease, and diabetes complications, to obtain the national representative data. This study reflected the innovation of "combination of medicine and prevention" and multi department cooperation in the fields of clinical medicine and public health and provided some big data for the health policy making and the evaluation of the effects of chronic disease prevention and control.

Objective: To quantify the effects of short-term walking intervention on body mass index (BMI), waist circumference, and related indicators of working population. Methods: The China Motivational Healthy Walking Program in 2016 recruited 29 224 individuals from 139 demonstration areas for comprehensive prevention and control of chronic and non-communicable diseases at the national level and 70 areas at the provincial level. All subjects volunteered to participate into this program. The intervention lasted 100 days from June to September 2016. The walking behavior, height, weight and waist circumference of subjects were measured using a uniform pedometer, body composition tester, height tester and waist-to-hip caliper before and after the intervention. Daily average steps, daily average effective steps, and the percent of fulfilling continuous walking per day were used as main indicators in this study. Self-administered questionnaires were used to collect demographic characteristics, behaviors and chronic diseases. With the adjustment of potential confounding factors, multilevel regression was used to quantify the effect of walking on BMI and waist circumference, and multilevel logistic regression was used to quantify the effect on overweight or obesity and central obesity. Results: A total of 12 368 subjects completed walking interventions and all measurements included in this study. The mean±SD age of all subjects was (41.19±8.99) years, and 5 155 (59.17%) of them were women. After 100-day intervention, all outcome variables decreased significantly. For every additional 1 000 steps per day, subjects would have extra decrease in BMI of 0.023 kg/m² [β (95%CI): -0.023 (-0.030, -0.017)], in waist circumference of 0.046 cm [β (95%CI): -0.046 (-0.071, -0.020)], and the likelihood for becoming overweight or obesity was 0.97 times [OR (95%CI): 0.97 (0.95, 0.98)] that of before intervention. With additional 1 000 effective steps per day, the difference between baseline and endpoint increased by 0.028 kg/m² [β (95%CI): -0.028 (-0.035, -0.020)] in BMI and 0.062 cm [β (95%CI): -0.062 (-0.091, -0.033)] in waist circumference. The likelihood of progressing into overweight or obesity was 0.97 times [OR (95%CI): 0.97(0.95, 0.98)] that of before-intervention, and the likelihood of becoming central obesity was 0.98 times [OR (95%CI): 0.98(0.96, 0.99)] that of before-intervention. Compared to subjects with low level percent of fulfilling continuous walking per day, those with high level would have an extra decrease in BMI by 0.150 kg/m² [β (95%CI): -0.150 (-0.22, -0.079)], in waist circumference by 0.340 cm [β (95%CI): -0.340 (-0.620, -0.064)], and the likelihood of becoming overweight or obesity decreased to 0.74 times that of the low level group [OR (95%CI): 0.74(0.62, 0.89)] and the likelihood of becoming central obesity decreased to 0.78 times that of the low level group [OR (95%CI): 0.78 (0.68, 0.91)]. Conclusion Strengthening walking exercise in the short term has a positive effect on promoting working population to reduce BMI and waist circumference, and prevent overweight, obesity and central obesity.

Objective To quantify the effects of short?term walking intervention on body mass index (BMI), waist circumference, and related indicators of working population. Methods The China Motivational Healthy Walking Program in 2016 recruited 29 224 individuals from 139 demonstration areas for comprehensive prevention and control of chronic and non?communicable diseases at the national level and 70 areas at the provincial level. All subjects volunteered to participate into this program. The intervention lasted 100 days from June to September 2016. The walking behavior, height, weight and waist circumference of subjects were measured using a uniform pedometer, body composition tester, height tester and waist?to?hip caliper before and after the intervention. Daily average steps, daily average effective steps, and the percent of fulfilling continuous walking per day were used as main indicators in this study. Self?administered questionnaires were used to collect demographic characteristics, behaviors and chronic diseases. With the adjustment of potential confounding factors, multilevel regression was used to quantify the effect of walking on BMI and waist circumference, and multilevel logistic regression was used to quantify the effect on overweight or obesity and central obesity. Results A total of 12 368 subjects completed walking interventions and all measurements included in this study. The mean ± SD age of all subjects was (41.19 ± 8.99) years, and 5 155 (59.17%) of them were women. After 100?day intervention, all outcome variables decreased significantly. For every additional 1 000 steps per day, subjects would have extra decrease in BMI of 0.023 kg/m2 [β (95%CI ): -0.023 (-0.030, -0.017)], in waist circumference of 0.046 cm [β (95%CI ):-0.046 (-0.071, -0.020)], and the likelihood for becoming overweight or obesity was 0.97 times [OR (95%CI): 0.97 (0.95, 0.98)] that of before intervention. With additional 1 000 effective steps per day, the difference between baseline and endpoint increased by 0.028 kg/m2 [β (95%CI ): -0.028 (-0.035,-0.020)] in BMI and 0.062 cm [β (95%CI):-0.062 (-0.091,-0.033)] in waist circumference. The likelihood of progressing into overweight or obesity was 0.97 times [ OR (95%CI ): 0.97(0.95, 0.98)] that of before?intervention, and the likelihood of becoming central obesity was 0.98 times [OR (95%CI): 0.98(0.96, 0.99)] that of before?intervention. Compared to subjects with low level percent of fulfilling continuous walking per day, those with high level would have an extra decrease in BMI by 0.150 kg/m2 [β (95%CI):-0.150 (-0.22,-0.079)], in waist circumference by 0.340 cm [β (95%CI ):-0.340 (-0.620,-0.064)], and the likelihood of becoming overweight or obesity decreased to 0.74 times that of the low level group [ OR (95%CI ): 0.74(0.62, 0.89)] and the likelihood of becoming central obesity decreased to 0.78 times that of the low level group [ OR (95%CI ): 0.78 (0.68, 0.91)]. Conclusion Strengthening walking exercise in the short term has a positive effect on promoting working population to reduce BMI and waist circumference, and prevent overweight, obesity and central obesity.

Objective To quantify the effects of short?term walking intervention on body mass index (BMI), waist circumference, and related indicators of working population. Methods The China Motivational Healthy Walking Program in 2016 recruited 29 224 individuals from 139 demonstration areas for comprehensive prevention and control of chronic and non?communicable diseases at the national level and 70 areas at the provincial level. All subjects volunteered to participate into this program. The intervention lasted 100 days from June to September 2016. The walking behavior, height, weight and waist circumference of subjects were measured using a uniform pedometer, body composition tester, height tester and waist?to?hip caliper before and after the intervention. Daily average steps, daily average effective steps, and the percent of fulfilling continuous walking per day were used as main indicators in this study. Self?administered questionnaires were used to collect demographic characteristics, behaviors and chronic diseases. With the adjustment of potential confounding factors, multilevel regression was used to quantify the effect of walking on BMI and waist circumference, and multilevel logistic regression was used to quantify the effect on overweight or obesity and central obesity. Results A total of 12 368 subjects completed walking interventions and all measurements included in this study. The mean ± SD age of all subjects was (41.19 ± 8.99) years, and 5 155 (59.17%) of them were women. After 100?day intervention, all outcome variables decreased significantly. For every additional 1 000 steps per day, subjects would have extra decrease in BMI of 0.023 kg/m2 [β (95%CI ): -0.023 (-0.030, -0.017)], in waist circumference of 0.046 cm [β (95%CI ):-0.046 (-0.071, -0.020)], and the likelihood for becoming overweight or obesity was 0.97 times [OR (95%CI): 0.97 (0.95, 0.98)] that of before intervention. With additional 1 000 effective steps per day, the difference between baseline and endpoint increased by 0.028 kg/m2 [β (95%CI ): -0.028 (-0.035,-0.020)] in BMI and 0.062 cm [β (95%CI):-0.062 (-0.091,-0.033)] in waist circumference. The likelihood of progressing into overweight or obesity was 0.97 times [ OR (95%CI ): 0.97(0.95, 0.98)] that of before?intervention, and the likelihood of becoming central obesity was 0.98 times [OR (95%CI): 0.98(0.96, 0.99)] that of before?intervention. Compared to subjects with low level percent of fulfilling continuous walking per day, those with high level would have an extra decrease in BMI by 0.150 kg/m2 [β (95%CI):-0.150 (-0.22,-0.079)], in waist circumference by 0.340 cm [β (95%CI ):-0.340 (-0.620,-0.064)], and the likelihood of becoming overweight or obesity decreased to 0.74 times that of the low level group [ OR (95%CI ): 0.74(0.62, 0.89)] and the likelihood of becoming central obesity decreased to 0.78 times that of the low level group [ OR (95%CI ): 0.78 (0.68, 0.91)]. Conclusion Strengthening walking exercise in the short term has a positive effect on promoting working population to reduce BMI and waist circumference, and prevent overweight, obesity and central obesity.

Objective: To examine the effects and associated factors of the China Motivational Healthy Walking Program among occupational population. Methods: The 2016 China Motivational Healthy Walking Program recruited 29 224 participants from 139 demonstration areas for comprehensive prevention and control of chronic and non-communicable disease at national level and 70 at provincial level. Intervention on walking was carried out by adopting group and individual motivating measures. Walking steps were recorded by electronic pedometer. We used percent of days achieving 10 000 steps (P10 000), percent of days fulfilling continuous walking (PCW), and proportion of valid walking (PVW) steps to reflect walking quantity, pattern and quality of participants. Motivation intensity was measured by summing up scores of each motivating activity. Questionnaire-based online survey collected information about demographic characteristics, lifestyle risk factors and chronic diseases. This study finally included 12 368 individuals in the analysis. Multilevel logistic regression model was used to assess the effect of group and individual motivating measures on walking activity and corresponding associated factors. Results: Age of the study sample was (41.2±8.99) years, and 58.17% (7 194) of them were female. After 100-day intervention, the P10 000, PCW and PVW of all participants were 93.89%±14.42%,92.01%±15.97% and 81.00%±7.45%, respectively. The mean P10 000 and PCW increased with rising group-motivated scores, self-motivated scores and individual-activity scores (P<0.001 for all). The mean PVW decreased with both higher group-motivated scores and self-motivated scores (both P<0.05), and varied little among groups with different level individual-activity scores (P=0.525). According to the results from the multilevel model, those who had greater group-motivated scores and self-motivated scores tended to have more likelihood of high-level of P10 000 and PCW. Age, sex, smoking status, education attainment and alcohol drinking were associated with P10 000 and PCW (P<0.05 for all). Conclusion The Motivational Healthy Walking Program had positive effect on promoting healthy walking among occupational population. Group-motivated and self-motivated activities were associated with healthy walking.

Objective: To investigate the prevalence of hypercholesterolemia among Chinese adults in different geographic areas, and to analyze the related factors. Methods: China Chronic and Non-Communicable Disease and Risk Factor Surveillance was conducted in 2013, based on 298 counties/districts in 31 provinces of Chinese mainland. The adults aged 18 years old were randomly selected using multi-stage stratified clustering sampling method. Information on chronic disease and risk factors was collected using face-to-face questionnaire interview and physical measurement. Blood samples were collected by local staffs. Serum total cholesterol (TC) was determined using standard method in a central laboratory. After excluding 565 participants missing key variables and 1 558 participants with abnormal TC values, a total of 174 976 participants were included. Weighted prevalence of hypercholesterolemia was calculated. Hypercholesterolemia related individual or geographic determinants were defined using multilevel logistic regression. Results: The prevalence of hypercholesterolemia in Chinese adults age 18 years old and above was 6.9% (95%CI: 6.4%-7.3%), ranged from 3.0% (95%CI: 2.5%-3.4%) in the northwest of China to 14.2% (95%CI: 12.9%-15.5%) in the south (χ²=183.42, P<0.001). The prevalence of hypercholesterolemia was higher in 6 provinces including Tianjin, Liaoning, Fujian, Guangdong, Guangxi, and Hainan (≥9.0%), but lower in 7 provinces including Shanxi, Shannxi, Gansu, Qinghai, Ningxia, Xinjiang, and Tibet (<3.9%). There was a 1.27 fold variation in hypercholesterolemia prevalence by provincial level, and 72.5% of the geographical variation in hypercholesterolemia prevalence was account for by area-level determinants. With multilevel logistic analysis, the individual risk factors associated with hypercholesterolemia included aging, higher education level or annual household income per capita, regular drinking, too much red meal intake, inactivity, overweight or obesity. For geographic factors, residents living at south China, counties/districts with higher urbanization rates, higher education level or lower standardized death rates were more likely to have hypercholesterolemia (all P<0.05). Conclusion The prevalence of hypercholesterolemia was high in Chinese adults, it was different between regions and related with characteristics of population, individual behaviors and geographical regions.

Objective: To investigate the geographical variation of prediabetes in adults in different regions of China, and to analyze the related factors of prediabetes. Methods: Data was obtained from China Chronic Disease and Related Risk Factor Surveillance in 2013. The surveillance adopted multiple-stage stratified cluster random sampling method, which sampled 177 099 residents aged above 18 years old among 298 surveillance points in 31 provinces of Chinese Mainland. Questionnaire interview was used to obtain demographic variables, personal living style, and socio-economical information. Physical examination was conducted and fasting venous blood sample and (oral glucose tolerance test-2 hours, OGTT-2 h) venous blood sample were obtained from the participants. A total of 171 567 residents aged 18 and above were included in the analysis. The prevalence of prediabetes was analyzed by provinces and by China's geographical regions, after complex weighting. Multilevel logistic models were established to explore the related factors of prediabetes on the area level and individual level. Results: The prevalence of prediabetes among residents aged 18 and above was 16.6% (95%CI: 15.6%-17.6%) in China. The prevalence of prediabetes was the highest (18.3%) in the south China and lowest (13.1%) in the northwest area. The difference of the prevalence in different areas were not statistically significant (P=0.510). If categorized the prevalence of prediabetes into 5 groups by quintile, Hainan, Jilin, Shandong, Anhui, Hunan and Chongqing were in the highest group of prevalence of prediabetes (18.6%-22.7%), and Tibet, Qinghai, Gansu, Ningxia, Guizhou, and Jiangxi were in the lowest group (7.6%-12.6%). The variance of prevalence of prediabetes on the county level (MOR: 1.60 (95%CI:1.53-1.67)) was more diverse than the province level (MOR: 1.21(95%CI:1.08-1.29)) and higher than the street level (1.23 (95%CI:1.14-1.30)). Several factors increased risk of pre-diabetes, including smoking, hazardous drinking and harmful drinking, drinking in the past 30 days, overweight, obesity, central obesity, sugary drink intake, hypertension, high total cholesterol, high triglycerides, high blood low-density lipoprotein cholesterol, low blood high-density lipoprotein cholesterol (all P<0.05). After adjusted the above variables, 92.5% of variance of prediabetes prevalence conld be explained on the provincial level. Conclusion The geographical distribution of prediabetes in adults in China differed by geographic areas, and it significantly varied on the county level. The related variables included demographic variables, personal behavior, and geographic related variables.

Objective: To evaluate the provincial representativeness of China Non-communicable and Chronic Disease Risk Factor Surveillance System, 2013. Methods: The Sixth National Population Census data which was collected by National Bureau of Statistics of People's Republic of China was used to calculate proportion of population who aged 65 and above, mortality rate, the proportion of non-agriculture population, the illiteracy rate and urbanization rate in order to evaluate the surveillance system in each province. The Mann-Whitney U test was used to determine the statistically differences between the surveillance system and corresponding general population. Results: Among the 298 disease surveillance points (DSPs) in China Non-communicable and Chronic Disease Risk Factor Surveillance System, there were 111, 85, and 102 DSPs located in the east, middle, and west area of China, which covering 13.90%, 11.48%, and 12.28% of the total population, respectively. The surveillance system covered 169 million of the population of China, accounting for 12.70% of Chinese population. The number of DSPs by provinces ranges from 6 (Hainan, Qinghai, and Ningxia) to 14 (Shandong, Guangdong and Henan). It indicated that mortality rate (DSP: 0.238%; Province: 0.482%) and the illiteracy rate (DSP: 15.54%; Province: 26.22%) among DSPs in Tibet were significantly lower than the provincial level, on the other hand, the proportion of non-agriculture population among DSPs (40.6%) was significantly higher than the provincial level (18.8%). The urbanization rate among Jiangxi DSPs (43.4%) was significantly lower than the provincial level (59.9%). The proportion of non-agriculture population among Shandong DSPs (32.8%) was significantly higher than the provincial level (24.2%), however, the illiteracy rate among Shandong DSPs (3.86%) was significantly lower than the provincial level (5.25%). Other than the provinces mentioned above, there was no statistical differences (P>0.05) among proportions of population who aged 65 and above, mortality rates, the proportions of non-agriculture population, the illiteracy rates and urbanization rate between provincial surveillance system and corresponding area. Conclusion Other than 3 provinces, in general, China Non-communicable and Chronic Disease Risk Factor Surveillance System had provincial representativeness.