Long-term effects of climate factors on dengue fever over a 40-year period.

BACKGROUND: Dengue fever stands as one of the most extensively disseminated mosquito-borne infectious diseases worldwide. While numerous studies have investigated its influencing factors, a gap remains in long-term analysis, impeding the identification of temporal patterns, periodicity in transmission, and the development of effective prevention and control strategies. Thus, we aim to analyze the periodicity of dengue fever incidence and explore the association between various climate factors and the disease over an extended time series. METHODS: By utilizing monthly dengue fever cases and climate data spanning four decades (1978-2018) in Guangdong province, China, we employed wavelet analysis to detect dengue fever periodicity and analyze the time-lag relationship with climate factors. Additionally, Geodetector q statistic was employed to quantify the explanatory power of each climate factor and assess interaction effects. RESULTS: Our findings revealed a prolonged transmission period of dengue fever over the 40-year period, transitioning from August to November in the 1970s to nearly year-round in the 2010s. Moreover, we observed lags of 1.5, 3.5, and 3 months between dengue fever and temperature, relative humidity, and precipitation, respectively. The explanatory power of precipitation, temperature, relative humidity, and the Oceanic Niño Index (ONI) on dengue fever was determined to be 18.19%, 12.04%, 11.37%, and 5.17%, respectively. Dengue fever exhibited susceptibility to various climate factors, with notable nonlinear enhancement arising from the interaction of any two variables. Notably, the interaction between precipitation and humidity yielded the most significant effect, accounting for an explanatory power of 75.32%. CONCLUSIONS: Consequently, future prevention and control strategies for dengue fever should take into account these climate changes and formulate corresponding measures accordingly. In regions experiencing the onset of high temperatures, humidity, and precipitation, it is imperative to initiate mosquito prevention and control measures within a specific window period of 1.5 months.

Identification of Fusarium oxysporum Causing Leaf Blight on Dendrobium chrysotoxum in Yunnan Province, China.

Leaf-blight disease caused by the Fusarium oxysporum is an emerging problem in Dendrobium chrysotoxum production in China. Symptoms of leaf blight were observed on seedlings of D. chrysotoxum cultivated in a nursery in Ruili City, Yunnan Province, China. In this study, we isolated the Fusarium sp. associated with leaf-blight disease of D. chrysotoxum from the diseased seedlings. A pathogenicity test was performed to fulfill Koch's postulates to confirm the pathogenicity of isolated strains and identified using morphological and molecular techniques. The results revealed that all four isolated Fusarium sp. isolates (DHRL-01~04) produced typical blight symptoms followed by marginal necrosis of leaves on the D. chrysotoxum plants. On the PDA medium, the fungal colony appeared as a white to purplish color with cottony mycelium growth. Microconidia are oval-shaped, whereas macroconidia are sickle-shaped, tapering at both ends with 2-4 septations. The phylogenetic trees were construed based on internal transcribed spacer (ITS), translation elongation factor (EF-1α), and RNA polymerase subunit genes RPB1 and RPB2 genes, respectively, and blasted against the NCBI database for species confirmation. Based on the NCBI database's blast results, the isolates showed that more than 99% identify with Fusarium oxysporum. To our knowledge, this is the first comprehensive report on the identification of Fusarium oxysporum as the causal agent of Dendrobium chrysotoxum leaf blight in Yunnan Province, China, based on morphological and molecular characteristics.

A Multienzyme Reaction-Mediated Electrochemical Biosensor for Sensitive Detection of Organophosphorus Pesticides.

Ethephon (ETH), a commonly employed growth regulator, poses potential health risks due to its residue in fruits and vegetables, leading to both acute and subchronic toxicity. However, the detection accuracy of ETH is compromised by the color effects of the samples during the detection process. In this work, a multienzyme reaction-mediated electrochemical biosensor (MRMEC) was developed for the sensitive, rapid, and color-interference-resistant determination of ETH. Nanozymes Fe3O4@Au-Pt and graphene nanocomplexes (GN-Au NPs) were prepared as catalysts and signal amplifiers for MRMEC. Acetylcholinesterase (AChE), acetylcholine (ACh), and choline oxidase (CHOx) form a cascade enzyme reaction to produce H2O2 in an electrolytic cell. Fe3O4@Au-Pt has excellent peroxidase-like activity and can catalyze the oxidation of 3,3',5,5'-tetramethvlbenzidine (TMB) in the presence of H2O2, resulting in a decrease in the characteristic peak current of TMB. Based on the inhibitory effect of ETH on AChE, the differential pulse voltammetry (DPV) current signal of TMB was used to detect ETH, offering the limit of detection (LOD) of 2.01 nmol L-1. The MRMEC method effectively analyzed ETH levels in mangoes, showing satisfactory precision (coefficient of variations, 2.88-15.97%) and recovery rate (92.18-110.72%). This biosensor holds promise for detecting various organophosphorus pesticides in food samples.

Associations Between Air Temperature and Daily Varicella Cases - Jinan City, Shandong Province, China, 2019-2021.

What is already known about this topic?: The impact of air temperature on varicella has been studied, but there is limited research exploring its effect on varicella by gender and age group. What is added by this report?: We conducted a time series analysis to examine the differential effects of air temperature on varicella infection across different demographic groups. Our findings indicate that lower temperatures have a more pronounced influence on varicella incidence among males and children compared to females and adults. What are the implications for public health practice?: These findings can assist in identifying populations that are vulnerable to temperature-related varicella and in guiding the implementation of effective measures for varicella control.

The spatial distribution mechanism of PM2.5 and NO2 on the eastern coast of China.

The spatial distribution characteristics of multi-air pollutants and their impacts are difficult to quantify effectively. As PM2.5 and NO2 are the main air pollutants, it is of great significance to explore the spatial causes of their pollution and their interaction mechanism. This study used machine learning (LightGBM) and hot spot analysis to map the spatial distribution of PM2.5 and NO2 in Southwest Fujian (SWFJ) in 2018 and their key pollution areas. Then, the factors and interactive detection of geographical detectors were used to conduct a detailed analysis of the quantitative impact of potential factors such as human activities, terrain, air pollutants, and meteorology on PM2.5 and NO2 pollution. From this we can learn that 1. LightGBM has good stability for drawing the spatial distribution of PM2.5 and NO2. 2. The spatial mechanism of PM2.5 and NO2 can be effectively interpreted from a massive data and macro perspective. 3. A large amount of evidence shows that potential factors such as human activities, topography, air pollutants and meteorology have direct or indirect effects on PM2.5 and NO2 pollution in the SWFJ area. This includes the direct impact of local road traffic emissions on the distribution of PM2.5 and NO2 pollution, the digestion of both by vegetation, the mutual transformation of atmospheric pollutants themselves, and the impact of meteorological conditions. This study not only confirms the effectiveness of machine learning combined with geographical detectors to promote the study of regional air pollution mechanisms, but also confirms the feasibility of exploring the spatial distribution mechanisms of various air pollutants. Therefore, this study is of great significance for explaining the spatial distribution of PM2.5 and NO2, and can also provide reference for policy formulation to reduce regional PM2.5 and NO2 concentrations.

Knowledge diffusion of Geodetector: A perspective of the literature review and Geotree.

Spatial heterogeneity is a fundamental research topic in the field of geography, and Geodetector is a widely used tool for studying this phenomenon. To understand the research advancements and knowledge diffusion trends surrounding Geodetector, we constructed an author evolutionary tree structure fusing its 847 core citations in the Web of Science database and Geotree model for the first time. The results of our literature statistics indicated that Geodetector has garnered the attention of 3123 authors from 48 countries since its publication in the Ecological Indicators journal in 2010, who have published core papers concerning ten important topics. The majority of these studies focused on spatial heterogeneity and its influencing factors. Our analysis of Geotree data revealed a significant correlation between the publication rate of scholars in large teams and their academic activities. Our analysis of the knowledge diffusion chain shown that only 2% of the total number of authors have contributed to over 20% of the scientific collaborations and knowledge diffusion, and they were recognized as experts in Geodetector research. To provide a comprehensive reference for future scholars, we have summarized the citing countries, five classical articles, main scientific domains, and core teams of Geodetector research.

Spatial analysis of overweight prevalence in China: exploring the association with air pollution.

BACKGROUND: Overweight is a known risk factor for various chronic diseases and poses a significant threat to middle-aged and elderly adults. Previous studies have reported a strong association between overweight and air pollution. However, the spatial relationship between the two remains unclear due to the confounding effects of spatial heterogeneity. METHODS: We gathered height and weight data from the 2015 China Health and Retirement Long-term Survey (CHARLS), comprising 16,171 middle-aged and elderly individuals. We also collected regional air pollution data. We then analyzed the spatial pattern of overweight prevalence using Moran's I and Getis-Ord Gi* statistics. To quantify the explanatory power of distinct air pollutants for spatial differences in overweight prevalence across Southern and Northern China, as well as across different age groups, we utilized Geodetector's q-statistic. RESULTS: The average prevalence of overweight among middle-aged and elderly individuals in each city was 67.27% and 57.39%, respectively. In general, the q-statistic in southern China was higher than that in northern China. In the north, the prevalence was significantly higher at 54.86% compared to the prevalence of 38.75% in the south. SO2 exhibited a relatively higher q-statistic in middle-aged individuals in both the north and south, while for the elderly in the south, NO2 was the most crucial factor (q = 0.24, p < 0.01). Moreover, fine particulate matter (PM2.5 and PM10) also demonstrated an important effect on overweight. Furthermore, we found that the pairwise interaction between various risk factors improved the explanatory power of the prevalence of overweight, with different effects for different age groups and regions. In northern China, the strongest interaction was found between NO2 and SO2 (q = 0.55) for middle-aged individuals and PM2.5 and SO2 (q = 0.27) for the elderly. Conversely, in southern China, middle-aged individuals demonstrated the strongest interaction between SO2 and PM10 (q = 0.60), while the elderly showed the highest interaction between NO2 and O3 (q = 0.42). CONCLUSION: Significant spatial heterogeneity was observed in the effects of air pollution on overweight. Specifically, air pollution in southern China was found to have a greater impact on overweight than that in northern China. And, the impact of air pollution on middle-aged individuals was more pronounced than on the elderly, with distinct pollutants demonstrating significant variation in their impact. Moreover, we found that SO2 had a greater impact on overweight prevalence among middle-aged individuals, while NO2 had a greater impact on the elderly. Additionally, we identified significant statistically interactions between O3 and other pollutants.

Exploring the spatial pattern of house collapse rates caused by extreme rainfall in central China: The role of natural and social factors.

The collapse of houses represents a prominent hazard associated with floods, mudslides, and other disastrous events resulting from extreme rainfall. Nevertheless, previous research in this area has been insufficiently dedicated to comprehending the factors that specifically contribute to house collapse triggered by extreme rainfall. This study endeavors to address this knowledge gap by proposing a hypothesis that the occurrence of house collapse, induced by extreme rainfall, demonstrates spatial heterogeneity and is subject to the interactive impacts of various factors. In the study, we investigate the relationship between house collapse rates and natural and social factors in the provinces of Henan, Shanxi, and Shaanxi provinces in 2021. These provinces are representative of flood-prone areas in central China. Spatial scan statistics and GeoDetector model were used to analyze spatial hotspot areas of house collapse rates and determinant power of natural and social factors on the spatial heterogeneity of house collapse rates, respectively. Our analysis reveals that the spatial hotspot areas predominantly concentrated in regions characterized by high rainfall, including areas along riverbanks and low-lying regions. Multiple factors contribute to the variations in house collapse rates. Among these factors, precipitation (q = 0.32) is the most significant, followed by the ratio of brick-concrete houses (q = 0.24), per capita GDP (q = 0.13), elevation (q = 0.13) and other factors. Notably, the interaction of precipitation and slope explains 63 % of the damage pattern, making it the strongest causal factor. The results substantiate our initial hypothesis and underscore the fact that the pattern of damage does not solely rely on a singular factor but rather on the interaction of multiple factors. These findings hold significance in advancing the formulation of more precise strategies aimed at bolstering safety measures and safeguarding properties within regions susceptible to flooding.

Spatiotemporal associations between hand, foot and mouth disease and meteorological factors over multiple climate zones.

Prior studies of hand, foot, and mouth disease (HFMD) have often observed inconsistent results regarding meteorological factors. We propose the hypothesis that these meteorological associations vary in regions because of the heterogeneity of their geographical characteristics. We have tested this hypothesis by applying a geographical detector and Bayesian space-time hierarchy model to measure stratified spatiotemporal heterogeneity and local associations between meteorological factors and HFMD risk in five climate zones in China from January 2016 to December 2017. We found a significant spatial stratified heterogeneity in HFMD risk and climate zone explained 15% of the spatial stratified heterogeneity. Meanwhile, there was a significant temporal stratified heterogeneity of 14% as determined by meteorological factors. Average temperatures and relative humidity had a significant positive effect on HFMD in all climate zones, they were the most obvious in the southern temperate zone. In northern temperate, southern temperate, northern subtropics, middle subtropics and southern subtropics climate zone, a 1 °C rise in temperature was related to an increase of 3.99%, 13.76%, 4.38%, 3.99%, and 7.74% in HFMD, and a 1% increment in relative humidity was associated with a 1.51%, 5.40%, 2.21%, 3.44%, and 4.78% increase, respectively. These findings provide strong support for our hypotheses that HFMD incidence has a significant spatiotemporal stratified heterogeneity and different climate zones have distinct influences on the disease. These findings provide strong support for our hypotheses: HFMD incidence had significant spatiotemporal stratified heterogeneity and different climate zones had distinct influences on it. The study suggested that HFMD prevention and policy should be made according to meteorological variation in each climate zone.

Geographical evolutionary pathway of global tuberculosis incidence trends.

BACKGROUNDS: Tuberculosis (TB) remains a serious public health and human development problem, especially in developing countries. Despite the effectiveness of directly observed therapy, short course programs in reducing transmission and progression of TB, poverty reduction and socioeconomic development remain crucial factors in decreasing TB incidence. However, the geographical pathway on the planet is not yet clear. OBJECTIVES: This study was to reconstruct the geographical evolutionary process of TB in 173 countries and territories from 2010 to 2019 to analyze the socioeconomic determinants that impact the global TB epidemic. In addition, the TB incidence in 2030 was predicted. METHODS: This study analyses TB incidence data from 173 countries and territories between 2010 and 2019. The Geotree model would be used to reconstruct the geographical evolutionary process of TB, which provides a simplified schema for geo-visualizing the trajectories of TB incidence and their socioeconomic drivers. Additionally, to estimate the future TB incidence in 2030, a multilevel model was utilized in conjunction with the hierarchical nature of the Geotree based on a stratified heterogeneity analysis. RESULTS: Global TB incidence was found to be associated with the country type and development stages. Between 2010 and 2019, the average TB incidence rate in 173 countries and territories was -27.48%, with marked spatially stratified heterogeneity by country type and development stage. Low-income and lower-middle-income countries were most vulnerable to TB. Upper-middle-income countries experienced a faster decline in TB incidence than high-income countries, and TB incidence generally decreased as the development stage increased, except for the lower-middle development stage in 2019.The highest average rate of decline in TB incidence was observed in the upper-middle development stage of high-income countries, with a reduction of 45.24%. Meanwhile, 37 high-income countries in the high development stage demonstrated an average rate of change of -13.93%. Socioeconomic determinants, including gross domestic product per capita, urbanization rate, and sociodemographic index, were found to inhibit TB incidence. Based on current trends, the predicted average global TB incidence in 2030 is 91.581 per 100,000 population. CONCLUSIONS: The trajectories of the global TB incidence have been reconstructed to formulate targeted public health responses. To eliminate TB, countries at similar development stage can draw on the experiences of countries at higher development stages that are tailored to their unique characteristics. By learning from successful TB control strategies, countries can take strategic steps toward eradicating TB and improving public health outcomes.

Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006-2020: a Bayesian modeling study of national mortality registries.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in China. No previous study has reported CVD mortality at county-level, and little was known about the nonmedical ecological factors of CVD mortality at such small scale in mainland China. Understanding the spatiotemporal variations of CVD mortality and examining its nonmedical ecological factors would be of great importance to tailor local public health policies. METHODS: By using national mortality registration data in China, this study used hierarchical spatiotemporal Bayesian model to demonstrate spatiotemporal distribution of CVD mortality in 2844 counties during 2006 to 2020 and investigate how nonmedical ecological determinants have affected CVD mortality inequities from the spatial perspectives. RESULTS: During 2006-2020, the age-standardized mortality rate (ASMR) of CVD decreased from 284.77 per 100,000 in 2006 to 241.34 per 100,000 in 2020. Among 2844 counties, 1144 (40.22%) were hot spots counties with a higher CVD mortality risk compared to the national average and located mostly in northeast, north central, and westernmost regions; on the contrary, 1551 (54.53%) were cold spots counties and located mostly in south and southeast coastal counties. CVD mortality risk decreased from 2006 to 2020 was larger in counties where CVD mortality rate had been higher in 2006 in most of the counties, vice versa. Nationwide, nighttime light intensity (NTL) was the major influencing factor of CVD mortality, a higher NTL appeared to be negatively associated with a lower CVD mortality, with one unit increase in NTL, and the CVD mortality risk will decrease 11% (relative risk of NTL was estimated as 0.89 with 95% confidence interval of 0.83-0.94). CONCLUSIONS: Substantial between-county discrepancies of CVD mortality distribution were observed during past 15 years in mainland China. Nonmedical ecological determinants were estimated to significantly explain the overall and local spatiotemporal patterns of this CVD mortality risk. Targeted considerations are needed to integrate primary care with clinical care through intensifying further strategies to narrow unequally distribution of CVD mortality at local scale. The approach to county-level analysis with small area models has the potential to provide novel insights into Chinese disease-specific mortality burden.

Development, validation, and implementation of a robust and quality control-friendly focused peptide mapping method for monitoring oxidation of co-formulated monoclonal antibodies.

Monoclonal antibody (mAb) coformulation containing two therapeutic proteins provides benefits of improved therapeutic efficacy and better patient compliance. Monitoring of the individual mAb stability in the coformulation is critical to ensure its quality and safety. Among post-translational modifications (PTMs), oxidation is often considered as one of the critical quality attributes (CQAs) as it potentially affects the structure and potency. Although hydrophobic interaction chromatography (HIC) and reversed phase liquid chromatography (RPLC) have been used to monitor overall protein oxidation, mass spectrometry of peptide digests resolved by LC methods can afford superior selectivity and sensitivity for specific PTMs. With the advent of the Quadrupole Dalton (QDa) mass spectrometer as an affordable add-on detector, implementation of targeted oxidation assays in development and quality control (QC) laboratories is now feasible. In this study, as the first effort to implement MS-based methods for antibody coformulation in QC laboratories, we developed and validated a high-throughput and robust focused peptide mapping method using QDa for simultaneous site-specific monitoring of oxidation of methionine and tryptophan residues in heavy-chain (HC) complementary determining regions (CDRs) of two co-formulated mAbs. The method was validated in terms of accuracy, precision, linearity, range, quantitation limit (QL), specificity, and solution stability per recommendations in ICH Q2. The method robustness was systematically assessed involving multiple sample preparation and instrument method parameters. The method met the validation criteria in GMP laboratories with excellent robustness and was implemented in both GMP and development environments.

Spatial-temporal heterogeneity and determinants of HIV prevalence in the Mano River Union countries.

BACKGROUND: Utilizing population-based survey data in epidemiological research with a spatial perspective can integrate valuable context into the dynamics of HIV prevalence in West Africa. However, the situation in the Mano River Union (MRU) countries is largely unknown. This research aims to perform an ecological study to determine the HIV prevalence patterns in MRU. METHODS: We analyzed Demographic and Health Survey (DHS) and AIDS Indicator Survey (AIS) data on HIV prevalence in MRU from 2005 to 2020. We examined the country-specific, regional-specific and sex-specific ratios of respondents to profile the spatial-temporal heterogeneity of HIV prevalence and determine HIV hot spots. We employed Geodetector to measure the spatial stratified heterogeneity (SSH) of HIV prevalence for adult women and men. We assessed the comprehensive correct knowledge (CCK) about HIV/AIDS and HIV testing uptake by employing the Least Absolute Shrinkage and Selection Operator (LASSO) regression to predict which combinations of CCKs can scale up the ratio of HIV testing uptake with sex-specific needs. RESULTS: In our analysis, we leveraged data for 158,408 respondents from 11 surveys in the MRU. From 2005-2015, Cote d'Ivoire was the hot spot for HIV prevalence with a Gi_Bin score of 3, Z-Score 8.0-10.1 and P < 0.001. From 2016 to 2020, Guinea and Sierra Leone were hot spots for HIV prevalence with a Gi_Bin score of 2, Z-Score of 3.17 and P < 0.01. The SSH confirmed the significant differences in HIV prevalence at the national level strata, with a higher level for Cote d'Ivoire compared to other countries in both sexes with q-values of 0.61 and 0.40, respectively. Our LASSO model predicted different combinations of CCKs with sex-specific needs to improve HIV testing uptake. CONCLUSIONS: The spatial distribution of HIV prevalence in the MRU is skewed and the CCK about HIV/AIDS and HIV testing uptake are far below the threshold target set by UNAIDS for ending the epidemic in the sub-region. Geodetector detected statistically significant SSH within and between countries in the MRU. Our LASSO model predicted that different emphases should be implemented when popularizing the CCK about HIV/AIDS for adult women and men.

Spatiotemporal variation in the blood lead levels of Chinese children with the environmental Kuznets curve trend.

The environmental Kuznets curve (EKC) is often used to analyze the relationship between environmental pollution health indicators and economic development level in different regions. In developed countries, the blood lead levels (BLLs) of children have been declining fitting the EKC since the 1970s. However, such figures in China have remained at relatively high levels, without any obvious decline, since 2010. We explored spatial variations and graded countermeasures using reported data on BLLs including the lead poisoning rates (LPRs) of children. We found that there were prefectures where either the mean BLLs of the children had reached 100.00 µg/L or the LPRs of more than 40% of the children had reached 100 µg/L. When we reduced the average BLLs to 50 µg/L or lowered the proportion of children with a lead poisoning rate (LPR) above 100 µg/L to 10.00%, the EKC trend decreased, and the linear slope after 2010 became -0.202. If the areas where children's BLLs exceeded 50.00 µg/L or the proportion of children with an LPR above 100 µg/L was more than 10.00% will be controlled, the linear slope of the EKC decrease trend after 2010 will became -0.989, and the national average of children's BLLs would decline by 22.17%. The study concluded that children's BLLs in different regions of China are situated at different EKC stages, and urgent prevention and control strategies must be put in place for undeveloped areas.

A new method for interpolation of missing air quality data at monitor stations.

Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods.

Expansion of non-native plant Flaveria bidentis (L.) Kuntze driven by a range of factors leading to patchy distribution patterns.

Given the growing concern over the ecological impacts of non-native species, exploring these species' expansion edge and distribution patterns and their driving factors is important for developing suitable management measures. Flaveria bidentis (L.) Kuntze, a non-native plant that was introduced to China in the 1990s, has spread from southern Hebei Province, where it first took root, to the surrounding regions and has become one of the most notorious invasive weeds in northern China. Based on 15 years (2006-2021) of extensive field investigations, the spatial distribution of sampling and occurrence points were mapped in the recently expanded region of F. bidentis' population. Then, nearest neighbor analysis is used to characterize the spatial pattern differences between samplings and occurrences. An exponential decay function was used to elucidate the driving factors contributing to the presence and absence of F. bidentis. Our results demonstrated an effective random sampling setup, a heterogeneous spatial distribution of F. bidentis, and a multi-regional independent aggregation distribution pattern (p < .01). There were significant spatial correlations between the aggregation areas of plant occurrence points and the locations of roads and construction sand distribution centers. These findings suggest that human activities involving major roads and construction sand distribution centers were driving factors contributing to this long-distance dispersal and spatially discontinuous distribution patterns. The presence of these patchy distribution patterns has important implications for ongoing efforts to manage populations of non-native species.

Spatiotemporal Analysis of Online Purchase of HIV Self-testing Kits in China, 2015-2017: Longitudinal Observational Study.

BACKGROUND: Since the introduction of HIV self-testing by UNAIDS in 2014, the practice has been extensively implemented around the world. HIV self-testing (HIVST) was developed in China around 2015, and the online purchase of HIVST kits through e-commerce platforms has since become the most important delivery method for self-testing, with advantages such as user-friendliness, speed, and better privacy protection. OBJECTIVE: Understanding the spatiotemporal characteristics of online HIVST kit purchasing behavior and identifying potential impacting factors will help promote the HIV self-testing strategy. METHODS: The online retail data of HIVST kits from the 2 largest e-commerce platforms in China from 2015 to 2017 were collected for this study. The Bayesian spatiotemporal hierarchical model was used to investigate the spatiotemporal characteristics of online purchased HIVST kits. Ordinary least squares regression was used to identify potential factors associated with online purchase, including GDP per capita, population density, road density, HIV screening laboratory density, and newly diagnosed HIV/AIDS cases per 100,000 persons. The q statistics calculated by Geodetector were used to determine the interactive effect of every 2 factors on the online purchase. RESULTS: The online purchase of HIVST kits increased rapidly in China from 2015 to 2017, with annual peak sales in May and December. Five economically superior regions in China, Pearl River Delta, Yangtze River Delta, Chengdu and surrounding areas, Beijing and Tianjin areas, and Shandong Peninsula, showed a comparatively higher spatial preference for online purchased HIVST kits. The GDP per capita (P<.001) and the rate of newly diagnosed HIV/AIDS cases per 100,000 persons (P<.001) were identified as 2 factors positively associated with online purchase. Among the factors we investigated in this study, 2 factors associated with online purchase, GDP per capita and the rate of newly diagnosed HIV/AIDS cases per 100,000 persons, also displayed the strongest interactive effect, with a q value of 0.66. CONCLUSIONS: Individuals in better-off areas are more inclined to purchase HIVST kits online. In addition to economic status, the severity of the HIV epidemic is also a factor influencing the online purchase of HIVST kits.

Unbalanced Risk of Pulmonary Tuberculosis in China at the Subnational Scale: Spatiotemporal Analysis.

BACKGROUND: China has one of the highest tuberculosis (TB) burdens in the world. However, the unbalanced spatial and temporal trends of TB risk at a fine level remain unclear. OBJECTIVE: We aimed to investigate the unbalanced risks of pulmonary tuberculosis (PTB) at different levels and how they evolved from both temporal and spatial aspects using PTB notification data from 2851 counties over a decade in China. METHODS: County-level notified PTB case data were collected from 2009 to 2018 in mainland China. A Bayesian hierarchical model was constructed to analyze the unbalanced spatiotemporal patterns of PTB notification rates during this period at subnational scales. The Gini coefficient was calculated to assess the inequality of the relative risk (RR) of PTB across counties. RESULTS: From 2009 to 2018, the number of notified PTB cases in mainland China decreased from 946,086 to 747,700. The average number of PTB cases in counties was 301 (SD 26) and the overall average notification rate was 60 (SD 6) per 100,000 people. There were obvious regional differences in the RRs for PTB (Gini coefficient 0.32, 95% CI 0.31-0.33). Xinjiang had the highest PTB notification rate, with a multiyear average of 155/100,000 (RR 2.3, 95% CI 1.6-2.8; P<.001), followed by Guizhou (117/100,000; RR 1.8, 95% CI 1.3-1.9; P<.001) and Tibet (108/100,000; RR 1.7, 95% CI 1.3-2.1; P<.001). The RR for PTB showed a steady downward trend. Gansu (local trend [LT] 0.95, 95% CI 0.93-0.96; P<.001) and Shanxi (LT 0.94, 95% CI 0.92-0.96; P<.001) experienced the fastest declines. However, the RRs for PTB in the western region (such as counties in Xinjiang, Guizhou, and Tibet) were significantly higher than those in the eastern and central regions (P<.001), and the decline rate of the RR for PTB was lower than the overall level (P<.001). CONCLUSIONS: PTB risk showed significant regional inequality among counties in China, and western China presented a high plateau of disease burden. Improvements in economic and medical service levels are required to boost PTB case detection and eventually reduce PTB risk in the whole country.

Lagged Effects of Exposure to Air Pollutants on the Risk of Pulmonary Tuberculosis in a Highly Polluted Region.

BACKGROUND: Although significant correlations have been observed between air pollutants and the development of pulmonary tuberculosis (PTB) in many developed countries, data are scarce for developing and highly polluted regions. METHOD: A combined Poisson generalized linear regression-distributed lag nonlinear model was used to determine the associations between long-term exposure (2005-2017) to air pollutants and the risk of PTB in the Beijing-Tianjin-Hebei region. RESULTS: The monthly PTB cases exhibited a fluctuating downward trend. For each 10 µg/m3 increase in concentration, the maximum lag-specific risk and cumulative relative risk (RR) were 1.011 (95% confidence interval (CI): 1.0091.012, lag: 3 months) and 1.042 (1.036-1.048, 5 months) for PM2.5, and 1.023 (1.015-1.031, 0 months) and 1.041 (1.026-1.055, 2 months) for NO2. The risk of PTB was negatively correlated with O3 exposure, and the minimum lag-specific risk and cumulative RR were 0.991 (95% CI: 0.987-0.994, lag: 0 months) and 0.974 (0.968-0.981, 4 months), respectively. No age-dependent effects were observed. CONCLUSIONS: Our results revealed potential associations between outdoor exposure to PM2.5, NO2, and O3 and the risk of PTB. Further research should explore the corresponding interactions and potential mechanisms.