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Background Neural tube defects (NTDs) are congenital birth defects of the central nervous system that affect 0.5–2 per 1000 pregnancies worldwide. Therefore effective interventions for birth defects, especially NTDs, are very important. Methods Yuanping City is a coal mining city in Shanxi Province, China, with a high incidence of NTDs. This study evaluates the effects of NTD interventions in this city after adjusting for covariates that characterize the native environment. The number of NTD cases and births for the 18 towns in Yuanping City from 2007 to 2014 were included in the study. A shared-component zero-inflated Poisson regression was applied to analyse the temporal–spatial variance among the incidence rates of NTDs in Yuanping City before and after the interventions. Results The results showed that existing interventions to mitigate birth defects, such as folic acid supplementation, reduced the incidence of NTDs by 53.5% in coal mining areas in Yuanping City. However, the NTD risk in areas near coal mines, especially unrestored coal mines, was still high, even after the intervention. Conclusions The government should focus on health hazards related to mining and agricultural production and should provide education and resources to reduce environmental exposure. Reducing environmental risks should be regarded as an early intervention strategy to mitigate birth defects.

BackgroundThe effect of the COVID-19 outbreak has led policymakers around the world to attempt transmission control. However, lockdown and shutdown interventions have caused new social problems and designating policy resumption for infection control when reopening society remains a crucial issue. We investigated the effects of different resumption strategies on COVID-19 transmission using a modeling study setting.MethodsWe employed a susceptible-exposed-infectious-removed model to simulate COVID-19 outbreaks under five reopening strategies based on China’s business resumption progress. The effect of each strategy was evaluated using the peak values of the epidemic curves vis-à-vis confirmed active cases and cumulative cases. Two-sample t-test was performed in order to affirm that the pick values in different scenarios are different.ResultsWe found that a hierarchy-based reopen strategy performed best when current epidemic prevention measures were maintained save for lockdown, reducing the peak number of active cases and cumulative cases by 50 and 44%, respectively. However, the modeled effect of each strategy decreased when the current intervention was lifted somewhat. Additional attention should be given to regions with significant numbers of migrants, as the potential risk of COVID-19 outbreaks amid society reopening is intrinsically high.ConclusionsBusiness resumption strategies have the potential to eliminate COVID-19 outbreaks amid society reopening without special control measures. The proposed resumption strategies focused mainly on decreasing the number of imported exposure cases, guaranteeing medical support for epidemic control, or decreasing active cases.

Recent studies have reported a variety of health consequences of climate change. However, the vulnerability of individuals and cities to climate change remains to be evaluated. We project the excess cause-, age-, region-, and education-specific mortality attributable to future high temperatures in 161 Chinese districts/counties using 28 global climate models (GCMs) under two representative concentration pathways (RCPs). To assess the influence of population ageing on the projection of future heat-related mortality, we further project the age-specific effect estimates under five shared socioeconomic pathways (SSPs). Heat-related excess mortality is projected to increase from 1.9% (95% eCI: 0.2–3.3%) in the 2010s to 2.4% (0.4–4.1%) in the 2030 s and 5.5% (0.5–9.9%) in the 2090 s under RCP8.5, with corresponding relative changes of 0.5% (0.0–1.2%) and 3.6% (−0.5–7.5%). The projected slopes are steeper in southern, eastern, central and northern China. People with cardiorespiratory diseases, females, the elderly and those with low educational attainment could be more affected. Population ageing amplifies future heat-related excess deaths 2.3- to 5.8-fold under different SSPs, particularly for the northeast region. Our findings can help guide public health responses to ameliorate the risk of climate change., Global warming is expected to increase mortality due to heat stress in many regions. Here, the authors asses how mortality due to high temperatures changes in China changes for different demographic groups and show that heat-related excess mortality is increasing under climate change, a process that is strongly amplified by population ageing.

Background As one of its Millennium Development Goals (MDGs), China has achieved a dramatic reduction in the maternal mortality ratio (MMR), although a distinct spatial heterogeneity still persists. Evidence of the quantitative effects of determinants on MMR in China is limited. A better understanding of the spatiotemporal heterogeneity and quantifying determinants of the MMR would support evidence-based policymaking to sustainably reduce the MMR in China and other developing areas worldwide. Methods and findings We used data on MMR collected by the National Maternal and Child Health Surveillance System (NMCHSS) at the county level in China from 2010 to 2013. We employed a Bayesian space–time model to investigate the spatiotemporal trends in the MMR from 2010 to 2013. We used Bayesian multivariable regression and GeoDetector models to address 3 main ecological determinants of the MMR, including per capita income (PCI), the proportion of pregnant women who delivered in hospitals (PPWDH), and the proportion of pregnant women who had at least 5 check-ups (PPWFC). Among the 2,205 counties, there were 925 (42.0%) hotspot counties, located mostly in China’s western and southwestern regions, with a higher MMR, and 764 (34.6%) coldspot counties with a lower MMR than the national level. China’s westernmost regions, including Tibet and western Xinjiang, experienced a weak downward trend over the study period. Nationwide, medical intervention was the major determinant of the change in MMR. The MMR decreased by 1.787 (95% confidence interval [CI]: 1.424–2.142, p < 0.001) per 100,000 live births when PPWDH increased by 1% and decreased by 0.623 (95% CI 0.436–0.798, p < 0.001) per 100,000 live births when PPWFC increased by 1%. The major determinants for the MMR in China’s western and southwestern regions were PCI and PPWFC, while that in China’s eastern and southern coastlands was PCI. The MMR in western and southwestern regions decreased nonsignificantly by 1.111 (95% CI −1.485–3.655, p = 0.20) per 100,000 live births when PCI in these regions increased by 1,000 Chinese Yuan and decreased by 1.686 (95% CI 1.275–2.090, p < 0.001) when PPWFC increased by 1%. Additionally, the western and southwestern regions showed the strongest interactive effects between different factors, in which the corresponding explanatory power of any 2 interacting factors reached up to greater than 80.0% (p < 0.001) for the MMR. Limitations of this study include a relatively short study period and lack of full coverage of eastern coastlands with especially low MMR. Conclusions Although China has accomplished a 75% reduction in the MMR, spatial heterogeneity still exists. In this study, we have identified 925 (hotspot) high-risk counties, mostly located in western and southwestern regions, and among which 332 counties are experiencing a slower pace of decrease than the national downward trend. Nationally, medical intervention is the major determinant. The major determinants for the MMR in western and southwestern regions, which are developing areas, are PCI and PPWFC, while that in China’s developed areas is PCI. The interactive influence of any two of the three factors, PCI, PPWDH, and PPWFC, in western and southwestern regions was up to and in excess of 80% (p < 0.001)., Junming Li and co-workers report on trends in maternal mortality at county level across China., Author summary Why was this study done? Information about the spatiotemporal trends of the maternal mortality ratio is helpful in the policymaking response to reducing the maternal mortality ratio (MMR) in developing areas. The study can help the government to preassess the effects of policy if the corresponding magnitudes of influence of the underlying determinants can be quantified. The quantitative statistical results of national and subnational influencing effects and patterns can help the government to create policies with precision. What did the researchers do and find? We employed a Bayesian space–time model to explore the spatiotemporal trends of the MMR in 2,205 Chinese counties from 2010 to 2013 and used Bayesian multivariable regression and GeoDetector models to address 3 main ecological determinants of MMR. The major determinants of the MMR in China are medical intervention factors. The MMR will decrease by 1.787 (95% CI 1.424–2.142, p < 0.001) and 0.623 (95% CI 0.436–0.798, p < 0.001) per 100,000 live births when the proportion of hospital births and the proportion of 5 or more antenatal care visits increase by 1.0%. The major determinants for the MMR in the western and southwestern regions of China are per capita income and antenatal care, while in the eastern and southern coastal regions, it is per capita income. What do these results mean? Many countries, and particularly developing countries, may learn from China’s dramatic improvement in maternal survival rates. This progress has profited from long-term strategies to enhance delivery care in healthcare facilities and the provision of professional maternity care in large hospitals. There are, however, a variety of policy effects that have occurred in different areas due to regional heterogeneity. We have revealed the dominant factors and their corresponding influencing magnitudes at the national and subnational level, and this evidence may help China or other developing countries to preassess policy effects.

Objective To investigate the relationship between climate variables, East Asian summer monsoon (EASM) and large outbreaks of dengue in China. Methods We constructed ecological niche models (ENMs) to analyse the influence of climate factors on dengue occurrence and predict dengue outbreak areas in China. Furthermore, we formulated a generalised additive model (GAM) to quantify the impact of the EASM on dengue occurrence in mainland China from 1980 to 2016. Results Mean Temperature of Coldest Quarter had a 62.6% contribution to dengue outbreaks. Southern China including Guangdong, Guangxi, Fujian and Yunnan provinces are more vulnerable to dengue emergence and resurgence. In addition, we found population density had a 68.7% contribution to dengue widely distribution in China using ENMs. Statistical analysis indicated a dome-shaped association between EASM and dengue outbreak using GAM, with the greatest impact in the South-East of China. Besides, there was a positive nonlinear association between monthly average temperature and dengue occurrence. Conclusion We demonstrated the influence of climate factors and East Asian summer monsoon on dengue outbreaks, providing a framework for future studies on the association between climate change and vector-borne diseases.

Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10μm in aerodynamic diameter (PM10) on CHDs are inconsistent. We used two machine learning models (i.e., random forest (RF) and gradient boosting (GB)) to investigate the non-linear effects of PM10 exposure during the critical time window, weeks 3-8 in pregnancy, on risk of CHDs. From 2009 through 2012, we carried out a population-based birth cohort study on 39,053 live-born infants in Beijing. RF and GB models were used to calculate odds ratios for CHDs associated with increase in PM10 exposure, adjusting for maternal and perinatal characteristics. Maternal exposure to PM10 was identified as the primary risk factor for CHDs in all machine learning models. We observed a clear non-linear effect of maternal exposure to PM10 on CHDs risk. Compared to 40μgm-3, the following odds ratios resulted: 1) 92μgm-3 [RF: 1.16 (95% CI: 1.06, 1.28); GB: 1.26 (95% CI: 1.17, 1.35)]; 2) 111μgm-3 [RF: 1.04 (95% CI: 0.96, 1.14); GB: 1.04 (95% CI: 0.99, 1.08)]; 3) 124μgm-3 [RF: 1.01 (95% CI: 0.94, 1.10); GB: 0.98 (95% CI: 0.93, 1.02)]; 4) 190μgm-3 [RF: 1.29 (95% CI: 1.14, 1.44); GB: 1.71 (95% CI: 1.04, 2.17)]. Overall, both machine models showed an association between maternal exposure to ambient PM10 and CHDs in Beijing, highlighting the need for non-linear methods to investigate dose-response relationships.

Purpose This study aimed to investigate the spatial distribution pattern of the prevalence of congenital heart disease (CHD) in children in Qinghai-Tibetan Plateau (QTP), a high-altitude region in China. Methods Epidemiological data from a survey on the prevalence of CHD in Qinghai Province including 288,066 children (4–18 years) were used in this study. The prevalence and distribution pattern of CHD was determined by sex, CHD subtype, and nationality and altitude. Spatial pattern analysis using Getis-Ord Gi⁎ was used to identify the spatial distribution of CHD. Bayesian spatial binomial regression was performed to examine the relationship between the prevalence of CHD and environmental risk factors in the QTP. Results The prevalence of CHD showed a significant spatial clustering pattern. The Tibetan autonomous prefecture of Yushu (average altitude > 4000 m) and the Mongolian autonomous county of Henan (average altitude > 3600 m) in Huangnan had the highest prevalence of CHD. Univariate analysis showed that with ascending altitude, the total prevalence of CHD, that in girls and boys with CHD, and that of the subtypes PDA and ASD increasing accordingly. Thus, environmental factors greatly contributed to the prevalence of CHD. Conclusions The prevalence of CHD shows significant spatial clustering pattern in the QTP. The CHD subtype prevalence clustering pattern has statistical regularity which would provide convenient clues of environmental risk factors. Our results may provide support to make strategies of CHD prevention, to reduce the incidence of CHD in high altitude regions of China.

Although many studies examined the effects of fine particulate matter (PM 2.5 ) on the deaths of cardiovascular disease (CVD) and respiratory disease (RD), few research has paid attention to the effects of prolonged continuous exposure to high PM 2.5 pollution. This study estimated the excess risks (ER) of CVD and RD mortalities associated with prolonged continuous exposure to high PM 2.5 pollution for the whole population and specific subsociodemographic groups in Beijing, which is the capital city of China with over 20 million residents and having severe PM 2.5 pollution problems. Our results suggested that when high PM 2.5 pollution occurred continuously, at various thresholds and durations, the adverse effects on CVD and RD mortalities varied significantly. The CVD mortality risks in association with prolonged continuous high PM 2.5 pollution exposure were more serious for single individuals (including unmarried, divorced, and widowed), illiterate and outdoor workers than for other specific subsociodemographic groups. When the daily PM 2.5 concentration higher than 105 μg/m 3 consecutively occurs, at the ninth day, the ERs of CVD death for single individuals, illiterate and outdoor workers groups reached to 45% (95% CI: 22, 71), 51% (95% CI: 28, 79) and 53% (95% CI: 29, 82) respectively. On the other hand, prolonged continuous high PM 2.5 pollution level appeared to contribute a higher proportion of RD deaths among illiterate and outdoor workers, but less significant for the other specific subsociodemographic groups. When the duration with daily PM 2.5 pollution higher than 115 μg/m 3 reached to six days, the ERs for outdoor workers and illiterate attributed to prolonged continuous PM 2.5 pollution exposure increased 36% (95% CI: 5, 76) and 49% (95% CI: 16, 91) respectively.

Background Global warming may increase the frequency of compound hot extremes (CHEs). This study aimed to assess the current mortality burden and future mortality risk attributable to CHEs in China. Methods Daily meteorological, air pollution and mortality data were collected in 364 locations from 2006 to 2017 across China. Hot day/night was identified as a day where daily Tmax/Tmin was higher than its 90th percentile in summer. CHE was defined as a hot night with a following hot day. Mortality risk attributed to CHEs in each location was first evaluated using a distributed lag non-linear model. Location-specific association was pooled using a multivariate meta-analysis model, and attributable fraction in the current time and mortality risk from CHEs under different climate change scenarios (RCP 2.6, RCP 4.5, RCP 8.5) in the future were assessed. Results CHEs (RR: 1.23, 95%CI: 1.19–1.28) were associated with greater mortality risk, and 0.96% mortality was attributable to CHEs. We found that female, the elderly, and people living in north China were more vulnerable to CHEs. In addition, more intensive (RR: 1.07, 95%CI: 1.06–1.08) and consecutive CHEs (RR: 1.09, 95%CI: 1.02–1.17) could increase mortality risk. We further observed a seven to nineteen fold deaths attributable to CHEs in 2090s under middle and high climate change scenarios. Conclusion Our study found that CHEs significantly increased mortality risk and would cause considerable mortality burden in future. These findings suggest that it is necessary to develop clinical and public health policy to alleviate the mortality burden associated with CHEs.

Background Living closer to greenness were thought to benefit various health outcomes. We aimed to assess the association between residential greenness and mortality among patients undergoing multidrug resistant tuberculosis (MDR-TB) treatment. Methods We enrolled all local MDR-TB patients reported in Zhejiang, China from 2009 to 2017 and followed them throughout the treatment. We calculated the contemporaneous normalized difference vegetation index (NDVI) in the 250 and 500 m radius around patient’s residence. Cox proportional hazards regression models with time-varying NDVI were used to assess the impact of greenness exposure on all-cause mortality during MDR-TB treatment, adjusting for potential individual and contextual covariates. Results We ascertained 1,621 active MDR-TB cases, which contributed 3036 person-years at risk with an average follow-up of 684 days (s.d. 149 days) per patient. Among them, there were 163 deaths during follow-up, representing a crude mortality rate of 537 deaths per 10,000 person-years. Patients exposed to the second quintile (Q2) of greenness within the 500 m buffer had around 64% reduced mortality risk over the lowest quintile of greenness with hazard ratio (HR) = 0.364 (95% CI: 0.109–1.22). In lower nighttime light (NTL) areas, the hazard ratios (HR) per quintile increase in NDVI within the 500 m buffer were Q2: 0.35 (95% CI: 0.10–1.18), Q3: 0.24 (95% CI: 0.09–0.66), Q4: 0.26 (95% CI: 0.10–0.69), and Q5: 0.26 (95% CI: 0.10–0.71) relevant to the lowest quintile Q1, with a trend of p-value ≤0.01. Patients who were female, younger ( Conclusion Increasing greenness exposure along with medical treatment reduces all-cause mortality among patients living in lower NTL areas.

Geographically weighted regression (GWR) is an important local method to explore spatial non-stationarity in data relationships. It has been repeatedly used to examine spatially varying relationships between epidemic diseases and predictors. Malaria, a serious parasitic disease around the world, shows spatial clustering in areas at risk. In this article, we used GWR to explore the local determinants of malaria incidences over a 7-year period in northern China, a typical mid-latitude, high-risk malaria area. Normalized difference vegetation index (NDVI), land surface temperature (LST), temperature difference, elevation, water density index (WDI) and gross domestic product (GDP) were selected as predictors. Results showed that both positively and negatively local effects on malaria incidences appeared for all predictors except for WDI and GDP. The GWR model calibrations successfully depicted spatial variations in the effect sizes and levels of parameters, and also showed substantially improvements in terms of goodness of fits in contrast to the corresponding non-spatial ordinary least squares (OLS) model fits. For example, the diagnostic information of the OLS fit for the 7-year average case is R2 = 0.243 and AICc = 837.99, while significant improvement has been made by the GWR calibration with R2 = 0.800 and AICc = 618.54.

Background: China planned to achieve its fifth Millennium Development Goals by 2015, including reducing maternal mortality. The leading cause of maternal mortality in the country is obstetric haemorrhage. However, there is limited evidence on the burden of maternal mortality due to obstetric haemorrhage. It is also unclear which ecological-level risk factor is associated with maternal mortality due to obstetric haemorrhage. Methods: The data on maternal mortality due to obstetric haemorrhage were collected from the China Maternal Mortality Surveys conducted in 1832 counties of mainland China over the 2010-2013 period. The Bayesian spatiotemporal regression and the Geodetector model were used to examine the spatiotemporal distribution of the maternal mortality ratio (MMR) due to obstetric haemorrhage, as well as its association with per capita income, the proportion of pregnant women who received antenatal care at least five times, the hospital delivery rate, and the distance from the nearest blood station (DBS). Findings: The overall MMR due to obstetric haemorrhage decreased in China, but the trends largely varied by region. There were 568 hotspot counties, located mostly in China's western regions. Western China had the highest MMR due to obstetric haemorrhage. The proportion of pregnant women who had five or more maternal checkups and the DBS were the major risk factors for maternal mortality due to obstetric haemorrhage. The MMR increased from 1.92 to 2.51 to 7.06, with an increase in the DBS from 100 km, respectively. Interpretation: The findings provide information for accurate spatial targets and public health policies to reduce maternal deaths at both country and regional levels. Funding Statement: The case study was supported by the MOST (2012CB955503), the NSFC (41271404 and 41421001), the China Medical Board (grant number 11-065), and the United Nations International Children's Emergency Fund (2016EJH011). The data were collected by Sichuan University and processed by the Institute of Geographic Sciences and Natural Resources Research. Declaration of Interests: The authors declare they have no actual or potential competing financial interests.

The rate of neural tube defects (NTDs) in Shanxi Province is the highest world widely. Both human and environmental factors can induce NTDs, but various studies ignored contextual effects. This research examines whether there are significant soil type contextual effects on the rate of NTDs. A spatial two-level regression model is used to quantify the magnitude of contextual effects. Spatial autocorrelated errors structure is used to control autocorrelation of residuals. The results suggest that the spatial multilevel model fit the data better than non-spatial multilevel models. Our findings indicate that there are significant soil type contextual effects on the rate of NTDs, even after taking into account of fertilizer and net income. More attentions should be focused on how characteristics of each soil type may affect the rates of NTDs in further studies, which is a relevant issue for understanding etiology of NTDs.

Background Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China. Methods Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values. Results Average malaria incidence was 0.107 ‰ per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R2 = 0.825) and 17.102 % for test data (R2 = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped. Conclusions The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas.

Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control activities for sustaining elimination and preventing reintroduction of malaria. In China, however, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of climate change on four dominant malaria vectors (An. dirus, An. minimus, An. lesteri and An. sinensis) using species distribution models for two future decades: the 2030 s and the 2050 s. Simulation-based estimates suggest that the environmentally suitable area (ESA) for An. dirus and An. minimus would increase by an average of 49% and 16%, respectively, under all three scenarios for the 2030 s, but decrease by 11% and 16%, respectively in the 2050 s. By contrast, an increase of 36% and 11%, respectively, in ESA of An. lesteri and An. sinensis, was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios. in the 2050 s. In total, we predict a substantial net increase in the population exposed to the four dominant malaria vectors in the decades of the 2030 s and 2050 s, considering land use changes and urbanization simultaneously. Strategies to achieve and sustain malaria elimination in China will need to account for these potential changes in vector distributions and receptivity.

Background After the earthquake on 14, April 2010 at Yushu in China, a plague epidemic hosted by Himalayan marmot (Marmota himalayana) became a major public health concern during the reconstruction period. A rapid assessment of the distribution of Himalayan marmot in the area was urgent. The aims of this study were to analyze the relationship between environmental factors and the distribution of burrow systems of the marmot and to predict the distribution of marmots. Methods Two types of marmot burrows (hibernation and temporary) in Yushu County were investigated from June to September in 2011. The location of every burrow was recorded with a global positioning system receiver. An ecological niche model was used to determine the relationship between the burrow occurrence data and environmental variables, such as land surface temperature (LST) in winter and summer, normalized difference vegetation index (NDVI) in winter and summer, elevation, and soil type. The predictive accuracies of the models were assessed by the area under the curve of the receiving operator curve. Results The models for hibernation and temporary burrows both performed well. The contribution orders of the variables were LST in winter and soil type, NDVI in winter and elevation for the hibernation burrow model, and LST in summer, NDVI in summer, soil type and elevation in the temporary burrow model. There were non-linear relationships between the probability of burrow presence and LST, NDVI and elevation. LST of 14 and 23 °C, NDVI of 0.22 and 0.60, and 4100 m were inflection points. A substantially higher probability of burrow presence was observed in swamp soil and dark felty soil than in other soil types. The potential area for hibernation burrows was 5696 km2 (37.7 % of Yushu County), and the area for temporary burrows was 7711 km2 (51.0 % of Yushu County). Conclusions The results suggested that marmots preferred warm areas with relatively low altitudes and good vegetation conditions in Yushu County. Based on these results, the present research is useful in understanding the niche selection and distribution pattern of marmots in this region.

This study assessed the association between accessibility of catering service venues and adolescents’ alcohol use over the previous 30 days. The data were collected from cross-sectional surveys conducted in 2014, 2223 students at 27 high schools in Chaoyang and Xicheng districts, Beijing using self-administered questionnaires to collect the adolescents information on socio-demographic characteristics and recent alcohol experiences. The accessibility of, and proximity to, catering service venues were summarized by weights, which were calculated by multiplication of the type-weight and the distance-weight. All sampled schools were categorized into three subgroups (low, middle, and high geographic density) based on the tertile of nearby catering service venues, and a multi-level logistic regression analysis was performed to explore variance between the school levels. Considering the setting characteristics, the catering service venues weighted value was found to account for 8.6% of the school level variance of adolescent alcohol use. The odds ratios (OR) and 95% confidence intervals (CI) of drinking over the past 30-days among adolescents with medium and high accessibility of catering service venues were 1.17 (0.86, 1.57) and 1.47 (1.06, 2.02), respectively (p &lt, 0.001 for trend test). This study addressed a gap in the adolescent drinking influence by the catering service venues around schools in China. Results suggest that the greater accessibility of catering service venues around schools is associated with a growing risk of recent drinking.

Both pulmonary tuberculosis (PTB) and intestinal helminth infection (IHI) affect millions of individuals every year in China. However, the national-scale estimation of prevalence predictors and prevalence maps for these diseases, as well as co-endemic relative risk (RR) maps of both diseases’ prevalence are not well developed. There are co-endemic, high prevalence areas of both diseases, whose delimitation is essential for devising effective control strategies. Bayesian geostatistical logistic regression models including socio-economic, climatic, geographical and environmental predictors were fitted separately for active PTB and IHI based on data from the national surveys for PTB and major human parasitic diseases that were completed in 2010 and 2004, respectively. Prevalence maps and co-endemic RR maps were constructed for both diseases by means of Bayesian Kriging model and Bayesian shared component model capable of appraising the fraction of variance of spatial RRs shared by both diseases, and those specific for each one, under an assumption that there are unobserved covariates common to both diseases. Our results indicate that gross domestic product (GDP) per capita had a negative association, while rural regions, the arid and polar zones and elevation had positive association with active PTB prevalence; for the IHI prevalence, GDP per capita and distance to water bodies had a negative association, the equatorial and warm zones and the normalized difference vegetation index had a positive association. Moderate to high prevalence of active PTB and low prevalence of IHI were predicted in western regions, low to moderate prevalence of active PTB and low prevalence of IHI were predicted in north-central regions and the southeast coastal regions, and moderate to high prevalence of active PTB and high prevalence of IHI were predicted in the south-western regions. Thus, co-endemic areas of active PTB and IHI were located in the south-western regions of China, which might be determined by socio-economic factors, such as GDP per capita., Author Summary Pulmonary tuberculosis (PTB) and intestinal helminth infections (IHI) are infectious diseases of poverty, and both diseases affect millions of individuals every year in China. However, a neglected topic for both diseases is their co-endemicity, which mostly occurs in poor areas. This is the first time the co-endemicity of PTB and IHI and their risk factors have been explored by means of a Bayesian geostatistical logistic regression model, a Bayesian Kriging model and a Bayesian shared component model, based on data from the national surveys. Our results indicate that active PTB and IHI prevalence are associated with economic and ecological indices, both individually and collectively, with different disease spectra in different ecosystems. Additionally, we find that the south-western regions of China are the largest clustering areas for prevalence of both diseases, where socio-economic factors, such as GDP per capita may be common risk factors. Both socio-economic factors and epidemiological patterns relevant to control strategies for active PTB and IHI are illustrated clearly in this study, so we have reason to believe that they are essential for devising effective control strategies and should be considered in the control programs for both diseases.

Background Congenital heart disease (CHD) is the most common type of major birth defects in Sichuan, the most populous province in China. The detailed etiology of CHD is unknown but some environmental factors are suspected as the cause of this disease. However, the geographical variations in CHD prevalence would be highly valuable in providing a clue on the role of the environment in CHD etiology. Here, we investigate the spatial patterns and geographic differences in CHD prevalence among 0- to 14-year-old children, discuss the possible environmental risk factors that might be associated with CHD prevalence in Sichuan Basin from 2004 to 2009. Methods The hierarchical Bayesian model was used to estimate CHD prevalence at the township level. Spatial autocorrelation statistics were performed, and a hot-spot analysis with different distance thresholds was used to identify the spatial pattern of CHD prevalence. Distribution and clustering maps were drawn using geographic information system tools. Results CHD prevalence was significantly clustered in Sichuan Basin in different spatial scale. Typical hot/cold clusters were identified, and possible CHD causes were discussed. The association between selected hypothetical environmental factors of maternal exposure and CHD prevalence was evaluated. Conclusions The largest hot-spot clustering phenomena and the CHD prevalence clustering trend among 0- to 14-year-old children in the study area showed a plausibly close similarity with those observed in the Tuojiang River Basin. The high ecological risk of heavy metal(Cd, As, and Pb)sediments in the middle and lower streams of the Tuojiang River watershed and ammonia–nitrogen pollution may have contribution to the high prevalence of CHD in this area.

Background To reveal the spatio-temporal distribution of malaria vectors in the national malaria surveillance sites from 2005 to 2010 and provide reference for the current National Malaria Elimination Programme (NMEP) in China. Methods A 6-year longitudinal surveillance on density of malaria vectors was carried out in the 62 national malaria surveillance sites. The spatial and temporal analyses of the four primary vectors distribution were conducted by the methods of kernel k-means and the cluster distribution of the most widely distribution vector of An.sinensis was identified using the empirical mode decomposition (EMD). Results Totally 4 species of Anopheles mosquitoes including An.sinensis, An.lesteri, An.dirus and An.minimus were captured with significant difference of distribution as well as density. An. sinensis was the most widely distributed, accounting for 96.25% of all collections, and its distribution was divided into three different clusters with a significant increase of density observed in the second cluster which located mostly in the central parts of China. Conclusion This study first described the spatio-temporal distribution of malaria vectors based on the nationwide surveillance during 2005–2010, which served as a baseline for the ongoing national malaria elimination program.