Pt3Sn0.5Mn0.5 Intermetallic Electrocatalyst with Superior Stability for CO-Resilient Methanol Oxidation.

The sluggish kinetics of methanol oxidation reaction (MOR) and poor long-term durability of catalysts are the main restrictions of the large-scale applications of direct methanol fuel cells (DMFCs). Herein, we demonstrated an inspirational ternary Pt3Sn0.5Mn0.5/DMC intermetallic catalyst that reached 4.78 mA cm-2 and 2.39 A mg-1Pt for methanol oxidation, which were 2.50/2.44 and 5.62/5.31 times that of commercial PtRu/C and Pt/C. After the durability test, Pt3Sn0.5Mn0.5/DMC presented a very low current density attenuation (38.5%), which was significantly lower than those for commercial PtRu/C catalyst (84.2%) and Pt/C (93.1%). Density functional theory (DFT) calculations revealed that the coregulation of Sn and Mn altered the surface electronic structure and endowed Pt3Sn0.5Mn0.5 with selective adsorption of Pt for CO and Sn for OH, which optimized the adsorption strength for intermediates and improved the reaction kinetics of MOR. Beyond offering an advanced electrocatalyst, this study provided a new point of view for the rational design of superior methanol oxidation catalysts for DMFC.

Effects of meaning therapy on spirituality, psychological health, and quality of life in patients with cancer: A systematic review and meta-analysis of randomized controlled trials.

Objective: This study aimed to systematically review studies of meaning therapy on patients with cancer and to evaluate its effectiveness on spiritual outcomes, psychological outcomes, and quality of life (QOL). Methods: A comprehensive literature search were performed in five international databases (PubMed, Embase, Web of Science, The Cochrane Library, and CINAHL) and four Chinese databases (CNKI, Wanfang Data, VIP, and CBM) from the inception to August 2023. The methodological quality of each included studies was evaluated by using the revised Cochrane risk-of-bias tool for randomized trials. The random-effects model or fixed-effects model was utilized for effect size analysis, and the standardized mean difference (SMD) or mean difference (MD) along with its corresponding 95% confidence interval (CI) was computed. Meta-analysis was conducted by using the RevMan software 5.4.1. Results: Eight randomized controlled trials with 1251 participants were included in this review. Meta-analyses revealed that meaning therapy can significantly improve the spiritual outcomes including meaning in life (SMD = -0.48; 95% CI = -0.89 to -0.07; P = 0.02), hopelessness (SMD = -0.30; 95% CI = -0.51 to -0.09; P = 0.005), self-esteem (MD = -2.74; 95% CI = -4.17 to -1.32; P = 0.0002) and spiritual well-being (MD = -3.32; 95% CI = -5.63 to -1.01; P = 0.005), psychological outcomes including anxiety (MD = -0.66; 95% CI = -1.30 to -0.01; P = 0.05), depression (SMD = -0.37; 95% CI = -0.55 to -0.20; P < 0.0001), psychological distress (SMD = -0.35; 95% CI = -0.70 to -0.01; P = 0.04) and desire for hastened death (MD = -0.76; 95% CI = -1.47 to -0.05; P = 0.04), and QOL (SMD = -0.29; 95% CI = -0.50 to -0.09; P = 0.006) in patients with cancer. Conclusions: Meaning therapy has positive effects on improving spirituality, psychological health, and QOL of patients with cancer. More high-quality randomized controlled trials with larger sample sizes are warranted to confirm the results of our review and to clarify the long-term effects of meaning therapy in the future. Systematic review registration: PROSPERO (No. CRD42021278286).

Effect of skeletonisation and pedicled bilateral internal mammary artery grafting in coronary artery bypass surgery on post-operative wound infection: A meta-analysis.

The results showed that different internal thoracic artery (ITA) was associated with the rate of postoperative wound infection and the severity of pain following coronary artery bypass grafting (CABG). In order to ascertain if there was any genuine difference in the rate of postoperative infection and severity of the pain, we conducted a meta-analysis to evaluate if there was any actual difference in the wound complication that had been identified with the ITA method. Through EMBASE, Cochrane Library and Pubmed, and so forth, we systematically reviewed the results by August 2023, which compared the impact of skeletonised versus pedicled internal mammary artery (IMA) on wound complications following CABG. The trial data have been pooled and analysed in order to determine if a randomisation or fixed-effect model should be applied. The meta-analysis of data was performed with Revman 5.3 software. The results of this meta-study included 252 related articles from four main databases, and nine articles were chosen to be extracted and analysed. A total of 3320 patients were treated with coronary artery transplantation. Based on current data analysis, we have shown that the rate of postoperative wound infections is reduced by the use of the skeletonised internal mammary artery (SIMA) (OR, 1.84; 95% CI, 1.13, 3.01; p = 0.01). But the results showed that there were no statistically significant differences in the post-operation pain score of the patients (MD, 0.09; 95% CI, -0.58, 0.76; p = 0.79). Furthermore, the duration of the operation was not significantly different between the SIMA and pedicled internal mammary artery (PIMA) (MD, 3.30; 95% CI, -3.13, 9.73; p = 0.31). Overall, the SIMA decreased the rate of postoperative wound infection in CABG patients than the PIMA.

Impact of heat on emergency hospital admission in Texas: geographic and racial/ethnic disparities.

BACKGROUND: Studies exploring the racial/ethnicity disparity of the impact of heat on hospital admission are notably limited, especially in Texas, a state with a diverse population and consistently ranking among the top ten U.S. states for heat-related deaths per capita from 2018 to 2020. OBJECTIVE: Our objective is to determine the correlation between elevated temperatures and emergency hospital admissions for various causes and age groups across 12 Metropolitan Statistical Areas(MSAs) in Texas. Additionally, we aim to investigate health inequalities in the five largest MSAs in Texas between 2004 and 2013. METHODS: We used MSA-level hospital admission and weather data to estimate the relationship between heat and emergency hospital admissions. We applied a Generalized Additive Model and random effects meta-analysis to calculate MSA-specific associations and overall correlation, repeating the analysis for age groups and specific causes of admission. We also investigated health disparities across racial and ethnic groups and performed a sensitivity analysis. RESULTS: The results showed that a 1 °C increase in temperature was associated with a 0.50% (95% CI [0.38%, 0.63%]) increase in all-cause emergency hospital admissions. Heat's impact on hospital admissions varied among age groups and causes, with children under 6 years showing the highest effect estimate (0.64% (95% CI [0.32%,0.96%])). Statistically significant associations were found for Cardiovascular Diseases (0.27% (95% CI [0.07%,0.47%])), Ischemic Heart Diseases (0.53% (95% CI [0.15%,0.92%])), Pneumonia (0.70% (95% CI [0.25%,1.16%])), and Respiratory Diseases (0.67% (95% CI [0.18%,1.17%])). Health disparities were found among racial and ethnic groups in the five largest MSAs. IMPACT STATEMENT: Studies exploring the impact of heat on hospital admission in Texas are notably limited. Our research provided a comprehensive examination of the connection between heat and emergency hospital admissions throughout Texas. Furthermore, we are the first to examine racial/ethnic disparities, identifying African American and Hispanic groups as disproportionately affected. These insights provide valuable insights for policymakers to allocate resources and implement strategies to mitigate the negative consequences of rising temperatures.

The prognostic value of machine learning techniques versus cox regression model for head and neck cancer.

BACKGROUND: Accurate prognostic prediction for head and neck cancer (HNC) is important for the improvement of clinical management. We aimed to compare the prognostic value of various machine learning techniques (MLTs) and statistical Cox regression model for different types of HNC. METHODS: Clinical data of HNC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1974 to 2016. The prediction performance of five ML models, including random forest (RF), gradient boosting decision tree (GBDT), support vector machine (SVM), neural network (NN) and deep learning (DL), were compared with the statistical Cox regression model by estimating the concordance index (C-index), integrated Brier score (IBS), time-dependent receiver operating characteristic (ROC) curve and the area under the curve (AUC). RESULTS: Our results showed that the RF model outperformed all other models in prognostic prediction for all tumor sites of HNC, particularly for major salivary gland cancer (MSGC, C-index: 88.730 ± 0.8700, IBS: 7.680 ± 0.4800), oral cavity cancer (OCC, C-index: 84.250 ± 0.6700, IBS: 11.480 ± 0.3300) and oropharyngeal cancer (OPC, C-index: 82.510 ± 0.5400, IBS: 10.120 ± 0.1400). Meanwhile, we analyzed the importance of each clinical variable in the RF model, in which age and tumor size presented the strongest positive prognostic effects. Additionally, similar results can be observed in the internal (6th edition of the AJCC TNM staging system cohort) and external validations (the TCGA HNC cohort). CONCLUSIONS: The RF model is a promising prognostic prediction tool for HNC patients, regardless of the anatomic subsites.

A multiscale analysis of social and spatial determinants of cancer and noncancer hazards from on-road air pollution in Texas.

Evidence on environmental justice studies linking adverse health effects and on-roadair pollution showing spatial nonstationarity is limited.This study uses cancer and noncancer risk estimates from on-road sources of hazardous air pollutants modeled by the National-Scale Air Toxics Assessment (NATA) at the census tract (N = 5265) level and sociodemographic variables from U.S. Census Bureau to examine the nonstationarity spatial relationship by comparing aspatial and spatial regression modelsglobal ordinary least squares, spatial error model, geographically weighted regression, and multiscale geographically weighted regression. At first glance, census tracts within the highest quartile of cancer and noncancer risks were clustered in the major urban areas. Spatial regression indicates that cancer and non-cancer risk were associated with census tract level percentages of Black, Indigenous, and People of Color (BIPOC). These findings can serve as geospatial guidance for intervening in the processes that drive socio-spatial disparity in air pollution exposure.

Drug resistance prediction and resistance genes identification in Mycobacterium tuberculosis based on a hierarchical attentive neural network utilizing genome-wide variants.

Prediction of antimicrobial resistance based on whole-genome sequencing data has attracted greater attention due to its rapidity and convenience. Numerous machine learning-based studies have used genetic variants to predict drug resistance in Mycobacterium tuberculosis (MTB), assuming that variants are homogeneous, and most of these studies, however, have ignored the essential correlation between variants and corresponding genes when encoding variants, and used a limited number of variants as prediction input. In this study, taking advantage of genome-wide variants for drug-resistance prediction and inspired by natural language processing, we summarize drug resistance prediction into document classification, in which variants are considered as words, mutated genes in an isolate as sentences, and an isolate as a document. We propose a novel hierarchical attentive neural network model (HANN) that helps discover drug resistance-related genes and variants and acquire more interpretable biological results. It captures the interaction among variants in a mutated gene as well as among mutated genes in an isolate. Our results show that for the four first-line drugs of isoniazid (INH), rifampicin (RIF), ethambutol (EMB) and pyrazinamide (PZA), the HANN achieves the optimal area under the ROC curve of 97.90, 99.05, 96.44 and 95.14% and the optimal sensitivity of 94.63, 96.31, 92.56 and 87.05%, respectively. In addition, without any domain knowledge, the model identifies drug resistance-related genes and variants consistent with those confirmed by previous studies, and more importantly, it discovers one more potential drug-resistance-related gene.

Racial/Ethnic Heterogeneity and Rural-Urban Disparity of COVID-19 Case Fatality Ratio in the USA: a Negative Binomial and GIS-Based Analysis.

The 2019 coronavirus disease (COVID-19) has exacerbated inequality in the United States of America (USA). Black, indigenous, and people of color (BIPOC) are disproportionately affected by the pandemic. This study examines determinants of COVID-19 case fatality ratio (CFR) based on publicly sourced data from January 1 to December 18, 2020, and sociodemographic and rural-urban continuum data from the US Census Bureau. Nonspatial negative binomial Poisson regression and geographically weighted Poisson regression were applied to estimate the global and local relationships between the CFR and predictors-rural-urban continuum, political inclination, and race/ethnicity in 2407 rural counties. The mean COVID-19 CFR among rural counties was 1.79 (standard deviation (SD) = 1.07; 95% CI 1.73-1.84) higher than the total US counties (M = 1.69, SD = 1.18; 95% CI: 1.65-1.73). Based on the global NB model, CFR was positively associated with counties classified as "completely rural" (incidence rate ratio (IRR) = 1.24; 95% CI: 1.12-1.39) and "mostly rural" (IRR = 1.26; 95% CI: 1.15-1.38) relative to "mostly urban" counties. Nonspatial regression indicates that COVID-19 CFR increases by a factor of 8.62, 5.87, 2.61, and 1.36 for one unit increase in county-level percent Blacks, Hispanics, American Indians, and Asian/Pacific Islanders, respectively. Local spatial regression shows CFR was significantly higher in rural counties with a higher share of BIPOC in the Northeast and Midwest regions, and political inclination predicted COVID-19 CFR in rural counties in the Midwest region. In conclusion, spatial and racial/ethnic disparities exist for COVID-19 CFR across the US rural counties, and findings from this study have implications for public health.

'Gentrification is not improving my health': a mixed-method investigation of chronic health conditions in rapidly changing urban neighborhoods in Austin, Texas.

Though there are extensive studies on neighborhood effects on health, this relationship remains elusive and requires continuous empirical evidence to support existing findings. Gentrification is a process of neighborhood change that affects most longtime residents. This study examined the health impact of the rapidly changing physical and cultural environment using oral history interviews, electronic interviews, and a quantitative structured survey. The study draws on the social determinants of health framework to explain the self-reported chronic health conditions (SR-CHCs) among 331 residents in Austin, Texas. The study employed non-linear techniques suitable for Poisson distribution to estimate the association between gentrification and SR-CHCs and complemented by direct quotes from in-depth interviews (IDIs). Perceived gentrification score significantly vary by marital status (p < 0.001), educational attainment (p < 0.001), and gender (p < 0.01), while SR-CHCs only significantly varies by educational attainment, p = 0.015). Multivariate results show that gentrification was positively associated with SR-CHCs, after adjusting for socioeconomic variables. Compared to the Hispanics, blacks were 97% more likely to report multiple counts of SR-CHCs (IRR = 1.969, 95% CI 1.074-3.608), and participants with high household income were 8% less likely to report multiple CHCs (IRR = 0.920, 95% CI 0.870-0.973). Drawing from the empirical findings, this study recommends both area-based and individual-level policies to mitigate neighborhood change's impact on residents' health. Finally, this study further adds to the understanding of social determinants of health in understanding chronic health within the changing urban physical and socio-ecology systems. Supplementary Information: The online version contains supplementary material available at 10.1007/s10901-021-09847-8.

Evaluating Consumer Nutrition Environment in Food Deserts and Food Swamps.

This research examines the consumer nutrition environment in the selected neighborhoods identified as food deserts, food swamps, and food oases in Austin, Texas, by considering food availability, food price, food quality, and food labeling. A food auditing instrument M-TxNEA-S (He Jin, San Marcos, TX, USA) was developed to capture the unique dietary culture and food preferences in Texas. A total of 93 food items in 14 grocery stores and supermarkets (GS) and 32 convenience stores (CS) were surveyed. The GS in food swamps and food oases were found to offer significantly more healthy foods than the CS. The availability of healthy food in the GS in the food swamps and food oases is significantly higher than that of the GS from the food deserts; CS in the three neighborhoods did not exhibit a significant difference in healthy food availability. There was no significant difference between the price for the healthy items (lower fat, lower calorie, and whole grain) and that for the regular food options. No significant difference was found for food quality or food labeling between the stores from the different types of neighborhoods. The GS in food deserts are small grocery stores carrying limited ranges of foods. The establishment of larger food stores in the food deserts might not be very rewarding, but opening more small grocery stores with healthier options may alleviate food issues.

Efficacy-specific herbal group detection from traditional Chinese medicine prescriptions via hierarchical attentive neural network model.

BACKGROUND: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs. METHODS: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage. RESULTS: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups. CONCLUSION: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

Housing conditions as predictors of common childhood illness: Evidence from Nigeria Demographic and Health Surveys, 2008-2018.

Several studies have examined the impacts of water, sanitation, hygiene, and general housing conditions on childhood health in developing countries. However, up-to-date knowledge and change pattern in common childhood illness prevalence over time are scarce in Nigeria. To contribute toward meeting the Sustainable Development Goals three and six, we used pooled data (N = 94,053) from the Nigeria Demographic Health Surveys from 2008 to 2018 to examine the trend and determinants of four childhood illnesses: diarrhea, fever, cough, and respiratory infection. Multivariate logistic regression was used to estimate the determinants of the four health outcomes. Our results indicate that between 2008 and 2018, the prevalence of childhood diarrhea, cough, and fever slowly declined. However, there was a drastic decline in childhood-related respiratory illness. Housing conditions, sanitation facilities, and water sources were significantly associated with childhood illness based on the logistic regression analysis. Based on our findings, we recommend general improvement in housing conditions that can eradicate childhood cough, fever, and respiratory infections.

Determinants of Gender-Based Violence and Its Physiological Effects Among Women in 12 African Countries.

Goals 3 and 5 of the United Nations Sustainable Development Goals are to promote good health and well-being and to achieve gender equality, respectively. To successfully move toward these goals in the area of gender equality, there is the need to understand the underlying legislative or laws that protect women and girls from all forms of domestic violence (DV), including gender-based violence (GBV). The cardinal objective of this study, therefore, was to examine the risk factors of GBV and the physiological effects of GBV. To date, few studies have quantified the relationship between laws on DV and the incidence of DV/GBV. This article fills that gap by using Demographic and Health Surveys (DHS) data of 12 African countries. We applied multivariate logistic regression to estimate the association of the absence of laws on DV, men dominant power, history of violence, alcohol consumption, women's attitude toward men's violence perpetration, and decision-making power with the scores of GBV and physiological effects of GBV. Group Kruskal-Wallis Rank test was used to determine the variation of the two outcomes among the 12 countries. Results show significant disparities in the score of GBV, H test (11) = 168,217, p < .001, and score of physiological effects, H test (11) = 122,127, p < .001, among the 12 countries. Specifically, Ghana, Namibia, Rwanda, Mozambique, Zimbabwe, Malawi, Sierra Leone, and Togo reported the highest physiological effect of GBV. Presence of DV laws, male dominance, alcohol consumption, history of abuse, and women empowerment predict GBV and the physiological effect of GBV. Thus, building strong legal frameworks against all forms of DV and empowering women may reduce the incidence of GBV and physiological effects of GBV for all African women.

A retrospective cross-national examination of COVID-19 outbreak in 175 countries: a multiscale geographically weighted regression analysis (January 11-June 28, 2020).

OBJECTIVE: This study retrospectively examined the health and social determinants of the COVID-19 outbreak in 175 countries from a spatial epidemiological approach. METHODS: We used spatial analysis to examine the cross-national determinants of confirmed cases of COVID-19 based on the World Health Organization official COVID-19 data and the World Bank Indicators of Interest to the COVID-19 outbreak. All models controlled for COVID-19 government measures. RESULTS: The percentage of the population age between 15-64 years (Age15-64), percentage smokers (SmokTot.), and out-of-pocket expenditure (OOPExp) significantly explained global variation in the current COVID-19 outbreak in 175 countries. The percentage population age group 15-64 and out of pocket expenditure were positively associated with COVID-19. Conversely, the percentage of the total population who smoke was inversely associated with COVID-19 at the global level. CONCLUSIONS: This study is timely and could serve as a potential geospatial guide to developing public health and epidemiological surveillance programs for the outbreak in multiple countries. Removal of catastrophic medical expenditure, smoking cessation, and observing public health guidelines will not only reduce illness related to COVID-19 but also prevent unecessary deaths.

Fertility knowledge, contraceptive use and unintentional pregnancy in 29 African countries: a cross-sectional study.

OBJECTIVES: We examined the association between incorrect knowledge of ovulation and unintentional pregnancy and child among young women in sub-Saharan Africa countries. METHODS: Using Pearson's Chi-square, t test, multiple logistic regression, and likelihood ratio test, we analyzed Demographic and Health Survey data (2008-2017) of 169,939 young women (15-24 year). RESULTS: The range of prevalence of incorrect knowledge of ovulation was 51% in Comoros and 89.6% in Sao Tome and Principe, while unintentional pregnancy ranged between 9.4% in the Republic of Benin and 59.6% in Namibia. The multivariate result indicates a strong association between incorrect knowledge of ovulation and unintentional pregnancy (OR = 1.17; p < 0.05) and unintentional child (OR = 1.15; p < 0.05). CONCLUSIONS: Adolescent women (15-19) generally have poor knowledge of ovulation and are more likely to report an unintentional pregnancy/child than women between ages 20-24. To reduce the burden of unintentional child/pregnancy in Africa, fertility knowledge should not only be improved on but must consider the sociocultural context of women in different countries that might affect the adoption of such intervention programs. Pragmatic efforts, such as building community support for young women to discuss and share their experiences with professionals and educate them on fertility and sexuality, are essential.

Effects of Tai Chi exercise on cardiovascular disease risk factors and quality of life in adults with essential hypertension: A meta-analysis.

BACKGROUND: Hypertension is the major attributable risk factor for cardiovascular disease. The effect of Tai Chi on essential hypertension (EH) is contentious. OBJECTIVES: In this study, we investigated the effects of Tai Chi on the risk factors for cardiovascular disease and quality of life in adults with EH. METHODS: Using data collected from 15 databases up to December 2018, we meta-analyzed randomized controlled trials of the effect of Tai Chi on EH. RESULTS: Tai Chi exercise was associated with lower systolic blood pressure (SBP) (WMD -12.47, 95%CI -16.00 to -8.94, P < 0.001) and diastolic blood pressure (DBP) (WMD -6.46, 95%CI -8.28 to -4.64, P < 0.001); better quality of life (SMD 0.62, 95% CI 0.35 to 0.90, P < 0.001); lower lipid profiles, including total cholesterol (WMD -0.49, 95% CI -0.62 to -0.37, P < 0.001), triglycerides (WMD -0.49, 95% CI -0.92 to -0.07, P = 0.02), and low-density lipoprotein-cholesterol (LDL-C) (WMD -0.86, 95% CI -1.30 to -0.43, P < 0.001); and lower blood glucose (WMD -0.91, 95% CI -1.59 to -0.23, P = 0.009). Tai Chi had no significant effect on high-density lipoprotein-cholesterol (WMD -0.92, 95% CI -2.21 to -0.37, P = 0.16). CONCLUSIONS: Tai Chi lowers blood pressure, total cholesterol, triglycerides, LDL-C, and blood glucose and significantly increases the quality of life in adults with EH. There is strong evidence for the short-term efficacy of Tai Chi exercises. Larger well-designed RCTs focused on the long-term effect of Tai Chi exercises and patient adherence are needed.

Regional variation and demographic factors associated with knowledge of malaria risk and prevention strategies among pregnant women in Nigeria.

Pregnant women and children are the most vulnerable populations for malaria infection. Yet, knowledge of risk, and preventive measures are poor among this population. Using the 2015 Nigeria Malaria Indicator Survey, we applied logit link function to estimate the associations of wealth status, educational attainment, and region of residence with malaria risk knowledge and prevention strategies (using a treated mosquito net and malaria drugs) among 739 Nigerian pregnant women aged 15-49 years. Urban women who had obtained a secondary school education (Adjusted odds ratio [aOR] = 2.12; 95% confidence interval [CI] 1.09-4) or higher (aOR = 8.31; 95% CI 3.2-22) had more knowledge of malaria risk. Urban women in the South-West (aOR = 5.02; [CI] 2.02-12.50) and South-East (aOR = 2.68; 95% CI 1.19-6.06) were more likely to use treated mosquito nets during pregnancy. Women in the urban South-West (aOR = 4.04; 95% CI 1.5-11) were more likely to use malaria drugs during pregnancy than those in the North-Central. A wide regional disparity in the knowledge of malaria risks and use of preventive measures exists. Thus, promoting equal access to malaria preventive measures as well as improving knowledge about malaria transmission by mosquitoes should be considered as essential components of ongoing malaria control and elimination efforts in Nigeria.

Real-Time Forecasting of Hand-Foot-and-Mouth Disease Outbreaks using the Integrating Compartment Model and Assimilation Filtering.

Hand-foot-and-mouth disease (HFMD) is a highly contagious viral infection, and real-time predicting of HFMD outbreaks will facilitate the timely implementation of appropriate control measures. By integrating a susceptible-exposed-infectious-recovered (SEIR) model and an ensemble Kalman filter (EnKF) assimilation method, we developed an integrated compartment model and assimilation filtering forecast model for real-time forecasting of HFMD. When applied to HFMD outbreak data collected for 2008-11 in Beijing, China, our model successfully predicted the peak week of an outbreak three weeks before the actual arrival of the peak, with a predicted maximum infection rate of 85% or greater than the observed rate. Moreover, dominant virus types enterovirus 71 (EV-71) and coxsackievirus A16 (CV-A16) may account for the different patterns of HFMD transmission and recovery observed. The results of this study can be used to inform agencies responsible for public health management of tailored strategies for disease control efforts during HFMD outbreak seasons.

Climate change and dengue fever transmission in China: Evidences and challenges.

Dengue Fever (DF) has become one of the most serious infectious diseases in China. Dengue virus and its vector (Aedes mosquito) are known to be sensitive to climate condition. Climate impacts DF through affecting three essential bioecological aspects: DF virus, vector (mosquito) and DF transmission environment. Weather-based DF model, mosquito model and climate model are the three pillars to help the prediction of DF distribution. Through a systematic review of literature between 1980 and 2017, this paper summarizes empirical evidences in China on the impact of climate change on DF; it further reviews the related DF incidence models and their findings on how changes in weather factors may impact DF occurrences in China. Compared with some well-known research projects in the western countries, there is a lack of knowledge in China regarding how the spatiotemporal distribution of DF will respond to climate change. However, being able to predict DF distribution is key to China's efforts to prevent and control DF transmission. We conclude this paper by recommending four focused areas for China: promoting more advanced research on the relationship between extreme weather events and DF, developing regional-specific models for the high risk regions of DF in south China, encouraging interdisciplinary collaboration between climate studies and health services, and enhancing public health education and management at national, regional and local levels.