中华疾病控制杂志

Spatial-temporal distribution and influencing factors of Brucellosis in China

MA Yunlong, LIU Ying, LI Xiaolong, SHAO Lina, YANG Tong, MA Xiaoxia, ZHAI Changsheng, ZHAO Yu

2023, 27(11): 1241-1246. doi: 10.16462/j.cnki.zhjbkz.2023.11.001

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Objective To explore the spatiotemporal distribution characteristics and influencing factors of the incidence of Brucellosis in China from 2004 to 2019, and to provide a theoretical reference for the formulation of prevention and control policies and taking accurate control measures in the high-risk areas of Brucellosis in China. Methods Based on the standardized morbidity ratio (SMR) of Brucellosis in 31 provinces (municipalities and autonomous regions) in China from 2004 to 2019, spatial autocorrelation analysis and spatial regression analysis were carried out to explore the temporal and spatial distribution characteristics and influencing factors of Brucellosis in China. Results The incidence of Brucellosis in China showed an increasing trend first and then decreased from 2004 to 2019. Spatial autocorrelation analysis showed that the distribution of Brucellosis in China from 2004 to 2019 had a significant positive spatial clustering. The "High-High" clusters were mainly located in the Northern regions of Inner Mongolia and its neighboring provinces, while the "Low-Low" clusters were mainly distributed in the Southern regions such as Hunan, Guizhou, and Guangdong. In addition, spatial regression analysis indicated that the average temperature, relative humidity, cultivated land area, and livestock feeding were positively correlated with the national Brucellosis SMR, while the population, per capita gross domestic product and forest area were negatively correlated. Conclusions The results suggest that targeted prevention and control measures in key areas, epidemic prevention monitoring for different animal species, and the enhancement of health education and self-prevention awareness can reduce the occurrence of Brucellosis.

Spatial autocorrelation and spatial-temporal clustering analysis of pertussis in Anhui Province from 2016 to 2021

LI Tao, HOU Lijuan, LI Qingru, XUAN Kun, PANG Xingya, ZHA Zhenqiu, TANG Jihai

2023, 27(11): 1247-1253. doi: 10.16462/j.cnki.zhjbkz.2023.11.002

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Objective To analyze the temporal and spatial clustering and spatial correlation of pertussis in Anhui Province from 2016 to 2021 by using geographic information system (GIS), and to provide reference for prevention and control. Methods The reported cases of pertussis in Anhui Province from 2016 to 2021 were collected from the Chinese Disease Control and Prevention Information System, and a database was established by GIS to analyze spatial autocorrelation and spatiotemporal clustering. Results From 2016 to 2021, a total of 923 cases of pertussis were reported in Anhui Province, with an average annual reported incidence of 0.25/100 000; the peak incidence was from May to August every year; Spatial autocorrelation analysis showed that the incidence of pertussis in Anhui Province in 2016, 2018, 2019 and 2021 showed significant spatial clustering. In 2016 and 2018-2021, a total of 28 "High-High" aggregation areas were scanned; It was mainly concentrated in some counties and districts of Lu′an, Wuhu, Xuancheng, Huangshan, Chuzhou, Bengbu, Hefei and Huainan; A total of 13 significant aggregation areas were scanned for the incidence of pertussis in Anhui Province from 2016 to 2021, and the Class Ⅰ aggregation areas were mainly located in some counties and districts of Lu′an, Bengbu, Chuzhou, Wuhu, Ma′anshan, and Hefei. The gathering time was mainly from June to September, and the gathering continued until December in 2018 and 2021. Conclusions From 2016 to 2021, the incidence of pertussis in Anhui Province is mainly gathered in summer. The agglomeration area is mainly concentrated in the inter-provincial border counties and districts, and the provincial capital with convenient transportation and around large cities. The monitoring and prevention work in key areas should be strengthened.

Study on the relationship between the incidence of hand-foot-mouth disease and meteorological factors in Nanning City based on the distribution lag nonlinear model

LIANG Dongxu, KANG Qiang, ZENG Yi, XIE Zhichun

2023, 27(11): 1254-1261. doi: 10.16462/j.cnki.zhjbkz.2023.11.003

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Objective This study aims to investigate the influence and lag effects of meteorological factors on the incidence of hand-foot-mouth disease disease (HFMD). Methods Daily HFMD incidence data and concurrent meteorological data from 2010 to 2019 were collected and analyzed. The distributed lag non-linear model (DLNM) was utilized to examine the correlation between HFMD incidence and meteorological factors in Nanning. Results From 2010 to 2019, a total of 508 984 HFMD cases were reported in Nanning, with primary peaks observed from April to June and secondary peaks from September to October. The incidence rate for males to females was 1.57∶1, primarily among nursery-aged and home-based children. HFMD incidence exhibited a positive correlation with average temperature, relative humidity, and sunshine duration, and a negative correlation with atmospheric pressure. At 35.0 ℃, the mean temperature exhibited the highest RR value on the 9th day (RR=1.058, 95% CI: 1.037-1.079); At 86.0%, the relative humidity peaked on the 14th day (RR=1.011, 95% CI: 1.007-1.014); Atmospheric pressure displayed a "double peak" phenomenon, with the highest RR value at 990.0 hPa on the 7th day (RR=1.022, 95% CI: 1.017-1.027), and at 1 030.0 hPa on the 5th day (RR=1.054, 95% CI: 0.988-1.124); At 7.0 h/d, the sunshine duration revealed the maximum RR value on the 5th lag day (RR=1.017, 95% CI: 1.009-1.025). Notably, extreme heat, high-pressure, cold, and dry effects also demonstrated a significant lag effect on the onset of HFMD. Conclusions HFMD presents a severe epidemic situation in Nanning. The incidence of HFMD is significantly associated with meteorological factors. Meteorological factors can be leveraged to predict the disease′s development trend.

Application of spatial filtering model based on different spatial weight settings in hand-foot-mouth disease incidence data

CHEN Xi, LI Ke, YIN Yun, LIU Yuanhua, HONG Jie, SHI Jin, HUANG Jiaqi, ZHAO Zheng, XU Jiayao, YUAN Rui, ZHANG Zhijie

2023, 27(11): 1262-1267. doi: 10.16462/j.cnki.zhjbkz.2023.11.004

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Objective To study the application of spatial filtering model in the incidence data of hand-foot-mouth disease (HFMD) in East China given different spatial weight, and to determine its applicability by comparing the effects of different spatial models. Methods The incidence data of hand, foot and mouth disease in East China in 2009 were collected and the related influencing factors were identified. Four different spatial weight matrices were decomposed using the eigenvector spatial filtering method (ESF), and the eigenvectors were determined according to Moran′s I(MI) value and stepwise regression, which was introduced as the spatial filter into the model. The effects of different weight matrices were compared by Akaike information criterion (AIC), deviance information criterion (DIC) and Root Mean Square Error (RMSE). Finally, the spatial filtering model based on the optimal weight matrix was compared with the Bayesian spatial model in terms of the fitting value, standard deviation and confidence interval of the model coefficients. Results There were a total of 403 607 HFMD cases reported in East China in 2009, most of which concentrated in the west of Shandong Province and the southeast of Zhejiang Province. According to MI test, HFMD exhibited spatial correlation in East China. After the spatial filter was introduced into the normal negative binomial distribution model, the residual of the spatial filter model ceased to show spatial autocorrelation (MI were -0.11, -0.15, -0.08 and -0.09, respectively, all P>0.05), and the spatial autocorrelation was effectively removed. The Rook weight matrix was considered the optimal weight matrix. Although, the regression coefficient of the spatial filtering model under the optimal weight matrix were comparable to that of the Bayesian spatial model, the spatial filtering model was still significantly outweighed by the Bayesian spatial model in terms of standard deviation and confidence interval. Conclusions The spatial filtering model demonstrates the advantages of simple calculation and accurate results. Therefore, it can be applied to visualize the map patterns at different geographic scales from whole to local, and to reveal the underlying spatial structure of disease onset. It is also applicable as an effective alternative to traditional complex spatial models.

Structural equation model analysis of category Ⅱ vaccine behavior based on knowledge-attitude-practice theory

ZHANG Mengxi, XU Shuangfei, YANG Kunjie, TANG Jian, XU Ying, WANG Jianhua, ZHU Wenlong, WANG Shilan, WANG Weibing

2023, 27(11): 1268-1273. doi: 10.16462/j.cnki.zhjbkz.2023.11.005

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Objective To provide suggestions for promoting the practice of category Ⅱ vaccine, the knowledge, attitude, practice (KAP) as well as impact factors of category Ⅱ vaccine among parents of school-aged children in Tongren City, Guizhou Province were assessed. Methods Stratified sampling was used to select several primary schools in one district and one country of Tongren City, Guizhou Province. A structured questionnaire was used to investigate the parents′ KAP of category Ⅱ vaccine and the structural equation model was used to explore the association of parents′ KAP. Results A total of 517 valid questionnaires were received in this study. Parents in the vaccinated group had a higher rate of correct answers to questions about the effectiveness of category Ⅱ vaccine than those in the unvaccinated group. Parents in the vaccinated group were more likely to agree on the effectiveness of category Ⅱ vaccine, while parents in the unvaccinated group were more concerned about the risk of category Ⅱ vaccine. Higher parental educational level (γ=0.266), higher agreement on the effectiveness of the vaccine (β=0.469), and opposition to the risks of the vaccine promoted vaccination behavior for the category Ⅱ vaccine (β=0.398); parental education negatively affected parental attitudes toward the effects of the vaccine (γ=-0.725). Conclusions Attention should be paid to poorly educated parents to help them properly understand knowledge about category Ⅱ vaccine. For parents with higher education level, emphases should be placed on the explanation of the safety and effectiveness of category Ⅱ vaccine, in order to dispel parents′ doubts and promote practice of category Ⅱ vaccines.

Application and comparison of time-varying SEIQDR and ARIMA models in COVID-19 prediction in Shanghai

XU Shujun, MA Yifei, LUO Yuxin, GUO Jiaming, WANG Tong, LI Jiantao, LEI Lijian, HE Lu, YU Hongmei, XIE Jun

2023, 27(11): 1274-1281. doi: 10.16462/j.cnki.zhjbkz.2023.11.006

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Objective Based on the time-varying susceptible-exposed-infected-quarantined-dead-removed (SEIQDR) model and autoregressive integrated moving average (ARIMA) model, the Omicron infection data in Shanghai were studied and judged. The two models′ fitting, prediction effect and applicability were analyzed and compared. And the prediction model with a better effect and suitable for developing the epidemic in Shanghai, China was selected. Methods Data from March 1, 2022 to April 20, 2022, for newly infected cases in Shanghai were selected for fitting. Data from April 21, 2022 to May 30, 2022, were used to evaluate the prediction effect of the model. Time-varying SEIQDR models and ARIMA models were constructed, respectively. The models′ fitting and prediction effects were evaluated by comparing the magnitudes of R², MAE, and RMSE. Results Both the time-varying SEIQDR model and the ARIMA model had a better fitting effect, with R² of 0.990 and 0.984, respectively. The 5-day predictions of both models were fair, and the time-varying SEIQDR model was better and more consistent with the pattern of infectious disease transmission for the 20-day and 40-day predictions, with the 40-day predicted MAE and RMSE of 1 001.461 and 1 967.704, respectively, for the former and 1 265.331 and 2 068.094, respectively, for the latter. The time-varying SEIQDR model was able to achieve a more complete replication of the trend of the current epidemic and the number of incidences in Shanghai. Conclusions The time-varying SEIQDR model can better fit and predict the number and trend of COVID-19 in Shanghai, and the model effect is better than that of ARIMA(2, 2, 0).

A study on the prevalence rate of anxiety and depression status and influencing factors of COVID-19 patients in Fangcang shelter hospitals in Hainan

CHEN Hao, LI Haowei, LI Xuehang, YANG Lei, YE Zifeng, WANG Shengshu, YANG Shanshan, CHEN Shimin, LIU Shaohua, LI Rongrong, YANG Junhan, LI Huaihao, BAO Yinghui, SHI Yueting, WANG Jianhua, LIU Miao, HE Yao

2023, 27(11): 1282-1288. doi: 10.16462/j.cnki.zhjbkz.2023.11.007

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Objective To investigate the anxiety and depression status of asymptomatic and light COVID-19 patients admitted to Fangcang shelter hospitals in Hainan during the COVID-19 outbreak, and analyze the related influencing factors. Methods From August to October 2022, an online survey of anxiety and depression status using the Questionnaire Star program was conducted. The severity of anxiety and depression were assessed by Self-Rating Anxiety Scale and Self-Rating Depression Scale, respectively. A multivariable logistic regression model was used to analyze the influencing factors. Results Of the 569 patients included, 14.9% and 55.4% had COVID-19-related anxiety and depression symptoms, mainly mild and moderate. Multivariable logistic regression analysis showed that female sex, being married, alcohol consumption and past medical history were independent risk factors for anxiety symptoms (all P < 0.05). Additionally, being married, manual occupation and living alone were risk factors, while frequent physical exercises appeared as protective factors (all P < 0.05) related to depression symptoms. Conclusions The COVID-19 outbreak resulted in a related widespread increase in anxiety and depression in patients admitted to Fangcang shelter hospitals, and early identification and intervention of adverse psychological status in high-risk groups should be achieved.

Analysis of the disease burden and changing trends of road traffic injuries in Chinese population from 1990 to 2019

REN Yitao, ZHU Rongfang, LIN Jinxiong, SHEN Shuqun

2023, 27(11): 1289-1295. doi: 10.16462/j.cnki.zhjbkz.2023.11.008

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Objective To analyze the changing trend of disease burden of road traffic injuries in Chinese population from 1990 to 2019, and to propose a basis for the prevention and control of road traffic injuries in China. Methods The Global Burden of Disease Database (GBD) was used to select the number of deaths, mortality rate, disability adjusted of life years (DALYs), years lost due to disability (YLDs), years of life lost (YLLs), average annual percentage change (AAPC), DALYs rate, YLDs rate, and YLLs rate of road traffic injuries in China from 1990 to 2019. Joinpoint models were used to analyze the disease burden of road traffic injuries in Chinese population and the trend of change from 1990 to 2019. Results The mortality rate (AAPC=-0.47%, P < 0.05), DALYs rate (AAPC=-0.87%, P < 0.05), and YLLs rate (AAPC=-1.47%, P < 0.05) of road traffic injuries in Chinese population from 1990 to 2019 showed a trend of increasing and then decreasing, while the YLDs rate (AAPC=2.85%, P < 0.05) increased year by year. The number of traffic injury deaths in Chinese population in 2019 was 25.00 thousand. YLLs due to road traffic injuries accounted for 81.64% of DALYs. The numbers of deaths (rate) and YLLs (rate) in men were three times higher than those in women, and the YLDs and YLDs rates were also significantly higher than those in women. In China, the road traffic death rate, DALYs rate and YLLs rate among children aged 0 to 14 have all shown a decrease. In the elderly population, the death rate and YLLs rate exhibited a upward trend in individuals above the age of 65, while the DALYs rate displayed an increasing trend in the elderly population up to the age of 60. Additionally, the YLDs rate demonstrated an upward trend across all age groups. Conclusions The overall trend of road traffic injury disease burden in China′s population from 1990 to 2019 is decreasing. However, the overall road traffic injury disease burden is still high. The road traffic injury disease burden is increasing in old age group. The road traffic injury disease burden is higher in men than that in women. The traffic injury prevention and control measures should be strengthened.

Prediction of sarcopenia based on longitudinal physical examination data

YUE Yibing, YU Ying, SHEN Lei, WANG Yan, WANG Yingying, LYU Weibo, LIU Chuang

2023, 27(11): 1296-1299. doi: 10.16462/j.cnki.zhjbkz.2023.11.009

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Objective In the elderly, sarcopenia is a common disease and efficient identification of sarcopenia is very important to keep health. Methods Based on the longitudinal physical examination data, a total of 2 544 subjects in a hospital from Shanghai during 2013-2019 were included. Considering the difference across annual indices, various machine learning models were constructed to predict the risk of sarcopenia in the elderly, and the decision curve analysis was applied to provide reference for clinical decision makers. Results The prediction models results showed that the prediction accuracy based on the Light Gradient Boosting Machine (LightGBM) model was relatively high (AUC=0.913 4). Decision curve analysis indicated that the net profit of the LightGBM model became larger when threshold probability (threshold for judging sarcopenia) ranged from 0.01 to 0.42 and from 0.84 to 0.92. And the net profit of the Random Forest model was larger when threshold probability ranged from 0.42 to 0.50 and from 0.60 to 0.67, while in the case of logistic regression model, the range was located in 0.50-0.60 and 0.67-0.84. Conclusions The prediction model established based on longitudinal physical examination data and machine learning methods can effectively predict the future risk of sarcopenia in the elderly, and is of great value for the early diagnosis and intervention of sarcopenia.

Research on the priority of measures to promote physical activity based on IPA decision model

YANG Jing, LI Jing, FENG Jiayu, XU Xiaohui, XU Tingling, ZHANG Yanbo, ZHOU Maigeng, DONG Wenlan

2023, 27(11): 1300-1307. doi: 10.16462/j.cnki.zhjbkz.2023.11.010

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Objective To explore the priority of physical activity promotion measures in China and provide a theoretical basis for developing strategies and plans on physical activity promotion. Methods Key personnel from district-level Centers for Disease Control and Prevention (CDCs) in 488 national demonstration areas for comprehensive control and prevention of chronic diseases were asked to rate the feasibly and importance of 47 physical activity promotion measures under 13 dimensional indicators. The Importance-Performance Analysis (IPA) model was used to divide the dimensional indicators of physical activity promotion into quadrants, and the standardized factor loading coefficients of the second-order Confirmatory Factor Analysis were used to determine the priority of the dimensional indicators in the same quadrant, and the priority of the measures in each dimension. Results In the highest priority quadrant, the priority order of the dimensional indicators was capacity building, workplace intervention, school intervention, communications and community events, sports sector intervention, and improved facilities/spaces accessibility. In the quadrant for prioritized improvement, the only indicator was environment improvement. In the quadrant for the least prioritized improvement, the priority order was interventions in other settings and population, leadership and partnership, healthcare intervention, enhanced building design, financing and fiscal support, and monitoring and evaluation. Conclusions Combining confirmatory factor analysis, IPA model can work well in constructing the decision model for physical activity promoting.

A comparative study of the RNN, the JPR, and ARIMA for predicting maternal mortality ratio in rural areas in China

YIN Xiaolan, HE Xinxin, DU Lin, LI Yuansheng, ZHANG Junhui

2023, 27(11): 1308-1313. doi: 10.16462/j.cnki.zhjbkz.2023.11.011

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Objective To explore the application value of the recurrent neural network (RNN) model, the joinpoint regression (JPR) model, and the autoregressive integrated moving average (ARIMA) model on predicting maternal mortality ratio (MMR) in rural areas in China, and to make statistical predictions on whether the MMR decrease targets of "Healthy China 2030" and other documents will be achieved or not. Methods The RNN, JPR, and ARIMA models were constructed based on the data of MMR in rural areas in China from 2000 to 2019, and the mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) were used to compare the back generation fitting effects of the three models. The residuals and relative errors were used to compare the point prediction effects of the three models for MMR data in rural areas in 2020. Finally, the optimal model was selected to forecast the MMRs in rural areas from 2021 to 2030. Results From 2000 to 2020, the MMRs in rural areas in China showed an overall continuous downward trend. The back generation fitting effects of the three models were ranked in descending order as follows: RNN > JPR > ARIMA, and the MAE, MSE, and RMSE of the RNN models were all less than 0.02. The accuracies of the three models for the point prediction of rural MMR in 2020 were ranked in descending order as follows: RNN > JPR > ARIMA. The prediction results of the optimal RNN model showed that the rural MMR in 2022 would be 18.02/100 000, indicating the decreased target of the relevant documents would be achieved in 2022. The MMR in 2025 and 2030 would be 17.58/100 000 and 17.27/100 000, respectively, indicating the decrease targets of the "Health China 2030" and other documents would not be achieved in 2025 and 2030. Conclusions The predictive performance of the RNN model is much better than those of the JPR model and the ARIMA model. The JPR model is an acceptable predictive model, while the ARIMA model is less suitable for the prediction of this data.

Effects of gender and age on sibling relationship of first-born children at preschool age

XIAN Danxia, ZHAO Yafen, ZHANG Yan, ZHANG Jingyu, YIN Xiaona, QIU Shuangyan, WU Jianbo, WEN Guoming, LU Dali

2023, 27(11): 1314-1319. doi: 10.16462/j.cnki.zhjbkz.2023.11.012

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Objective To study the effects of gender and age on sibling relationships of first-born children at preschool age. Methods A cluster sampling method was used to select the first-born children aged 3-6 years with younger brothers/sisters at all the kindergartens in Longhua District of Shenzhen City. A total of 8 449 cases were investigated, of which 8 419 cases were effectively completed. A self-made questionnaire was used to collect general demographic data. The sibling inventory of behavior (SIB) scale, including three positive dimensions (companionship, empathy and teaching) and three negative dimensions (rivalry, aggression and avoidance), was used to explore the sibling relationship. Results The scores of sibling aggression in first-born boys were higher than those in girls; however, the scores of positive sibling relationships, including companionship, empathy and teaching, were lower than those in girls (all P＜0.05). The competition dimension score of a same-sex sibling was significantly higher than that of a heterosexual sibling (all P＜0.05). There were significant differences across all six dimensions of sibling relationships among different sibling combinations. With the increase of age, the scores of sibling avoidance significantly decreased, while the scores of the three positive dimensions, including companionship, empathy and teaching, increased considerably (all P＜0.05). Hierarchical regression analysis showed statistical significance in the empathy and teaching of the final model (model 3), which includes three variables (gender, age and sibling relationship) [R²=0.011, F=32.396 (P＜0.001); R²=0.021, F=58.921 (P＜0.001), respectively]. Conclusions The gender and age of first-born children may impact the development of sibling relationships. First-born girls and children aged 5-6 are more likely to develop positive sibling relationships.

Influencing factors analysis and prediction model construction of medical students′ suicidal ideation based on machine learning algorithm

ZHANG Yilin, WANG Chao, LI Mengdie, LIU Zhaorui, LYU Juncheng

2023, 27(11): 1320-1328. doi: 10.16462/j.cnki.zhjbkz.2023.11.013

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Objective The purpose of this study is to understand the factors influencing suicidal ideation among medical students and to explore the predictive effect of machine learning (ML) algorithms. Methods A random stratified whole-group sampling of medical students in Shandong Province was conducted from November 2021 to March 2022 to conduct the questionnaire survey. The Chi-square test, Fisher′s exact probability method, and Logistic regression were used to explore the factors influencing suicidal ideation among medical students. The prediction models were constructed based on a machine learning algorithm in the training set. The predictive ability of the model was evaluated in the test set based on accuracy, sensitivity, and so on. Results The suicidal ideation rate in medical students was 12.80%. The results showed that living in rural areas (OR=1.523, 95% CI: 1.023-2.271, P=0.039), and depression symptoms (OR=3.874, 95% CI: 2.676-5.621, P < 0.001) were risk factors for suicidal ideation. Having not been in love in the past two years (OR=0.601, 95% CI: 0.427-0.841, P=0.003) and so on were protective factors for suicidal ideation. The prediction accuracy and sensitivity of the four models were higher than 0.90, the Kappa values were higher than 0.80, and the positive and negative predictive values were higher than 0.90. Conclusions All four prediction models perform well, among which the suicide ideation prediction model based on support vector machine (SVM) has more advantages. The SVM model can effectively predict the risk of suicidal ideation, which is more helpful for early identification and intervention of high-risk groups.

A Meta-analysis of the association between postpartum depression and exclusive breastfeeding in the Chinese population

LI Xuan, Obore Nathan, TAO Yuchen, HU Yan, WANG Yixiao, HONG Xiang, WANG Bei, YU Hong

2023, 27(11): 1329-1335. doi: 10.16462/j.cnki.zhjbkz.2023.11.014

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Objective To explore the relationship between postpartum depression and exclusive breastfeeding (EBF) in the Chinese population. Methods Observational studies about postpartum depression and feeding patterns were searched in databases including Wanfang, SinoMed, CNKI, and so on. Stata 12.0 was used for meta-analysis. Subgroup analysis for both the control and depression populations was performed according to region, postpartum time, and the groups and populations. Results A total of 16 observational studies were included. The rate of EBF in the postpartum depression group was lower than that in the control group, and the difference was statistically significant (OR=0.44, 95% CI: 0.36-0.54, P < 0.001). Subgroup analysis showed that the depression group had a lower EBF rate than the control group in eastern China (OR=0.45, 95% CI: 0.36-0.56, P < 0.001), as well as in central and western China (OR=0.41, 95% CI: 0.25-0.66, P < 0.001). There was a decreased rate of EBF among mothers that had depression within 42 days postpartum (OR=0.48, 95% CI: 0.37-0.61, P < 0.001) and more significantly between 42 days and 6 months postpartum (OR=0.35, 95% CI: 0.21-0.60, P < 0.001). Exclusion of some studies among the different groups and populations did not significantly alter the overall results and the differences were statistically significant (OR=0.45, 95% CI: 0.36-0.56, P < 0.001). Meta-regression analysis suggested that the source of heterogeneity among studies may not be related to the region (P=0.906), postpartum time(P=0.528), or the groups and populations (P=0.722). Conclusions The rate of EBF is lower in the postpartum depression population compared with the control group. To increase the EBF rate within 6 months and reduce the prevalence of postpartum depression in China, we recommend screening for depression during pregnancy and multidisciplinary collaborative intervention starting from the prenatal period.

Research progress and implications of population medicine and global health

DAI Zhenwei, YANG Yue, FU Jiaqi, CHEN Xu, QU Yimin, WANG Yuping, LI Fujian, HAN Zhili, GUO Yanjun, QIAO Youlin, SU Xiaoyou

2023, 27(11): 1336-1341. doi: 10.16462/j.cnki.zhjbkz.2023.11.015

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Global health is an emerging interdisciplinary field that combines research and practice dedicated to improving human health and achieving equitable health for all, maximizing health gains of various actors. It focuses on solving the problem of inequitable distribution of global health resources and gives priority to indicators of disease burden. However, global-health-polcies-oriented proposals cannot fully address existing problems including health inequalities, waste of resources, separation of medical treatment and prevention, etc. Therefore, we can draw on the solution-oriented concept of population medicine (to make up for the deficiencies) in the field of global health. At present, however, there is no organic integrative study on population medicine and global health in China. This review summarizes and discusses the research progress in population medicine and global health, and its application in the field of primary health care, noncommunicable diseases, infectious diseases, and mental health. Researchers can refer to results of this review to develop research systems for interdisciplinary areas in population medicine and global health.The government should increase its support and investment in population medicine and global health research, establish collaborative research networks between population medicine and global health, and strengthen the exchanges and cooperation between medical schools and institutions, such as schools and institutes of politics, diplomacy, social science, economy and law, so as to deepen the research of population medicine and global health in China.

Relationship between BMI, abdominal obesity and the incidence of type 2 diabetes in Chinese adults: a prospective study

GOU Kaiming, YI Na, ZHAO Zhenping, JIANG Wei, JIANG Yingying, ZHOU Maigeng

2023, 27(11): 1342-1349. doi: 10.16462/j.cnki.zhjbkz.2023.11.016

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Objective To analyze the association of BMI and waist circumference with the risk of developing diabetes mellitus type 2(T2DM) in Chinese population. Methods The 12 085 adult residents who did not develop T2DM and entered the survey cohort for the first time in the China Health and Nutrition Survey in 1993, 1997, 2000, 2004, 2006, 2009, and 2011 were selected as the study subjects, and 1997-2015 was used as the follow-up endpoint. The Cox proportional risk model was used to analyze the risk ratios of T2DM in different combined BMI and waist circumference groups. Subgroup analysis with normal BMI and waist circumference group as the reference group, to analyze the risk of T2DM in normal BMI and abdominal obesity group. Results A total of 12 085 individuals were included in the analysis, with a median follow-up of 9.04 years(128 760 person-years), 775 T2DM events (450 males and 325 females) were observed during the follow-up period. After adjusting for related confounding factors, the risk of T2DM increased 1.26 times (HR=2.26, 95% CI: 1.84-2.78), 1.32 times (HR=2.32, 95% CI: 1.64-3.28), and 2.87 times (HR=3.87, 95% CI: 3.18-4.70) in overweight obese with normal, normal BMI with abdominal obesity, and overweight obese with abdominal obesity, respectively, using normal BMI with normal as the reference. In the subgroup analysis, baseline chronic disease was found to modify the association between normal BMI with abdominal obesity and the risk of T2DM (P_interaction=0.045). Conclusions People with abnormal waist circumference are at a higher risk of developing T2DM compared to BMI and should be the focus of early diabetes prevention programs, especially if they have other chronic diseases at the same time.

A two-sample Mendelian randomized study of causality between lncRNAs and lung adenocarcinoma

SUN Jihong, CONG Jing, WANG Pingyu

2023, 27(11): 1350-1353. doi: 10.16462/j.cnki.zhjbkz.2023.11.017

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Objective A two-sample Mendelian randomization method was used to investigate the causal relationship between long-chain non-coding RNAs(lncRNAs) and the risk of lung adenocarcinoma. Methods The genome-wide association study data of lung adenocarcinoma and the cis-eQTL data set of eQTL Gene Alliance were used. The single nucleotide polymorphisms (SNPs) closely related to lung adenocarcinoma was used as a tool variable. Five two-sample Mendelian randomization models, including the inverse variance weighting method, MR-Egger regression model, weighted median analysis method, simple mode method and weighted mode method, were applied to evaluate the causal effect between lncRNAs and the risk of lung adenocarcinoma. Moreover, the heterogeneity test, gene pleiotropy test and sensitivity analysis were carried out to evaluate the reliability and stability of the results. Results PVT1, LINC00824, Z94721.1 exhibited significant association with lung adenocarcinoma (all P < 0.05). The OR was 0.79 (95% CI: 0.71-0.89) for PVT1 and 0.59 (95% CI: 0.42-0.83) for LINC00824, indicating that both PVT1 and LINC00824 reduced the risk of lung cancer. The OR was 1.09 (95% CI: 1.03-1.16) for Z94721.1, showing that Z94721.1 increased the risk of lung cancer. All passed the heterogeneity test, gene pleiotropy test and sensitivity analysis. Conclusions The three lncRNAs, PVT1, LINC00824 and Z94721.1, have stable causal association with lung adenocarcinoma, providing an important theoretical basis for the study of the pathogenesis of lung adenocarcinoma.

Epidemiology and immune effectiveness evaluation during different immunization strategies for hepatitis B virus in Fujian Province, 2004-2020

ZHANG Dongjuan, LIN Guangcan, HUANG Lifang

2023, 27(11): 1354-1359. doi: 10.16462/j.cnki.zhjbkz.2023.11.018

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Abstract:
Objective To understand the real-world epidemiological characteristics of hepatitis B virus (HBV) infection under different hepatitis B vaccine (HepB) immunization strategies in Fujian Province. Methods HBV infection data were obtained from the Nationally Notifiable Disease Reporting System. The study population was divided into three birth cohort groups based on the national HepB immunization strategy in effect when they were born. Epidemiological characteristics of HepB incidence in different cohorts were analyzed and evaluated. Results In the 2002-2020 birth cohort group, the overall annual incidence of hepatitis B was 145.80/100, 000; the acute hepatitis B (AHB) annual incidence was 35.72/100 000 and the chronic hepatitis B (CHB) annual incidence was 110.08/100 000. Before 1992, the AHB incidence was 46.44/100 000. The annual incidence of CHB increased in all three groups, with the 1992-2001 group having a statistically significant increase (R²=0.68). The incidence rates in coastal areas were relatively high. Conclusions The incidence of hepatitis B is low among young people born under the free Hep B policy from 2002-2020 in Fujian Province. Following up and managing CHB patients to prevent or delay the development of cirrhosis and liver cancer is essential work to achieve the goal of eliminating hepatitis B as a public health problem by 2030.

Application of a dimensionality reduction strategy based on SIS and MDS and machine learning statistical modeling methods to breast cancer transcriptome data

MA Xuan, ZHANG Hualin, LIANG Jiaqi, YANG Kaixin, LIU Long

2023, 27(11): 1360-1364. doi: 10.16462/j.cnki.zhjbkz.2023.11.019

Abstract(188)

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Abstract:
Objective This study aimed to investigate the utility of a two-step dimensionality reduction strategy, incorporating sure independence screening (SIS) and multi-dimensional scaling (MDS), alongside machine learning algorithms, namely support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM), for constructing a statistical model for breast cancer lymph node metastasis risk prediction. This model aims to provide a scientific basis for the identification of high-risk groups and early intervention. Methods SIS and MDS were used as the initial dimensionality reduction method and the last absolute shrinkage and selection operator (LASSO) was used as the second step of dimensionality reduction. The filtered variables were incorporated into three machine learning models, SVM, RF and GBM, respectively, by the two-step dimensionality reduction strategies of SIS+LASSO and MDS+LASSO. The receiver operating characteristic (ROC) area under the curve (AUC) was used as an evaluation metric to measure the prediction performance of the models. Results Among all prediction models, the SIS+LASSO and MDS+LASSO two-step dimensionality reduction strategies have improved prediction stability and reduced running time and running memory relative to the SIS and MDS single-step strategies for the three prediction models SVM, RF, and GBM. The MDS+LASSO two-step dimensionality reduction strategy has improved prediction accuracy relative to the MDS single-step dimensionality reduction strategy. Among all strategies, GBM has higher prediction accuracy than SVM and RF. Conclusions The two-step dimensionality reduction strategy with LASSO added to SIS and MDS compensates for the shortcomings of SIS and MDS single-step dimensionality reduction in terms of computing speed, memory consumption, modeling method selection, and prediction accuracy. For different dimensionality reduction strategies, the prediction performance of GBM is better than that of SVM and RF.

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2023 Vol. 27, No. 11

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Editor

Editor-in-Chief Ye Dongqing M.D.

Assistant editor Fan Yinguang M.D.

Assistant editor Pan Haifeng M.D.

Assistant editor