Coverage Rate Research Articles

Pertussis Notification Rate and Tdpa Vaccine/Booster Coverage in Adults: An Opportunity for an Epidemiological Observatory in Primary Care.

Background: In recent years, Europe has experienced a significant increase in pertussis cases. One reason behind this rise is the decline in diphtheria-tetanus-pertussis (dTap) booster coverage among adults. Currently, Italy lacks a reliable monitoring system to track pertussis infections and vaccine coverage among adults. We therefore evaluated the reliability of a primary care framework to respond to this need. Methods: Using an Italian primary care database for individuals aged 15 or above, we determined the pertussis infection notification rate and dTap vaccine/booster coverage for the timeframe of 2009-2022. Results: In the overall population, we obtained a lifetime occurrence rate of pertussis infections of 7.52 per 10,000 individuals. The annual incidence rates of pertussis infections ranged from 0.008 to 0.001 per 10,000 person-years between 2009 and 2022. A rising trend in dTap vaccine coverage rate (ranging from 8.72 to 16.54 vaccines per 10,000 individuals) was observed during the same period. Notably, those aged 65 or older, smokers, and/or individuals with immunodeficiencies were more likely to receive the dTap vaccine compared to the general population. Conclusions: Given the organization of the Italian public health system, this primary care network might act as a reliable epidemiological monitoring system to keep track of pertussis infections and dTap vaccine coverage in adults. Pertussis cases were underreported, and there was a low uptake of vaccines and boosters. Therefore, it is crucial to closely monitor pertussis notifications and dTap administrations and develop intervention strategies at the national level to enhance vaccine-related prevention.

NANDA-I and NOC Linkages for Six Psychosocial Nursing Diagnoses: A Validation Study

The aim of this study was to validate the diagnostic content and the NANDA-I and NOC linkages for six psychosocial nursing diagnoses. This multicentre, descriptive, cross-sectional validation study followed Fehring’s model. Expert nurses in nursing methodology and standardised nursing languages in Spain participated, with expertise criteria based on academic level and clinical, teaching, and research experience in the fields of nursing methodology and standardised nursing languages. This study adhered to the STROBE guidelines for cross-sectional studies. Eighty-seven professionals participated in the diagnostic content validation phase, and 57 in the NANDA-I and NOC linkages validation phase. The content validity indices of the six diagnoses ranged from 0.74 to 0.84, all considered valid. All 84 proposed defining characteristics were valid, with 42 scoring ≥0.8. Thirty-two linkages between diagnoses and NOC outcomes were proposed, all valid, with mean scores between 0.73 and 0.98. Each diagnosis was linked to 5–6 NOC outcomes, comprising 26 main outcomes and 6 supplementary outcomes. Overall coverage rates for each diagnosis ranged from 68.42% to 100%. All linkages between defining characteristics and NOC outcome indicators were validated. The six selected psychosocial diagnoses, their defining characteristics, and the proposed linkages between diagnoses and outcomes have been validated. The validation of linkages between NOC indicators and nearly all major defining characteristics of these six psychosocial diagnoses will make it possible to enhance diagnostic accuracy and enable continuous assessment of the effects of nursing interventions on the clinical progression of these diagnoses.

O27 DEVELOPMENT OF A CONCEPTUAL EVALUATION MODEL AND TESTING ITS PERFORMANCE IN HYPOTHETICAL PAY-FOR-PERFORMANCE INTERVENTIONS TO IMPROVE HYPERTENSION CONTROL

Background and Objective: Hypertension is a growing concern, particularly in low- and middle-income countries. Financial incentives, such as pay-for-performance, have shown promise in improving non-hypertension outcomes. However, their efficacy and cost-effectiveness in hypertension management, especially in low-resource contexts, remain uncertain. Our objectives were to 1) document studies exploring health financial interventions for hypertension management 2) develop a conceptual model which can facilitate future research on health financing approaches for hypertension management, and 3) assess the model's performance in hypothetical pay-for-performance interventions. Methods: Our literature review identified few studies evaluating finance interventions on hypertension management with somewhat more evidence for pay-for-performance strategies. Thus, we developed a conceptual model and assessed its performance in hypothetical scenarios of pay-for-performance schemes to enhance public hypertension care in Bangladesh. Using a state-transition modeling framework, we estimated the incentive costs and impact on cardiovascular disease burden in 18 scenarios with varying intervention coverage, blood pressure effects, and incentive rates (Figure 1). Key model inputs were derived from the 2019 Global Burden of Disease Study, the 2018 WHO STEPS survey, and WHO-CHOICE. Our primary outcome was incentive costs per health-adjusted life year (HALY) gained. Results: Based on our model, the incentive costs per HALY gained depended on the intervention coverage, intervention effect and incentive rate (Figure). If only currently treated patients were included, the most optimistic scenario (i.e., the largest blood pressure reduction with the lowest incentive rate) was estimated to cost 11,043 USD in incentives per HALY gained (Figure). If the intervention also included new, previously untreated patients while maintaining the same total incentives, the estimated incentive costs per HALY gained was 5,515 USD. Conclusions: Our model suggests that pay-for-performance interventions for hypertension care may demand substantial financial resources, but that the intervention could become more favorable with increased treatment coverage. While more studies on the impact of financial interventions in hypertension care are necessary, our model can guide planning and designing such a study.

Invasive Pneumococcal Infections Among Moroccan Children: Pneumococcal Vaccination Challenges in the Mature Vaccine Era.

Streptococcus pneumoniae, a major contributor to global morbidity and mortality, disproportionately affects children, the elderly, and immunocompromised individuals. Despite vaccination efforts, the challenge of serotype replacement highlights the ongoing struggle against invasive pneumococcal diseases (IPD) in Morocco, emphasizing the need for updated public health strategies and vaccine efficacy assessments. This study was conducted at the Ibn Rochd University Hospital Center and the Mohammed VI University Hospital Center from 2019 to 2022, focusing on hospitalized children. It involved the analysis of 74 strains of IPD, assessing the distribution of pneumococcal serotypes and their antibiotic sensitivity in the post-vaccination era. The prevalence of meningitis or meningo-encephalitis was found to be 66% among the study subjects, with the most frequent serotypes being 3, 19A, 6B, 14, and 11. These serotypes varied significantly by age and location. Coverage rates for the pneumococcal conjugate vaccines, PCV-10 and PCV-13, were 20.27% and 56.75%, respectively. Notably, 43% of the strains were non-vaccine serotypes, with serotypes 3 and 19 accounting for 36% of the infections in children, indicating a lack of vaccine efficacy against these types. Additionally, 31.3% of the strains were Penicillin non-susceptible Streptococcus pneumoniae (PNSP), with 81.25% associated with non-vaccine serotypes. This study highlights the persistence of IPD in Moroccan children, revealing significant challenges despite vaccination efforts. With the reintroduction of PCV-13, concerns about the efficacy against non-vaccine serotypes, particularly 3 and 19A, remain. Continuous surveillance and adaptable vaccination strategies are essential to combat these serotype replacements and ensure the effectiveness of future preventive measures.

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival

Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes of performance metrics used to evaluate ML imputation methods: post-imputation bias of regression estimates, post-imputation predictive accuracy, and substantive model-free metrics. As an illustration, we applied data from a real-world breast cancer survival study. This comprehensive approach aimed to provide a thorough assessment of the effectiveness and reliability of ML imputation methods across various analytical perspectives. A simulated dataset with 30% Missing At Random (MAR) values was used. A number of single imputation (SI) methods - specifically KNN, missMDA, CART, missForest, missRanger, missCforest - and multiple imputation (MI) methods - specifically miceCART and miceRF - were evaluated. The performance metrics used were Gower’s distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores. The analysis revealed that in terms of Gower’s distance, CART and missForest were the most accurate, while missMDA and CART excelled for binary covariates; missForest and miceCART were superior for continuous covariates. When assessing bias and accuracy in regression estimates, miceCART and miceRF exhibited the least bias. Overall, the various imputation methods demonstrated greater efficiency than complete-case analysis (CCA), with MICE methods providing optimal confidence interval coverage. In terms of predictive accuracy for Cox models, missMDA and missForest had superior AUC and C-index scores. Despite offering better predictive accuracy, the study found that SI methods introduced more bias into the regression coefficients compared to MI methods. This study underlines the importance of selecting appropriate imputation methods based on study goals and data types in time-to-event research. The varying effectiveness of methods across the different performance metrics studied highlights the value of using advanced machine learning algorithms within a multiple imputation framework to enhance research integrity and the robustness of findings.

Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning.

The aboveground carbon storage (AGC) in forests serves as a crucial metric for evaluating both the composition of the forest ecosystem and the quality of the forest. It also plays a significant role in assessing the quality of regional ecosystems. However, current technical limitations introduce a degree of uncertainty in estimating forest AGC at a regional scale. Despite these challenges, remote sensing technology provides an accurate means of monitoring forest AGC. Furthermore, the implementation of machine learning algorithms can enhance the precision of AGC estimates. Lishui City, with its rich forest resources and an approximate forest coverage rate of 80%, serves as a representative example of the typical subtropical forest distribution in Zhejiang Province. Therefore, this study uses Landsat remote sensing images, employing backpropagation neural network (BPNN), random forest (RF), and categorical boosting (CatBoost) to model the forest AGC of Lishui City, selecting the best model to estimate and analyze its forest AGC spatiotemporal dynamics over the past 30 years (1989-2019). The study shows that: (1) The texture information calculated based on 9×9 and 11×11 windows is an important variable in constructing the remote sensing estimation model of the forest AGC in Lishui City; (2) All three machine learning techniques are capable of estimating forest AGC in Lishui City with high precision. Notably, the CatBoost algorithm outperforms the others in terms of accuracy, achieving a model training accuracy and testing accuracy R2 of 0.95 and 0.83, and RMSE of 2.98 Mg C ha-1 and 4.93 Mg C ha-1, respectively. (3) Spatially, the central and southwestern regions of Lishui City exhibit high levels of forest AGC, whereas the eastern and northeastern regions display comparatively lower levels. Over time, there has been a consistent increase in the total forest AGC in Lishui City over the past three decades, escalating from 1.36×107 Mg C in 1989 to 6.16×107 Mg C in 2019. This study provided a set of effective hyperparameters and model of machine learning suitable for subtropical forests and a reference data for improving carbon sequestration capacity of subtropical forests in Lishui City.

Public mental health services in Southern China and related health outcomes among individuals living with severe mental illness

Background Although national policies in China are comprehensive and instructive, a wide disparity exists between different cities. The current status of public mental health services by region in China remains unclear. This study aimed to investigate policies related to public mental health services, the contact coverage of public mental health services and outcomes of service users.MethodsA cross-sectional study was carried out in Southern China, between April 2021 and March 2022. Considering the geographical location, socioeconomic development levels, and prevalence of severe mental illness, four cities including Wuhan, Changsha, Guangzhou, and Shenzhen were selected. Relevant service providers were asked to report data on mental health policies and facility-related information, including mental health resources, registration rates of patients, management rates of patients, and medication rates of patients. Eligible patients were invited to report service user-related data, including contact coverage of public mental health services and their outcomes. SPSS 26.0 was used for data analysis.ResultsThe four cities in Southern China have made different efforts to develop public mental health services, primarily focusing on socio-economically disadvantaged individuals. Community health centers in Guangzhou and Shenzhen reported having more professional human resources on mental health and higher mental health budgets for patients. The contact coverage rates of most public mental services were higher than 80%. Patients in Changsha (B = 0.3; 95%CI: 0.1–0.5), Guangzhou (B = 0.2; 95%CI: 0.1–0.3), and Shenzhen (B = 0.3; 95%CI: 0.1–0.4) who received social medical assistance services reported higher levels of medication adherence. Patients in Wuhan (B = -6.5; 95%CI: -12.9--0.1), Guangzhou (B = -2.8; 95%CI: -5.5--0.1), and Shenzhen who received community-based rehabilitation services reported lower levels of disability (B = -2.6; 95%CI: -4.6--0.5).ConclusionsThere have been advances in public mental health services in the four Southern cities. The contact coverage rates of most public mental health services were higher than 80%. Patients’ utilization of public mental services was associated with better health outcomes. To improve the quality of public mental health services, the government should try to engage service users, their family members, and supporters in the design, delivery, operationalization, and evaluation of these public mental health services in the future.

Relationship between Urban Forest Fragmentation and Urban Shrinkage in China Differentiated by Moisture and Altitude

Forest fragmentation and urban shrinkage have become the focus of attention in global ecological conservation, with the goal of achieving sustainable development. However, few studies have been concerned with urban forest patterns in shrinking cities. It is necessary to explore whether the loss of the population will mitigate urban forest degradation. Thus, in this study, 195 shrinking cities were identified based on demographic datasets to characterize the spatiotemporal patterns of urban forests in China against a depopulation background. To illustrate the explicit spatial evolution of urban forests in shrinking cities in China, in this study, we reclassified land-use products and determined the annual spatial variations from 2000 to 2022 using area-weighted centroids and landscape pattern indexes. The effects of different climatic and topographical conditions on the spatiotemporal variations in the urban forest patterns against population shrinkage were discussed. The results demonstrated that the forest coverage rate in the shrinking cities of China increased from 40.05 to 40.47% with a generally southwestern orientation, and the most frequent decrease appeared from 2010 to 2015. Except for the temperate humid and sub-humid Northeast China, with plains and hills, all geographical sub-regions of the shrinking cities exhibited growing urban forests. Relatively stable movement direction dynamics and dramatic area changes in climatic sub-regions with large forest coverage were observed. The urban forest centroids of shrinking cities at a lower elevation exhibited more fluctuating changes in direction. The urban forests in the shrinking cities of China were slightly fragmented, and this weakened condition was identified via the decelerating fragmentation. The urban forests of the shrinking cities in the warm-temperate, humid, and sub-humid North China and basin regions exhibited the most pattern variations. Therefore, it is emphasized that the monitoring of policy implementation is essential due to the time lag of national policies in shrinking cities, especially within humid and low-altitude regions. This research concludes that the mitigation of urban deforestation in the shrinking cities of China is greatly varied according to moisture and altitude and sheds light on the effects of the population density from a new perspective, providing support for urban forest management and improvements in the quality of residents’ lives.

Barriers to Childhood Immunization in Rural and Remote Areas: A Qualitative Exploration From the Perspectives of Community Leaders in Sindh, Pakistan.

Despite overall improvements observed at the national level, there is a significant disparity in childhood vaccination coverage rates between urban and rural regions, particularly within tribal and remote areas in Pakistan. Our study aimed to explore the views of community/tribal leaders concerning barriers and their local solutions for improving vaccine uptake, with a specific focus on remote settings. An exploratory qualitative research study was conducted from July to September 2019 in Shikarpur, Sindh, Pakistan. We interviewed 11 community leaders, after developing a semi-structured interview guide based on the health belief model. Following validation, the collected data was transcribed and subsequently translated into the English language. A stepwise process of manual familiarization, coding, theme generation, and theme review using an inductive approach was followed. While most of the rural community leaders expressed a willingness to support vaccinations, numerous unexplored barriers were identified. These barriers included an unsustainable communication system heavily dependent on polio mobile teams, discourteous behavior of healthcare personnel, cultural restrictions that limited women's involvement, economic hardships, limited transportation options, insufficient collaboration with the education sector, poor knowledge among community leaders, and security concerns. Additionally, they proposed some innovative solutions of information dissemination methods through the Ottaque system, ethics-based training, subsidized transportation services or voucher system, and addressing security concerns in collaboration with community leaders. Our findings suggest policymakers take both community leaders and members for an inclusive policy-making process to redraft the special policy for these remote and rural areas.

Health Insurance Enrollment Among US Veterans, 2010-2021

Department of Veterans Affairs (VA) health care spending has increased in the past decade, in part due to legislative changes that expanded access to VA-purchased care. To understand how insurance coverage and enrollment in VA has changed between 2010 and 2021. This cross-sectional study used data from surveys conducted from 2010 to 2021. Participants were respondents across 4 national surveys who reported being a US veteran and reported on health insurance enrollment. Data were analyzed from October 2023 to June 2024. Self-reported health insurance coverage, reliance on VA insurance, and self-reported health. Among a total of 3 644 614 survey respondents (mean [SE] age, 60 [0.04] years; 91.3% [95% CI, 91.2%-91.5%] male) included, 52.2% (95% CI, 52.0%-52.4%) were out of the labor market and 63.1% (95% CI, 62.9%-63.3%) were married. In 2010, 94% of all veterans and 94% of veterans younger than age 65 years reported having health insurance coverage on the American Community Survey. Insurance enrollment increased over time, and by 2020, 97% of all veterans and 95% of veterans younger than 65 years reported having health insurance coverage on the American Community Survey. Insurance enrollment estimates were similar across the surveys. Approximately one-third of veterans reported being enrolled in VA health coverage. Of those who enrolled in VA insurance, more than 75% had more than 1 form of coverage, with Medicare and private insurance being the most common second insurance sources. VA insurance enrollment was negatively associated with income and health status. Veterans without insurance tended to be unemployed and younger. This study of veterans who responded to 4 national surveys found that veterans enrolled in VA health coverage had high rates of dual coverage. Further legislative efforts to increase access without recognizing the high rates of dual coverage may yield unintended consequences, such payer shifting.

Analysis of the Impact of Inactivated A71 Vaccine on the Incidence of Hand-Foot-Mouth Disease in Pudong New Area, Shanghai.

The aim of this study was to investigate the level of inactivated A71 (EV-A71) vaccination in Pudong New Area of Shanghai and its effects on the epidemiology and pathogen spectrum of hand-foot-mouth disease (HFMD) in this area, as well as to provide a basis for improving the prevention and control strategy of HFMD in this area. Data were collected from the "Comprehensive Management Cloud Platform for Immunization Program" system from December 2016 to December 2022. The data on HFMD cases from January 2012 to December 2022 were extracted from the "China Information System for Disease Control and Prevention". A total of 484,056 doses were administered. The vaccination rate of the first dose was 14.03%, and the full vaccination rate was 13.33%. There were significant differences between the first dose and the full vaccination rate in different years (χ2 = 46,538.831, p < 0.001, χ2 = 50,013.946, p < 0.001). A total of 91625 cases of HFMD were reported, including 58 severe cases, and no deaths. Before and after the administration of the inactivated EV-A71 vaccine, there were statistical differences in the distribution of HFMD cases in terms of gender, household registration, occupation, and age (p < 0.001). The etiological surveillance results showed that the rate of enterovirus positivity was 84.15%, with 9.85% being EV-A71, 23.74% CV-A16, and 50.56% non-EV-A71 and non-CV-A16. The coverage rate of the inactivated EV-A71 vaccine in Pudong New Area was not high, and the incidence of HFMD showed a downward trend after the postmarketing of the vaccine. The majority of HFMD infections were non-EV-A71 and non-CV-A16, with CV-A6 accounting for the highest proportion. It is recommended to accelerate the development of combined vaccines to provide more antibody protection.

Short‐term load interval prediction with unilateral adaptive update strategy and simplified biased convex cost function

AbstractThis article proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short‐term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this article, the model training is formulated as a bi‐level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower‐level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full connected Neural Network (FNN) to generate interval boundary. In upper‐level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper‐parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state‐of‐the‐art algorithms, achieving a 15% reduction in prediction error and a 20% decrease in computational time.

Enhancing cotton seedling recognition: a method for high-resolution UAV remote sensing images

ABSTRACT Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring.

Spatial spillover effect and driving factors of urban carbon emissions in the Yellow River Basin using nighttime light data

Yellow River Basin (YRB) is a pivotal region for energy consumption and carbon emissions (CEs) in China, with cities emerging as the main sources of regional CEs. This highlights their critical role in achieving regional sustainable development and China’s carbon neutrality. Consequently, there is a pressing need for a detailed exploration of the urban spillover effects and an in-depth analysis of the complex determinants influencing CEs within the YRB. Remote sensing data provide optimal conditions for conducting extensive studies across large geographical areas and extended time periods. This study integrates DMSP/OLS and NPP/VIIRS nighttime light datasets for a longitudinal analysis of urban CEs in the YRB. Using a harmonized dataset from DMSP/OLS and NPP/VIIRS nighttime light from 2007 to 2021, this study quantifies CEs of 58 prefecture-level cities in the YRB. By combining ESDA, STIRPAT model and spatial econometric model, this investigation further clarifies empirically the spatial spillover effects and driving factors of urban CEs. The analysis delineates a phase-wise augmentation in urban CEs, converging towards a distinct spatial distribution characterized by “lower reach > middle reach > upper reach”. The spatial autocorrelation tests unravel a complex interplay between agglomeration and differentiation patterns within urban CEs, underscored by pronounced spatial lock-in phenomena. Significantly, this study demonstrates that urbanization, economic development, energy consumption structure, green coverage rate, industrial structure, population, technological progress, and FDI each exhibit varied direct and indirect effect on urban CEs. Furthermore, it elaborates on potential policy implications and future research directions, offering crucial insights for formulating CEs mitigation strategies to advance sustainable development.

A particle dynamical system algorithm to find the sparse linear complementary solutions

The Linear Complementarity Problem (LCP) offers a comprehensive modeling framework for addressing a wide range of optimization problems. In many real-world applications, finding an LCP solution with a sparse structure is often necessary. To address this problem, we introduce an innovative global optimization framework named the Particle Dynamical System Algorithm (PDSA), which consists of two components. The first component is a dynamical system (DS) inspired by the Absolute Value Equation (AVE), proven to have equilibria corresponding to LCP solutions, with additional relaxing regulators that enhance coverage rate and stability. The second component is an Adaptive Oscillated Particle Swarm Optimization (AOPSO) designed to globally enhance sparsity in LCP solutions, addressing the complexities posed by non-convex and non-smooth regulation models. Within this framework, the DS achieves optimality, while the AOPSO promotes solution sparsity. We compared our proposed DS with relaxing regulators to two classic efficient DSs, fully validating the effectiveness of our approach and underscoring the significant role of the introduced relaxing regulators in improving the convergence rate. Our newly developed variant of PSO, AOPSO, was compared with three classic and state-of-the-art variants on fourteen benchmark functions, demonstrating its competitive performance. Finally, we performed experiments on seven test examples and an application in portfolio selection, showing that the proposed PDSA algorithm surpasses other competitors in finding sparse LCP solutions.

Role of the dengue vaccine TAK-003 in an outbreak response: Modeling the Sri Lanka experience.

Outbreaks of dengue can overburden hospital systems, drastically reducing capacity for other care. The 2017 dengue serotype 2 (DENV-2) outbreak in Sri Lanka coincided with vaccination in an ongoing phase 3 efficacy trial of a tetravalent dengue vaccine, TAK-003 (NCT02747927). Here, we present data on the efficacy of TAK-003 following two doses of the vaccine administered 3 months apart in participants aged 4-16 years in Sri Lanka. In addition, we have used the 2017 outbreak dynamics to model the potential impact of TAK-003 on virologically confirmed dengue (VCD) cases and hospitalizations during an outbreak situation. Modeling was performed using an age-structured, host-vector, spatial and stochastic transmission model, assuming 65% vaccine coverage and 30 days until initiation of vaccination. Efficacy of TAK-003 against VCD and hospitalized VCD cases was based on data against DENV-2 from the first year of the phase 3 trial. Vaccine efficacy and safety findings in Sri Lanka were in line with those of the overall trial population. The efficacy estimates in Sri Lanka up to the first 12 months after the second dose of TAK-003 were 94.7% and 95.7% against VCD and hospitalized VCD cases, respectively. Modeling of the trial data over an extended geographic area showed a substantial reduction in cases and a flattening of outbreak curves from TAK-003 use. The baseline vaccination scenario (initiation at 30 days, 65% target coverage, vaccine effective at 14 days, 70% hospitalization rate, VE of 95% for VCD and 97% for hospitalized VCD, and 47% for asymptomatic) resulted in a 69.1% reduction in VCD cases and 72.7% reduction in VCD hospitalizations compared with no vaccination. An extreme high scenario (vaccination initiated at Day 15, 80% coverage rate, baseline VE) resulted in 80.3% and 82.3% reduction in VCD and VCD hospitalizations, respectively. Vaccine performance, speed of vaccination campaign initiation, and vaccine coverage were key drivers in reducing VCD cases and hospitalizations. Overall, the study and modeling results indicate that TAK-003 has the potential of meaningful utility in dengue outbreaks in endemic areas.

Lessons Learned From Bhutan on extending Girls-Only HPV Vaccination Program to Boys: A Qualitative Study.

In 2020, Bhutan pioneered a school-based gender-neutral human papillomavirus (HPV) vaccination program, achieving an impressive 96% vaccination coverage rate by 2021. This study, conducted through 49 in-depth interviews with community leaders, policymakers, parents, teachers, and health workers, and 12 focus group discussions with boys who received HPV vaccination. We used conventional content analysis to analyze the data. Enablers of the extension of gender-neutral HPV vaccination included social mobilization and advocacy efforts, which encompassed community engagement and leadership and collaborations with schools. Equally crucial were proficient program management and the strategic use of digital interventions. Challenges included tracking and reaching eligible adolescents. Vaccinated boys perceived school-based vaccination to be a key enabler of vaccine update. The study concludes that extending a girls-only HPV vaccination program to gender-neutral is feasible and acceptable in Bhutan. Findings related to challenges and ways for overcoming them can support other countries interested in gender-neutral HPV vaccination program.

Peer effects in digital inclusive finance participation decisions: Evidence from rural China

Digital inclusive finance (DIF) has emerged as a powerful tool for promoting economic development and eliminating regional imbalances. Many studies have discussed different approaches to expand the coverage rate and increase the participation rate of DIF in developing countries. This study employs a binary choice model with rational heterogeneous expectations utilizing data from CHFS to examine the impact of peer effects on DIF participation choices in rural China. The findings indicate that peers' participation choices in DIF have a positive impact on the individual's participation choices in DIF, primarily through an information transmission mechanism. Moreover, the regional economy effects are explicitly demonstrated since the progress of DIF in more advanced communities may produce a unidirectional spillover effect on less advanced communities. Based on these results, policy implications are recommended to enhance financial education and construct financial wellness programs.

Spatial Allocation Rationality Analysis of Medical Resources Based on Multi-Source Data: Case Study of Taiyuan, China.

Reasonably allocating medical resources can effsectively optimize the utilization efficiency of such resources. This paper took Taiyuan City as an example and established a model to evaluate the rationality of medical resource spatial allocation, incorporating two key dimensions: the spatial layout and the supply and demand of medical resources. In terms of the spatial layout, three indexes were included: Firstly, the service coverage rates of different levels of medical institutions, based on residents' medical orientations, were calculated using network analysis methods. Secondly, the Huff-2SFCA method was improved to calculate the accessibility of medical resources for four different modes of transportation. Then, the Health Resource Agglomeration Degree (HRAD) and Population Agglomeration Degree (PAD) were used to quantify the equity of medical resources. In terms of the supply and demand of medical resources, one index was included: the supply-demand ratio of medical resources during sudden public health events, which was calculated using the number of beds per thousand people as an indicator. These four indexes were weighted using the entropy weight method to obtain the rationality grade of medical resource spatial allocation in Taiyuan City. The study found that the rationality evaluation level of medical resource allocation in the central urban area of Taiyuan City followed a "concentrically decreasing" pattern. The rating ranged from "very reasonable" to "less reasonable", with the area of each level expanding gradually. The areas rated within the top two categories only accounted for 19.92% of the study area, while the area rated as "less reasonable" occupied 38.73% of the total area. These results indicate that the model accounted for residents' travel for various medical orientations and the availability of resources during public health emergencies. It considered both the spatial layout and supply and demand of medical resources, offering recommendations for the precise allocation of urban medical resources.

玉米植保无人机离心喷施系统设计与雾滴分布特性试验

Aiming at the problems of spraying pole-type plant protection machines difficult to get down to the field after row closure of maize in the middle and late stages, uneven droplet distribution of pressure nozzle-type plant protection drone, and difficult to change the droplet particle size, this paper designed a UAV centrifugal spraying system for maize planting protection through the designed centrifugal nozzle combined with a plant protection drone. A single nozzle parameter test was carried out to study the relationship between nozzle speed, flow rate and droplet size. The variable parameter flow rate is set in the range of 300 ml ~ 1000 mL / min, and the nozzle rotation speed is set in the range of 8000 ~ 18000 r / min gradient change. The test results show that the droplet size is related to the liquid supply flow rate and the nozzle rotation speed. According to the theory of optimal biological particle size, the centrifugal nozzle parameter is determined to select the liquid supply flow rate of 1000 mL / min and the nozzle rotation speed of 14000 r / min. The droplet distribution characteristics test under the actual operating conditions was carried out with this parameter, and the important index parameters such as droplet size, droplet density and coverage rate were analyzed to characterize the UAV aerial spraying operation. The experimental results show that the flight speed of the UAV has an important effect on the droplet deposition parameters, which significantly affects the droplet coverage, droplet density and deposition amount of the bottom layer of maize, and the droplet coverage and the droplet deposition amount of each sampling layer tends to decrease with the increase of flight speed, and the coefficient of variation (CV) value of the centrifugal spraying system was the smallest at the flight speed of 1.5m/s, and the effect of droplet deposition was the most uniform. at a flight speed of 1.5m/s. The effect of droplet deposition is the most uniform. This study can provide a reference basis for the optimization of parameters and the correct use of centrifugal plant protection UAV in the middle and late stage plant protection operations of tall crops such as maize.