Shenzhen Research Articles

STI or DUI modes of innovation: Evidence from comprehensive evaluation of four cities in China

The Science and Technology Innovation Center holds a pivotal position in the national science and technology innovation system, and a scientific evaluation of the “Sci-tech Innovation Center” will guide its construction direction. This study found the advantages and disadvantages of the four cities through comparison; Hence improvement suggestions were proposed for the weaknesses of the four cities. There are two main paths for the government to drive technology innovation: STI (Science and Technology Innovation) mode and DUI (Doing, Using, Interacting) mode. With the aid of the evaluation index system of the Sci-tech Innovation Center, this article uses fuzzy sets, rough sets and fuzzy dynamic clustering methods to comprehensively evaluate the effects of driving technology innovation in the four cities of Beijing, Shanghai, Shenzhen and Guangzhou. The results found that Shenzhen has a significant effect in DUI, and Beijing has a significant effect in STI. The choice of path is related to the abundance of innovation resources.

Model-inferred timing and infectious period of the chickenpox outbreak source

BackgroundIn May 2024, a chickenpox outbreak was reported at Xiasha Primary School located in Nanshan District, Shenzhen City, China, with a total of 12 cases identified. Despite thorough on-site investigations, the source of infection remained undetected. The purpose of our study was to infer the timing and duration of the infectious period of the initial case using modeling techniques, thereby deducing the identity of the source.MethodsWe conducted an individual contact survey within the class affected by the epidemic and utilized an agent-based model (ABM) to estimate the key parameters related to the timing of the infectious source’s emergence and the duration of its infectiousness. The point estimates derived from the ABM served as prior information for a subsequent Bayesian analysis, which in turn provided the posterior distribution for these parameters.ResultsOur models suggested the infection source entered the classroom around April 24th (95% credible interval: April 22nd to April 26th), with an infectious period of approximately two days. Based on these findings, we should aim to detect students who may have been absent due to atypical chickenpox symptoms during this period and closely examine teachers who were present for two consecutive days for any indication of potential infection.ConclusionThis study demonstrates the efficacy of combining contact surveys with mathematical modeling for outbreak source tracing, offering a novel approach to supplement field epidemiological surveys.Clinical trial numberNot applicable.

Prevalence of and factors associated with overweight and obesity in patients with severe mental disorders in Shenzhen: results from the urban Chinese population.

To determine the prevalence of overweight and obesity in patients with severe mental disorders (SMD) and the factors associated with their socio-demographic and disease characteristics in a cross-sectional population-based study. This analysis examined the prevalence of overweight and obesity in 14 868 managed SMD patients in an urban area of Shenzhen city based on data from the health information monitoring system in 2021. Multivariate logistic regression were used to identify the factors associated with the prevalence of overweight and obesity in patients with SMD. China. 14 868 patients with SMD. The prevalence of overweight and obesity in patients with SMD in this study was 32·6 % and 16·1 %, respectively. In multivariate analysis, married status, Shenzhen household registration, management durations of 5-10 years and >10 years, participation in family physician services, taking clozapine or aripiprazole, FPG > 6·1 mmol/l, hypertension, TC ≥ 5·2 mmol/l, TG ≥ 1·7 mmol/l, and more frequent follow-ups in the past year were associated with higher odds of overweight and obesity. Compared to their respective reference categories, living with parents, spouse and children, taking risperidone, aripiprazole, amisulpride and perphenazine, FPG > 6·1 mmol/l, hypertension, TC ≥ 5·2 mmol/l, TG ≥ 1·7 mmol/l, and more frequent follow-ups in the past year were associated with higher odds of obesity. We observed a high prevalence of overweight and obesity in patients with SMD in this study. The findings highlight the need for integrated management of overweight and obesity risk factors among patients with SMD.

An analytical framework based on social-ecological systems for identifying priority areas for ecological restoration in coastal regions

Owing to rapid urbanization, coastal regions worldwide are experiencing immense resource and environmental stress. Ecological restoration is vital for maintaining and sustainably developing coastal regions. This study proposed a method for delineating priority areas for ecological restoration based on the social-ecological systems (SES) theory. First, an analytical model was established based on three dimensions: ecosystem function, social system pressure, and SES evolution, for analyzing the social and natural systems of coastal regions. Second, based on the modes of land–sea interaction relevant to ecological restoration, auxiliary marine factors were incorporated to aid the delineation of priority areas for ecological restoration. A case study was conducted on the Dapeng New District of Shenzhen City to validate our method. The marine areas of the Dapeng New District have significantly poorer ecosystem function than its terrestrial areas because of the natural geography of this area and human activities. A total of 43.4% of marine areas require artificially assisted ecological restoration, and inland areas 1.5 km from the shoreline are significant for ecological conservation and restoration. Additionally, estuaries and artificial shorelines should be the focus of ecological restoration work in the study area. This case study proved the efficacy of our method. The findings will serve as a reference for the planning and implementation of ecological restoration plans in coastal regions.

Global, regional, and national prevalence of HIV-1 drug resistance in treatment-naive and treatment-experienced children and adolescents: a systematic review and meta-analysis

Despite significant reductions in mother-to-child HIV-1 transmission risks due to the advancements and scale-up of antiretroviral therapy (ART), the global burden of HIV-1 drug resistance (HIVDR) in treatment-naive and treatment-experienced children and adolescents remains poorly understood. In this study, we conducted a systematic review and meta-analysis to estimate the prevalence of HIVDR in these populations globally, regionally, and at the country level. We systematically searched PubMed, Embase, and Web of Science for studies reporting HIVDR in treatment-naive and treatment-experienced children and adolescents from inception to June 28, 2024. Eligible studies reported at least ten successfully genotyped cases. We excluded studies where drug resistance was not reported separately for children and adults or for treatment-naive and treatment-experienced populations. The methodological quality of eligible studies was assessed, and random-effect models were used for meta-analysis to determine the pooled overall and regimen-specific prevalence of one or more HIVDR mutations in these populations globally, regionally, or at the country level. This study is registered with PROSPERO under the number CRD42023424483. Of 2282 records identified, 136 studies (28,539 HIV-1-infected children from 52 countries) were included for analysis. The overall prevalence of HIVDR is 26.31% (95% CI, 20.76-32.25) among treatment-naive children and 74.16% (95% CI, 67.74-80.13) among treatment-experienced children (p<0.0001). HIVDR varied widely across subregion with the highest prevalence in Southern Africa (37.80% [95% CI, 26.24-50.08]) and lowest in South America (11.79% [95% CI, 4.91-20.84]) for treatment-naive children while highest in Asia (80.85% [95% CI, 63.76-93.55]) and lowest in Europe (54.39% [95% CI, 28.61-79.03]) for treatment-experienced children. The proportion of viral failure (VF) presented positive correlation with DR prevalence for treatment-experienced children, which increased from 61.23% (95% CI, 47.98-73.72) in proportion of VF<50%-81.17% (95% CI, 71.57-89.28) in proportion of 100%. Meta-regression analysis for both groups showed that only age (naive: p=0.0005; treated: p<0.0001) was the sources of heterogeneity. Non-nucleoside reverse transcriptase inhibitor (NNRTI) resistances were the most seen mutations among the treatment-naive group, with the HIVDR prevalence more than 10% in Southern Africa, Western and Central Africa, Eastern Africa, Asia, and North America. Both nucleoside reverse transcriptase inhibitor (NRTI) and NNRTI resistances were commonly seen among the treatment-experienced group, varying from 36.33% (95% CI, 11.96-64.93) in North America to 77.54% (95% CI, 62.70-89.58) in South America for NRTI and from 39.98% (95% CI, 13.47-69.97) in Europe to 68.86 (95% CI, 43.91-89.17) in Asia for NNRTI, respectively. This study underscores the significant burden of HIVDR among children and adolescents worldwide, particularly pronounced in sub-Saharan Africa and low-income countries. It emphasizes the critical importance of surveillance in all HIV-1-infected children and advocates for the adoption of dolutegravir (DTG) or other optimal formulations as first-line ART in settings where NNRTI resistance exceeds the WHO's 10% threshold. DTG's high resistance barrier, potent antiviral efficacy, and favorable safety profile makes it a superior choice for managing drug-resistant HIV-1, surpassing traditional antiretroviral therapies. This work was supported by the Science and Technology Innovation Committee of Shenzhen Municipality (No. JCYJ20220531102202005) and the Natural Science Foundation of Guangdong Province (No. 2024A1515012118).

The relationship between BMI and depression: a cross-sectional study.

Mental health problems, especially depressive symptoms, are becoming increasingly prominent, posing a significant risk to public health. Changes in the body mass index (BMI) may impact an individual's mental health, however, the relationship between BMI and depressive symptoms is unclear. The purpose of this study was to investigate the association between BMI and depressive symptoms. Using a multi-stage sampling method, 10,686 adults in Longgang District, Shenzhen City, Guangdong Province, China, were selected for participation in this study. Surveys were distributed in 2020 and 2021 to measure participant demographic data and health. Binary logistic regression, restricted cubic spline regression, and subgroup analyses were performed to explore the relationship between BMI and depressive symptoms. The results showed a U-shaped relationship between BMI and depression. Both obesity and underweight increased the risk of depression among the participants, especially in subgroups of participants who were young, highly educated, single and employed. These findings suggest that adults should try to maintain a normal body weight as a way to prevent depression and maintain their physical and mental health.

Limited Firm Insurance and Aggregate Implications

This paper studies the impacts of financial shocks on firm insurance, firm dynamics, and macroeconomic implications. A key departure from the literature is that firms provide wage insurance contracts to risk-averse workers in a long-term relationship. Such contracts endogenously impose a form of inflexible debt liability to firms, and make firms with limited financial net worth more vulnerable to shocks than others. In our model, firms have heterogenous productivity and financial net worth, and also firms face financial frictions. Search and matching in the labor market endogenously determines the value of wage contract, and thus the level of liability to the firm. We have three new findings: (1) The wage dynamics of the quantitative model are consistent with those in empirical studies, with significant insurance against firm-level idiosyncratic shocks, and also a weak comovement between aggregate wage and GDP when there are aggregate shocks. (2) Firms with limited financial resources are hit by financial shocks significantly more than others, with larger negative impacts on dividends, credit borrowing, firm value, and firm entry; the endogenous wage insurance also becomes more limited, and there are more separations. These impacts on both firms and workers are different from—and cannot be accounted for by—standard models with a representative firm or worker. (3) Quantitative exercises for the Great Recession in the United States suggest that aggregate productivity shocks and financial shocks play different roles; without financial shocks, we cannot account for the dynamics of financial variables, including the reduced credit borrowing, spikes in credit spreads, and large drops in dividend payments. Lastly, we provide microlevel empirical supports for our model implications. This paper was accepted by Lukas Schmid, finance. Funding: Y. Wang acknowledges the financial support from the National Natural Science Foundation of China [Grant 72150003] and the grant from Shenzhen Municipal Government [Grant 1210614103]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.01627 .

A hierarchical deep reinforcement learning method for solving urban route planning problems under large-scale customers and real-time traffic conditions

As urbanization and economic growth advance, large-scale customers and real-time traffic conditions have become crucial factors in urban route planning. Deep reinforcement learning is considered the most effective method for solving urban route planning problems involving large-scale customers and real-time traffic conditions. Due to memory usage limitations, existing deep reinforcement learning methods cannot identify candidate customers or determine optimal travel routes in large-scale and real-time environments. To tackle these problems, this study introduces a hierarchical deep reinforcement learning method utilizing an improved transformer model (HDRLITF) based on the divide-and-conquer concept. Graph attention networks and gate mechanisms are integrated into the transformer model to capture dynamic features and improve the model’s performance. A two-stage training method, based on the actor-critic algorithm, is proposed to determine the optimal policy function. To evaluate the HDRLITF method, experiments were conducted using datasets from the cities of Shenzhen and Chengdu in China. The experimental results suggest that the HDRLITF method can effectively interact with real-time traffic environments and obtain high-quality solutions compared to other deep reinforcement learning methods. The robustness and reliability of HDRLITF were further validated across multiple traffic scenarios and indicators.

Diagnosis of the fragmentation of urban ecological network structure and its social-ecological responses.

The fragmentation of ecological network structures has become a common problem faced by cities. By establishing the urban ecological network under a specific socio-ecological system framework, we aimed to propose a quantitative index to diagnose the fragmentation of the network structure, and to construct detection model to explore the driving factors and mechanism of the network fragmentation. Using Shenzhen City as an example, we used the Floyd-Prim algorithm to generate the skeleton structure of the ecological network and construct a density discontinuity index to diagnose network fragmentation. Combined with the ecological network scenario, social-ecological system framework and a two-layer indicator system were constructed. The detection models were then established to explore the drivers of network disruption and their mode of impact. The models show that the average degree of network fragmentation in Shenzhen was 0.13, and the density of about 85% of corridor discontinuities was greater than 0.01, reflecting the serious state of structural fragmentation. Corridors with more severe structural fragmentation have poorer social-ecological coordination. The fragmentation in Shenzhen was mainly affected by the activities of actors (A) at the microlevel and the resource system (RS) at the macrolevel. The methods and the framework of socio-ecosystem analysis proposed in this paper can reveal the driving factors and influence modes of network fragmentation, providing decision-making reference for ecological restoration practice in urbanized areas.

Exploring collective activity space and its spatial heterogeneity using mobile phone signaling Data: A case of Shenzhen, China

The planning ethos of providing proximity-based services to all inhabitants has been prevailing recently, and underlines the importance of knowing areal differences in collective activity space (AS) of populations. Mobile phone signaling data (MSD) has great potentials for this end, especially in mega-cities with fast changing and spatially varying demographic composition. However, two problems need to be addressed when applying MSD-based AS measurement for planning practices, including the identification of regularly visited locations and the selection of measure indices. This paper proposes a three-step workflow to apply the MSD to measure local collective AS with considering addressing the problems. This three-step workflow aims to illustrate the procedure of using MSD to measure collective AS for supporting planning practice in urban China, with clarifying some key concerns when doing so. We apply the workflow to examine the spatial heterogeneity of the collective AS in Shenzhen City and discuss the transferability of the workflow in different social and institutional contexts.

The renal histopathology of nonproteinuric kidney impairment: a three center experience

Proteinuria is a biomarker of kidney injury that typically results from glomerular and/or tubulointerstitial disease. Whereas kidney impairment with normal urinary protein excretion is usually less focused and understudied. We conducted a retrospective review of the renal histopathology of the patients with variable degrees of unexplained renal insufficiency but with normal range proteinuria between 2014 and 2024 of three university teaching hospitals in Shenzhen city of Southern China. Patients with kidney dysfunction of undetermined or uncertain etiology and with normal urinary protein excretion (defined by a 24hr urinary protein excretion < 150 mg or spot urinary protein to creatinine ratio [PCR] < 150 mg/g) were enrolled and analyzed. In a total of 2405 patients, 53 (2.2%) fulfilled the inclusion criteria (male/female 40/13, age 47.3 ± 14.3 years) with a mean eGFR of 46.6 ± 16.8 ml/min per 1.73 m2. Glomerular disease (GD) was the most frequent pathological finding identified in 23 (43.4%) patients, while 19 (35.8%) cases showed tubulointerstitial disease (TID) and 11 (20.8%) patients exhibited small vascular disease (SVD). Patients in the TID had the lowest mean eGFR and the highest numerical 24hr urinary protein excretion among the three groups. The incidence of acute kidney injury was significantly higher in TID than in other two groups. The patients in the SVD group had the highest fraction of underlying hypertension. Kidney dysfunction with normal range proteinuria may be related with, in descending order of probablity, glomerular, tubulointerstitial and small vascular diseases. Renal biopsies were proved useful in informing therapeutic choice, long-term management and in predicting prognosis in this setting.

Related Factors of Syphilis Positive Rate in Blood Donors During the COVID-19 Epidemic.

In Shenzhen of China, the continuous increase of syphilis infections threatens the safety of blood transfusion. In 2020, COVID-19 was discovered and spread rapidly around the world, and affected the prevalence of syphilis among blood donors. From 2013 to 2020, there were 839,161 blood samples collected in the Shenzhen Blood Center. Blood samples were screened by ELISA tests and confirmed by the TPPA (Treponema pallidum particle agglutination) tests and the TRUST (toluidine red unheated serum tests). All data was analyzed by the chi-square test. From 2013 to 2020, the positive rate of syphilis among blood donors varied significantly among individuals in different ages, educational backgrounds, regions, and blood donation histories (P<0.001). In 2020, It was the first time that there were more repeat blood donors than first-time blood donors and more blood donors with a higher education level than those with a lower education level, and the lowest reactive and positive rate of syphilis among blood donors was observed. Compared to 2019, the prevalence of syphilis among female and repeat blood donors decreased significantly in 2020 (P<0.01). The prevalence of syphilis in blood donors is related to the characteristics of blood donors (in addition to gender) and the COVID-19 epidemic. COVID-19 can affect the prevalence of syphilis among blood donors by influencing the composition of blood donors and the number of syphile-positive donors in certain blood donors, including female and repeat blood donors.

Spatial-spectral attention state-space fusion network for tree carbon estimation using multi-source data

Abstract Amid rapid industrialization and urbanization, global warming and the urban heat island effect are intensifying. There is an urgent need for rapid and accurate monitoring of carbon storage in urban vegetation to assess and mitigate these environmental issues. While methods exist for estimating above-ground carbon (AGC) stocks in individual trees using deep learning, fewer studies integrate multi-source remote sensing data with these techniques. This study introduces a network that leverages spatial-spectral attention and state-space models, called the spatial-spectral attention state-space fusion network (S4FNet). S4FNet utilizes UAV hyperspectral imaging (HSI) data, light detection and ranging (LiDAR) data, and RGB data to estimate AGC storage in individual trees. This study conducted experiments at two sites, the South and North areas of Shenzhen University’s Yuehai Campus, Shenzhen City, Guangdong Province, China, and compared S4FNet with eight traditional machine learning methods and a deep learning method. The experimental results demonstrate that S4FNet achieves the best performance across all metrics, confirming its excellent capability in predicting AGC stocks using multi-source remote sensing data.

Decision-making method for low-carbon transformation technology in existing residential areas based on knowledge graph and scenario analysis methods

ABSTRACT Cities are the main contributors to carbon dioxide emissions, and communities are crucial for implementing urban low-carbon emissions. As a result of the controversies surrounding existing transformation technologies and the varying conditions faced by different targets for transformation, an effective method is required for application in specific target communities to achieve good carbon reduction effects. We propose a method that utilizes the knowledge graph and scenario analysis to systematically screen and evaluate community low-carbon technologies. First, a comprehensive knowledge map spanning multiple fields was constructed, and the correlations were analyzed using CiteSpace. Additionally, we categorized the transformation technologies into nine scenarios to assess their effectiveness and feasibility across different community contexts. Finally, the near-zero carbon emission community pilot area of Shenzhen City was selected as a case study, and the method was applied as a demonstration. The total carbon emissions of the pilot community were reduced by 43% after applying the proposed method. This method is helpful for technical screening at the low-carbon transformation stage and also provides a useful reference for the low-carbon construction stage of communities.

Integrating a multi-variable scenario with Attention-LSTM model to forecast long-term coastal beach erosion

Beach erosion is an adverse impact of climate change and human development activities. Effective beach management necessitates integrating natural and anthropogenic factors to address future erosion trends, while most current prediction models focus only on natural factors, which may provide an incomplete and potentially inaccurate representation of erosion dynamics. This study enhances prediction methods by integrating both natural and anthropogenic factors, thereby enhancing the accuracy and reliability of erosion projections. By extracting historical shorelines through CoastSat model from 1986 to 2020, we develop multivariable scenarios with Attention-LSTM model to predict the regional impacts of natural and anthropogenic factors on erosion to sandy beaches along the typical shoreline of Shenzhen in China. Results reveal that Shenzhen's beaches experienced erosion up to 12 m over the past 35 years. Here we project a decrease in the mean erosion rate of the beaches, identifying population growth (21.0 %) as the main controlling factor before the mid-century in a range of scenarios. We find that Attention-LSTM multi-model ensemble approach can provide overall improved accuracy and reliability over a wide range of beach erosion compared to scenario prediction model of Attention-LSTM and statistical model of Digital Shoreline Analysis System (DSAS), yielding an average uncertainty of 10.99 compared to 13.29. These insights reveal policies to safeguard beaches because of the rising demand for beaches due to human factors, coupled with decreased impervious surfaces through ecological conservation, lead to mitigation for beach erosion. Accurate forecasts empower policymakers to implement effective coastal management strategies, safeguard resources, and mitigate erosion's adverse effects. Our study offers finely-tuned predictions of coastal erosion, providing crucial insights for future coastal conservation efforts and climate change adaptation along the shoreline, and serving as a foundation for further research aimed at understanding the evolving environmental impacts of beach erosion in Shenzhen.

Green commuting within the x-minute city: Towards a systematic evaluation of its feasibility

In cities, carbon emissions associated with commuting transport is large and significant. This study integrates data about the jobs-housing relationship, road network configurations, public transport availability, and real-time traffic conditions during peak hours to evaluate the commuting feasibility and performance of green travel modes (walking, cycling and public transport) and explore the potential determining factors. In the context of the x-minute city, the green travel commuting feasibility (GTCF) indicator measures the percentage of the working population who can commute via green travel modes within specific x-minute thresholds. 15, 30, 45 and 60 min have been considered. In comparison, the car travel commuting feasibility (CTCF) indicator is developed to evaluate the corresponding commuting performance by car. Nanshan district of Shenzhen in China is taken as a case study. Results show that distinct gaps exist between GTCF and CTCF. Public transport performs well only for long-distance (> 8 km) commuting trips, and cycling does well for short (< 3 km) and medium-distance trips. Geographically, areas with large differences of GTCF and CTCF are identified for improving green travel modes with priority. Potential factors influencing GTCF are explored with a regression model and case-based analysis. Smaller street blocks, bus route realignment and better jobs-housing balance should be targeted. Designing cycling short-cuts and public transport routes that avoid traffic jams are also recommended to promote green commuting. The findings demonstrate that real-time trip-planning information is of great value in evaluating the commuting feasibility of multimodal travel and identifying influential factors for achieving the x-minute city.

Clinical phenotype and genetic analysis of a rare case with 6p duplication and terminal deletion syndrome

To explore the genetic basis for a child with developmental delay and intellectual deficit (DD/ID). A child who was admitted to the Maternal and Child Health Care Hospital of Longhua District of Shenzhen City on June 3, 2023 due to DD/ID, craniofacial malformations, and recurrent infections of upper respiratory tract was selected as the study subject. G-banded chromosomal karyotyping was carried out for the child and her parents. Low-depth genome-wide copy number variation sequencing (CNV-seq) and chromosomal microarray analysis (CMA) were used to screen for genome-wide copy number variations (CNV), and fluorescence in situ hybridization (FISH) was used to verify the origin of candidate CNV. The child, an 8-year-old girl, had featured unexplained growth and intellectual development delay, multiple craniofacial malformations, and recurrent infections of the upper respiratory tract. She was found to have a karyotype of 46,XX,der(6)add(6)(q23), while both of her parents were normal. Both CNV-seq and CMA showed that the child has harbored a 21.38 Mb interstitial duplication at 6p25.3p22.3 and a 0.78 Mb terminal deletion at 6p25. FISH verified that both the duplication and deletion had occurred de novo. The abnormal phenotype of the child may be attributed to the 6p duplication and terminal deletion.

Nasopharyngeal carriage of Streptococcus pneumoniae among children aged 30 days to &lt;60 months in Beijing and Shenzhen, China (2018–2021) during pneumococcal conjugate vaccine introduction and the coronavirus disease (COVID-19) pandemic

ObjectivesTo describe the carriage rate, serotype distribution, and antimicrobial susceptibility patterns of Streptococcus pneumoniae (S. pneumoniae) nasopharyngeal (NP) isolates among healthy children aged 30 days to &amp;lt;60 months in the cities of Beijing and Shenzhen during 2018–2021.MethodsA NP swab sample was collected among four annual cohorts of healthy children at routine well-child visits. S. pneumoniae was identified by culture, optochin sensitivity and bile solubility, serotypes determined by latex agglutination and Quellung, and antimicrobial susceptibility testing performed using E-test strips.ResultsS. pneumoniae NP carriage was 13.1% (645/4,911), with the highest S. pneumoniae carriage prevalence (15.3%) observed in 25 to &amp;lt;60 months. The carriage prevalence was 15.1% in children 13–24 months, 13.2% in children 7–12 months, and 8.2% in children 30 days to 6 months (P &amp;lt; 0.01). Living with siblings [20.0% vs. 9.4%: OR: 2.42 (95% CI: 2.05–2.87)] or attending day-care [31.8% vs. 11.3%: OR: 3.67 (95% CI: 2.94–4.57)] increased the risk (P &amp;lt; 0.01). During the period (January 2020–April 2021) of strict non-pharmaceutical interventions to prevent and control the COVID-19 pandemic, the proportion of children with S. pneumoniae colonization declined from 16.0% (94/587) to 5.8% (108/1,848) in Beijing while increasing from 14.5% (64/443) to 18.6% (379/2,033) in Shenzhen. Among S. pneumoniae isolates, 36.7% (237/645) belonged to 13-valent pneumococcal conjugate vaccine (PCV13) serotypes, 64.3% (408/645) were non-PCV13 serotypes, including 20.8% (134/645) non-serotypeable S. pneumoniae (NST). A total of 158/644 isolates (24.5%) were MDR. For the PCV13 isolates, MDR was detected in 36.3% (86/237) of isolates; in comparison, 17.6% (72/407) of non-PCV13 serotypes, including NST, were MDR (P &amp;lt; 0.01). S. pneumoniae NP carriage was detected in 10.7% of children with previous pneumococcal vaccination (PCV7 or PCV13 only) compared with 14.9% in children without previous pneumococcal vaccination.ConclusionsThe highest S. pneumoniae carriage prevalence were found in the oldest age group (25 to &amp;lt;60 months) and in children living with siblings or attending day-care. Vaccination with PCV7 or PCV13 was associated with lower PCV13-serotype colonization. In Beijing, S. pneumoniae carriage significantly declined during the COVID-19 pandemic.

GIS-based assessment of spatial and temporal disparities of urban health index in Shenzhen, China.

To explore the inter-regional health index at the city level to contribute to the reduction of health inequalities. Employed the health determinant model to select indicators for the urban health index of Shenzhen City. Utilized principal component analysis, the weights of these indicators are determined to construct the said health index. Subsequently, the global Moran's index and local Moran's index are utilized to investigate the geographical spatial distribution of the urban health index across various administrative districts within Shenzhen. The level of urban health index in Shenzhen exhibits spatial clustering and demonstrates a positive spatial correlation (2017, Moran's I = 0.237; 2019, Moran's I = 0.226; 2021, Moran's I = 0.217). However, it is noted that this clustering displays a relatively low probability (90% confidence interval). Over the period from 2017 to 2019, this spatial clustering gradually diminishes, suggesting a narrowing of health inequality within economically developed urban areas. Our study reveals the urban health index in a relatively high-income (Shenzhen) in a developing country. Certain spatially correlated areas in Shenzhen present opportunities for the government to address health disparities through regional connectivity.

Factors influencing safe use of drugs among community residents: a cross-sectional study

BackgroundResearchers have paid little attention to the safety of drug use among community residents (CRs). Irrational use of drugs can lead to health risks. We investigated the situation of knowledge–attitude–practices (KAP) of CRs in Shenzhen (China) for safe use of drugs, and analyzed the main factors influencing drug use.MethodsA multi-stage, random sampling method was used. We used a validated questionnaire to conduct an online questionnaire survey on the demographic characteristics and KAP of safe use of drugs of CRs in 10 administrative districts of Shenzhen City. The KAP score of safe use of drugs of CRs was analyzed. Influencing factors were identified using a single-factor chi-squared test and binary logistic regression analysis.ResultsA total of 7269 valid questionnaires were collected. The average scores of knowledge, attitude, and behavior were (9.08 ± 1.49) (possible range: 0–10), (37.82 ± 3.96) (possible range: 8–40), and (35.82 ± 4.56) (possible range: 8–40), respectively, indicating that they had a better grasp of safe use of drugs. Logistic regression analysis showed that sex, age, education level, occupation, monthly household income per capita, marital status, health status, and different sources of information were the main factors affecting the knowledge and behavior of safe use of drugs of CRs. In addition to the marital status variable, other variables also have a significant impact on attitude towards safe use of drugs of CRs.ConclusionsMale sex, lower education level, lower income level, average/poor self-rated health status, and single source of drug-use information were the main factors affecting safe use of drugs based on KAP theory. The government and medical workers should carry out various forms of drug-education activities for people with different needs, encourage CRs to learn safe use of drugs, and promote safe use of drugs by CRs through diverse information sources.