Coverage Of Monitoring Research Articles

Assessing progress on the coverage of interventions in the first 1000 days in India: role of national programs

BackgroundHigh coverage of nutrition-specific interventions is critical to meet global nutrition targets, and it is imperative to understand how to attain it. We examined trends and inequalities in the coverage of interventions in India and the role of the National Nutrition Mission (NNM) in achieving improvements.MethodsWe conducted trends and equity analysis of 30 interventions using two rounds of National Family Health Survey data (2015–2016, n=1 78 874, and 2019–2021, n=1 70 697). We also compared coverage between states that received incentives and monitoring under NNM and those that did not. We reviewed programme documents and grey literature to construct a policy timeline to trace pathways to coverage improvement and consulted with stakeholders to confirm interpretation of findings.FindingsBetween 2016 and 2021, coverage improved significantly for nearly all interventions (~1–22 percentage points (pp) during pregnancy, ~7–20 pp during delivery/postpartum and~5–17 pp during early childhood). Improvements in coverage were higher among the poor and in rural areas compared with the rich and in urban areas, respectively; wealth and residence gaps narrowed for most interventions. These improvements could be traced to community mobilisation, technology and monitoring under NNM. Improvements in coverage of growth monitoring and counselling were higher in states that received additional incentives and monitoring under NNM. Stakeholders concurred that the improvements in coverage were likely driven by NNM.InterpretationFocused policy attention and programmatic efforts improved coverage and reduced inequities indicating an inclusive approach. Persistent coverage gaps for certain interventions require further inquiry.

Breastfeeding Support for All: Narrowing Inequalities for Nurturing a Future That Is Fair and Sustainable

Early initiation of breastfeeding, duration, and exclusivity are internationally recommended practices to maximize impact in preventing and reducing the double burden of malnutrition, reducing child mortality and other morbidities, improving child development, and reducing health costs. The protection, promotion, and support of breastfeeding are also central to countries' commitments to the 2030 Agenda for Sustainable Development. Global progress in breastfeeding rates has been reported in past years. However, many children are still left behind and are not receiving the numerous benefits of breastfeeding. There are also relevant inequities in breastfeeding rates across global regions and countries and between groups from different socioeconomic statuses and cultural backgrounds within countries. These inequities are also extended to the frequency and availability of comparable standardized indicators worldwide. In 2023, globally, only 39% of the countries provided information on exclusive breastfeeding rates to the age of six months, and 7% assessed their overall breastfeeding support programs in the past five years. The increase in the coverage and frequency of monitoring globally agreed breastfeeding targets and the assessment of the impact of breastfeeding programs are imperative to providing breastfeeding support for all and increasing breastfeeding prevalence equitably. Breastfeeding can be an equalizer in society; no one should be left behind. Only this way can breastfeeding mums be celebrated in all their diversity and journeys, attending the 2024 World Breastfeeding Week call.

Drone Insights: Unveiling Beach Usage through AI-Powered People Counting

Ocean beaches are a major recreational attraction in many coastal cities, requiring accurate visitor counts for infrastructure planning and value estimation. We developed a novel method to assess beach usage on the Gold Coast, Australia, using 507 drone surveys across 24 beaches. The surveys covered 30 km of coastline, accounting for different seasons, times of day, and environmental conditions. Two AI models were employed: one for counting people on land and in water (91–95% accuracy), and another for identifying usage types (85–92% accuracy). Using drone data, we estimated annual beach usage at 34 million people in 2022/23, with 55% on land and 45% in water—approximately double the most recent estimate from lifeguard counts, which are spatially limited and prone to human error. When applying similar restrictions as lifeguard surveys, drone data estimated 15 million visits, aligning closely with lifeguard counts (within 9%). Temporal (time of day, day of the week, season) and spatial (beach location) factors were the strongest predictors of beach usage, with additional patterns explained by weather variables. Our method, combining drones with AI, enhances the coverage, accuracy, and granularity of beach monitoring, offering a scalable, cost-effective solution for long-term usage assessment.

Summary Analysis of National Surveillance on Kashin-Beck Disease from 1990 to 2023

ObjectiveTo analyze the epidemiological characteristics and epidemic situation of children with Kashin-Beck disease (KBD) in China, and provide the basis for formulating prevention and control measures. MethodsFixed-point monitoring, moving-point monitoring, and full coverage of monitoring were promoted successively from 1990 to 2023. Some children (7–12 years old) underwent clinical and righthand X-ray examinations every year. According to the KBD diagnosis criteria, clinical and X-ray assessments were used to confirm the diagnosis. ResultsIn 1990, the national KBD detectable rate was 21.01%. X-ray detection decreased to below 10% in 2003 and below 5% in 2007. Between 2010 and 2018, the prevalence of KBD in children was less than 0.4%, which fluctuated at a low level, and has decreased to 0% since 2019. Spatial epidemiological analysis indicated a spatial clustering of adult patients prevalence rate in the KBD areas. ConclusionThe evaluation results of the elimination of KBD in China over the last 5 years showed that all villages in the monitored areas have reached the elimination standard. While the adult KBD patients still need for policy consideration and care.

Managing cyanobacterial blooms in recreational waters: decision support tools for public health responses

Freshwater blooms of cyanobacteria present a challenge to those tasked with managing beaches during bathing season, both to ensure the protection of public health, and to avoid lengthy beach closures. The combined effects of climate change and environmental pollution could cause blooms to become more frequent, intense, and persistent in the future in some locations, necessitating regularly review and update of response protocols. Decision support tools are used to help manage bloom events and inform responses. These can advise on the triggers for inspection, testing, posting of advisories, closing of beaches, and when to rescind advisories and reopen beaches. The aim of this paper was to present an overview of approaches and decision support tools used to inform public health responses to cyanobacterial blooms. During bathing season in Canada, most bloom monitoring is reactive, with a limited coverage of proactive monitoring, except at priority beaches. Responses to blooms vary widely, but many are informed by decision protocols or flow charts using visual inspection and single-level indicators, or alert level frameworks using multiple indicators and alert levels. The only health-based indicators used in any system are cyanotoxins, but capacity for frequent testing is often limited. Approaches to rescinding advisories also vary in the types of indicators and length of time used to determine when it is safe to resume recreational activities. This can vary from days to weeks, with some jurisdictions taking more precautionary approaches. Responsible authorities must balance public health protection with available resources for testing and monitoring with public acceptance of extended beach closures. With the prospect of more frequent and pervasive blooms in the future, there will be a need to allocate scare resources efficiently, which may require regular review and update of response protocols. Adapting approaches may require using a range of more accessible indicators alongside local knowledge, site history, and new tools to inform site-specific responses.

Fusion of computer vision and piezoelectric tactility approach to measuring moving vehicle loads

Vehicle overloading seriously threatens the safety and long-term operation of global traffic infrastructure. The emergence of computer vision-based non-contact weigh-in-motion (NC-WIM) technique has made it possible to expand the coverage of vehicle load monitoring, but misjudgment of vehicle weight due to abnormal tire pressure is still an insurmountable drawback of this approach. In this study, we propose a novel approach to measure moving vehicle loads by fusing computer vision and piezoelectric tactility. Meanwhile, a visual-tactile sensing system based on the proposed method is built on a real road. Field tests show that the proposed method can effectively avoid the misjudgment caused by abnormal tire pressure, and the errors of vehicle weight estimation are within the range of ± 10 % in both underinflated and overinflated cases. The proposed solution not only ensures the accuracy of overload warnings, but also promises to provide support for the design, operation, and maintenance of transport infrastructure.

Stronger together: different community science platforms all contribute to wildlife research

Context Engaging the general public can increase spatio-temporal coverage of wildlife monitoring. Given the potentially substantial costs, we need to evaluate the contribution of known and planned initiatives and confirm whether multiple platforms increase the efficiency of data collection. As observer behaviour affects data quantity and quality, users of specialised and generalist platforms are expected to behave differently, resulting in more connected networks for specialised and higher nestedness for generalist platforms. Specialist observers are expected to contribute a balanced ratio of rare and common species, whereas non-specialist contribution will depend more on species detectability. Aims We aim to evaluate whether the combined contribution of observers from different platforms can improve the quality of occurrence and distribution data of 218 endemic Atlantic Forest bird species in Brazil. We also describe and compare observer-bird species interaction networks to illustrate observer behaviour on different platforms. Methods On the basis of data from five community science platforms in Brazil, namely, eBird, WikiAves, Biofaces, iNaturalist and Táxeus, we compared the spatial distribution of bird observations, the number of observers, the presence of the same observers on various platforms, bird species coverage, and the proportion of duplicate observations within and among platforms. Key results Although species coverage of the joint dataset increased by up to 100%, spatial completeness among the five platforms was low. The network of individual platforms had low values of clustering, and the network of the joint dataset had low connectance and high nestedness. Conclusions Each platform had a somewhat unique contribution. Pooling these datasets and integrating them with standardised data can inform our knowledge on bird distributions and trends in this fragile biome. Nevertheless, we encourage observers to provide precise coordinates, dates and other data (and platforms to accommodate such data) and recommend submitting data from all platforms into the Global Biodiversity Information Facility to support wildlife research and conservation. Implications If new platforms engage more and different people, new initiatives can cover poorly represented areas and successfully expand monitoring effort for Atlantic Forest endemic bird species.

The incidence, monitoring coverage and clinical characteristics of hydroxychloroquine retinopathy in the United Kingdom

BackgroundRetinal monitoring is recommended for hydroxychloroquine users to detect pre-symptomatic retinopathy and preserve visual function. However, the incidence of hydroxychloroquine retinopathy and monitoring coverage in the U.K. are incompletely characterised. Moreover, the visual benefits of monitoring for retinopathy – recommended for over 70,000 long-term hydroxychloroquine users in the U.K. - remain unproven.MethodsA national, prospective observational study was undertaken with the British Ophthalmological Surveillance Unit (BOSU). Newly diagnosed cases of hydroxychloroquine retinopathy in the U.K. were reported and data captured using a standardised questionnaire over 3.5 years (July 2018–Dec 2021). The frequency of retinopathy and coverage of monitoring amongst long-term users was estimated. Visual function was compared between asymptomatic individuals detected on monitoring and those presenting with visual symptoms. The clinical characteristics, dosing and management of reported cases were captured.ResultsThe annualised number of incident cases of hydroxychloroquine retinopathy was 29–57, with an annualised frequency of 0.04–0.08% amongst long-term users (~1 in 1247–2625). The coverage of monitoring was approximately 2.6–5.5%. Visual acuity (0.1 vs. 0.22 logMAR; p = 0.007) and visual field mean deviation (−3.73 dB vs. −8.69 dB; p = 0.017) were better preserved in asymptomatic individuals compared to those presenting with visual symptoms.ConclusionThese data support the efficacy of monitoring in the preservation of visual function in patients with hydroxychloroquine retinopathy at diagnosis. The overall population coverage of monitoring was low, consistent with the high proportion of symptomatic patients at diagnosis. This study presents a method for evaluating the yield of monitoring for hydroxychloroquine retinopathy in the U.K.

Current State And Trends In The Incidence Of Chronic Kidney Disease Among Adult Population And Opportunities For Its Prevention And Treatment In Moscow Healthcare Organizations

Introduction. Currently, the incidence of kidney diseases among adult population is growing. It is expected to become the fifth leading cause of death by 2040. Showing no symptoms at early stages, chronic kidney disease (CKD) is closely related to diabetes mellitus, hypertension, obesity and kidney dysfunction in older age, which requires long-term and expensive treatment. Organization structure and the strategy of medical care provided by Moscow nephrology service is aimed at implementing measures for risk prevention and active early detection of kidney disease, as well as informing general population and medical community about existing diagnostic methods, modern treatment options and schemes. The purpose of the study was to identify trends in CKD prevalence and incidence among able-bodied population in the context of Moscow nephrology service improvement. Materials and Methods. An analysis of disease prevalence and incidence among able-bodied population based on official statistics data of the Russian Research Institute of Health (RIH) of the Ministry of Health of the Russian Federation and the Centre of Medical Statistics of Moscow Healthcare Department was carried out; indicators of time series and average growth/decrease rates were calculated. In order to identify a long-term trend, least squares method was used. Incidence rates were expressed per 100,000 able-bodied population. Results. In 2022, the overall disease prevalence in Moscow was lower than for the country as a whole (31.3 and 74.2 cases per 100,000 population, respectively), while incidence of the able-bodied population in Moscow was lower than countrywide (2.9 and 11.5 cases per 100,000 population, respectively). The long-term dynamics of CRD prevalence and incidence was generally characterized by an upward trend. Thus, the average annual incidence in Moscow was 9.8% and 6.1%, while in the Russian Federation 7.2% and 5.4%, respectively. On average, 91.3% of able-bodied patients with CKD in Moscow are under follow-up monitoring for a year. The number of dispensary patients with CKD increased by 2.5 times in 2022 in comparison to 2013, from 11.7 to 29.5 per 100,000 population, respectively. Since 2016, patients with newly diagnosed CKD have been provided with 100% coverage of follow-up monitoring.

Citizen scientists filling knowledge gaps of phosphate pollution dynamics in rural areas

In situ monitoring is fundamental to manage eutrophication in rivers and streams. However, in recent decades, the frequency and spatial coverage of regulatory monitoring have often been reduced due to funding and infrastructure limitations. This reduction has made it impossible to provide adequate coverage for most water bodies. In this study, trained citizen scientists filled spatial and temporal gaps in agency monitoring across a major catchment in rural England. By integrating data from citizen scientists, regulatory agencies, and the local water company, it was possible to demonstrate the opportunities for hypothesis-based citizen scientist monitoring to identify continuous and event-driven sources of phosphate pollution. Local citizen scientists effectively covered important spatial gaps, investigating river conditions both upstream and downstream of suspected pollution point sources, improving the identification of their temporal dynamics. When combined with long-term monitoring data from regulatory agencies, it became possible to identify areas within the catchment that exhibited increased phosphate concentrations during periods of low river discharge (summer). Inter-annual trends and anomaly detection suggested that continuous pollution sources dominated over event-driven sources in many sub-basins, allowing for the prioritisation of mitigation actions. This study highlights the opportunity for citizen scientists to fill gaps in regulatory monitoring efforts and contribute to the improved management of eutrophication in rural catchments.

Contributions of the vegetation index (NDVI) in water quality prediction models in a semi-arid tropical watershed

In this work, a new approach to using NDVI as a predictor of water quality parameters in arid environments is proposed. Our focus was the Araçuaí river basin, which has a predominance of native cerrado vegetation and is subject to seasonal variations in rainfall and vegetation cover. MODIS images (MOD13Q1) from 2000 to 2018 were used to calculate the NDVI of the contributing areas of the water quality monitoring stations and to analyze its relationship with fourteen water quality parameters. The NDVI showed significant seasonality, with high values in the rainy season, and temporal trends of increase in stretches related to the main river. A strong and new relationship was observed between NDVI and six water quality parameters: color, total dissolved solids, total suspended solids, total solids, nitrate and turbidity, this being stronger at the local scale, with better performance at from models that use incremental NDVI, capturing local variations in vegetation cover, instead of regional NDVI, which reflects the general state of vegetation. The results demonstrate the potential of using these indices to develop and improve prediction models of water quality parameters in river basins and to expand the spatial and temporal coverage of water quality monitoring.

Low-cost tree crown dieback estimation using deep learning-based segmentation

Abstract The global increase in observed forest dieback, characterized by the death of tree foliage, heralds widespread decline in forest ecosystems. This degradation causes significant changes to ecosystem services and functions, including habitat provision and carbon sequestration, which can be difficult to detect using traditional monitoring techniques, highlighting the need for large-scale and high-frequency monitoring. Contemporary developments in the instruments and methods to gather and process data at large scales mean this monitoring is now possible. In particular, the advancement of low-cost drone technology and deep learning on consumer-level hardware provide new opportunities. Here, we use an approach based on deep learning and vegetation indices to assess crown dieback from RGB aerial data without the need for expensive instrumentation such as LiDAR. We use an iterative approach to match crown footprints predicted by deep learning with field-based inventory data from a Mediterranean ecosystem exhibiting drought-induced dieback, and compare expert field-based crown dieback estimation with vegetation index-based estimates. We obtain high overall segmentation accuracy (mAP: 0.519) without the need for additional technical development of the underlying Mask R-CNN model, underscoring the potential of these approaches for non-expert use and proving their applicability to real-world conservation. We also find that color-coordinate based estimates of dieback correlate well with expert field-based estimation. Substituting ground truth for Mask R-CNN model predictions showed negligible impact on dieback estimates, indicating robustness. Our findings demonstrate the potential of automated data collection and processing, including the application of deep learning, to improve the coverage, speed, and cost of forest dieback monitoring.

On Orchestration of Segment Routing and In-Band Network Telemetry

With the rapid development of programmable data plane (PDP), both segment routing (SR) and in-band network telemetry (INT) have attracted intensive interests. Hence, we have previously proposed the technique of SR-INT, which explores the benefits of SR and INT simultaneously and gets rid of the hassle of the accumulated overheads of them. In this work, we further expand the advantage of SR-INT by studying how to plan the SR-INT schemes of flows at the network level to balance the tradeoff between bandwidth usage and coverage of network monitoring, namely, the problem of “SR-INT orchestration". A mixed integer linear programming model (MILP) is first formulated for the problem, and we prove its NP-hardness. Then, to reduce the time complexity of problem-solving, we propose a novel greedy algorithm based on path ranking and a column generation (CG) based approximation algorithm. Extensive simulations verify the performance of our proposed algorithms.

Socioeconomic disparities in Rwanda’s under-5 population’s growth tracking and nutrition promotion: findings from the 2019–2020 demographic and health survey

BackgroundRegular growth monitoring can be used to evaluate young children’s nutritional and physical health. While adequate evaluation of the scope and quality of nutrition interventions is necessary to increase their effectiveness, there is little research on growth monitoring coverage measurement. The purpose of this study was to investigate socioeconomic disparities in under-5 Rwandan children who participate in growth monitoring and nutrition promotion.MethodsWe used data from the 2019–2020 Rwanda Demographic and Health Survey (RDHS), which included 8092under-5 children. Percentage was employed in univariate analysis. To examine the socioeconomic inequalities, concentration indices and Lorenz curves were used in growth monitoring and nutrition promotion among under-5 children.ResultsA weighted prevalence of 33.0% (95%CI: 30.6-35.6%) under-5 children growth monitoring and nutrition promotion was estimated. Growth monitoring and nutrition promotion among under-5 children had higher uptake in the most disadvantaged cohort, as the line of equality sags below the diagonal line in Lorenz curve. Overall, there was pro-poor growth monitoring and nutrition promotion among under-5 in Rwanda (Conc. Index = 0.0994; SE = 0.0111). Across the levels of child and mother’s characteristics, the results show higher coverage of under-5 growth monitoring and nutrition promotion in the most socioeconomic disadvantaged cohort.ConclusionThe study found a pro-poor disparity in growth monitoring and nutrition promotion among under-5 children in Rwanda. By implication, the most disadvantaged children had a higher uptake of growth monitoring and nutrition promotion. The Rwanda government should develop policies and programmes to achieve the universal health coverage for the well-off and underserved population.

Exploratory study of the Sentinel-3 level 2 product for monitoring chlorophyll-a and assessing ecological status in Danish seas

In situ Chl-a data were used to perform empirical calibration and validation of Sentinel-3 level 2 product in Danish marine waters. Comparing in situ data with both same-day and ±5 days moving averaged Sentiel-3 Chl-a values yielded two similar positive correlations (p > 0.05) with rpearson values of 0.56 and 0.53, respectively. However, as the moving averaged values resulted in significantly more available data than daily matchups (N = 392 vs. N = 1292) at a similar quality of correlation with similar model parameters (slope (1.53 and 1.7) and intercept (−0.28 and −0.33) respectively), which were not significantly different (p > 0.05), the further analyses were focused on ±5 days moving averaged values. A thorough comparison of seasonal and growing season averages (GSA) also showed a very good agreement, except for a few stations characterized by very shallow depth. Overestimation by the Sentinel-3 occurred in shallow coastal areas and was attributed to the interferences from benthic vegetation and high levels of Colored Dissolved Organic matter (CDOM) interfering with the Chl-a signals. Underestimation observed in the inner estuaries with shallow Chl-a rich waters, however, seen as a result of self-shading at high Chl-a concentrations, reducing effective absorption by phytoplankton. Besides the observed minor disagreements, there was no significant difference when the GSA values from in situ and Sentinel-3 were compared for all three water types (p > 0.05, N = 110). Analyzing Chl-a estimates along a depth gradient showed significant (p < 0.001) non-linear trends of declining concentrations from shallow to deeper waters for both in situ (explaining 15.2 % of the variance (N = 109)) and Sentinel-3 data (explaining 36.3 % of the variance (N = 110)), with higher variability in shallow waters. Furthermore, Sentinel-3 enabled full spatial coverage of all 102 monitored water bodies providing GSA data at much higher spatial and temporal resolutions for good ecological status (GES) assessment compared to only 61 through in situ sampling. This underlines the potential of Sentinel-3 for substantially extending the geographical coverage of monitoring and assessment. However, the systematic over- and underestimation of Chl-a in shallow nutrient rich inner estuaries through Sentinel-3 requires further attention to enable routine application of the Sentinel-3 level 2 standard product in the operational Chl-a monitoring in Danish coastal waters. We provide methodological recommendations on how to improve the Sentinel-3 products' representation of in situ Chl-a conditions. Continued frequent in situ sampling remains important for monitoring as these measurements provide essential data for empirical calibration and validation of satellite based estimates to reduce possible systematic bias.

An Estimation of Daily PM2.5 Concentration in Thailand Using Satellite Data at 1-Kilometer Resolution

This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-km resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011–2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R2) and root mean square error (RMSE), and the results indicate high accuracy for training (R2: 0.95, RMSE: 5.58 μg/m3), validation (R2: 0.78, RMSE: 11.18 μg/m3), and testing (R2: 0.71, RMSE: 8.79 μg/m3) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.

Using qPCR of environmental DNA (eDNA) to estimate the biomass of juvenile Pacific salmon (Oncorhynchus spp.)

AbstractDuring the outmigration of Pacific Salmon, the early marine phase is a critical period when high mortality can occur. Traditional sampling and monitoring of juvenile salmon migration can be limited by logistically intensive gear requirements, accessibility, and cost. Improved understanding of the early marine phase, for example, migration duration and habitat use, requires innovative techniques that can improve the spatial and temporal coverage of monitoring. Environmental DNA (eDNA) is genetic fragments present in the environment that can be used as a proxy for organism presence and can be effectively and efficiently collected through water samples. Estimating fish abundance or biomass from eDNA concentration data would provide a valuable fisheries tool but remains challenging to calibrate. To quantify the relationship between eDNA abundance and fish biomass, we used a controlled mesocosm experiment, in which eDNA samples were collected from 15 aquaria (340 L) with varying densities of juvenile Chinook salmon per tank (0, 5, 10, 20, and 30). The concentration of eDNA was obtained by qPCR scaled with fish biomass (ANOVA, p &lt; 0.05). However, we also observed that variability of eDNA concentrations among replicates of the same treatment positively scaled biomass (ANOVA, p &lt; 0.05). Therefore, higher biomasses of fish can yield more challenging data to interpret. This study lays important groundwork for the application of eDNA for monitoring juvenile salmonids yet highlights caveats for the applicability of eDNA as a stand‐alone method to assess biomass in a field setting.

Trends in care quality in China from 2011 to 2017: An analysis based on the National Specific (Single) Disease Monitoring System.

The Ministry of Health of China conducted a study targeting in single-disease quality control in 2009, aimed to strengthen quality management and improve health care services. This study retrospectively investigated the trends of quality indicators for six monitored diseases 2011-2017 to evaluate the improvement of care quality for the first batch of single-disease. We extracted data from the National Specific (Single) Disease Monitoring System for 2011-2017. We focused on six conditions: acute myocardial infarction, heart failure, community-acquired pneumonia, coronary artery bypass graft, hip / knee replacement, and acute ischemic stroke. A total of 56 quality indicators (QIs) were adopted to monitor the quality change and determine the trends in care quality. We also calculated the hospital process composite performance (HPCP) using a denominator-based weighting method for each hospital per year. The estimated annual percentage changes (EAPC) 2011-2017 were calculated at national and regional levels. The results showed that use of four QIs had significant downward trends, whereas 25 QIs (including reversed indicators) showed significant upward trends from 2011 to 2017. The greatest improvement was observed in CAP-4 (antibiotic treatment within four hours after admission to the hospital for critical pneumonia) in the central region (EAPC = 48.36, 95% CI = 15.92-89.87); while the largest decrease appeared in AIS-1 (thrombolytic therapy within 4.5 hours of symptom onset) in the western region (EAPC = -13.44, 95% CI = -24.98,-0.11). An increased HPCP was observed in four diseases nationwide, but not for acute myocardial infarction and heart failure. However, there were significant differences across regions in the process of care and outcomes, with the performance of Eastern and Western regions showing remarkable advantages compared with the Central region. We provide evidence for major advancement in care quality in China nationwide. However, the improvement of care in China was unbalanced geographically and should be carefully considered. Future challenges include expanding the coverage of quality monitoring, greater delivery efficiency, and region-balanced health care.

Remote and local threats are associated with population change in Taiwanese migratory waterbirds

Although several countries along the East Asian-Australasian Flyway have recently begun reporting population trends and identifying threats to migratory waterbirds, there remains rather sparse geographical coverage of systematic waterbird monitoring, impeding our understanding of the flyway-wide status of waterbird populations. To fill this gap, we used the data from a nationwide citizen science project, the “Taiwan New Year Bird Count'' to examine recent population trends of 31 migratory waterbird species across Taiwan, and within three of its waterbird hotspot regions, between 2014 and 2021. Island-wide, the abundance of two species declined significantly while five species increased. However, there was substantial heterogeneity in population trends among regions, with nine species declining significantly in Yi-Lan, four species in Chang-Hua and one species in Chia-Nan. Conversely, 11 species increased significantly in Chia-Nan, one species in Chang-Hua, but no species increased in Yi-Lan. This suggests that a combination of local and remote factors is driving population change in Taiwanese migratory waterbirds. Moreover, species that use local rice paddies or are dependent on tidal flats around the Yellow Sea were more likely to show population declines, and those able to use aquacultural wetlands showed growth. These results suggest that recent rice paddy loss and habitat loss in the Yellow Sea have been contributing to migratory waterbird declines in Taiwanese wetlands. Our findings suggest that local land use planning policies within Taiwan as well as mitigation of Yellow Sea tidal flat loss are likely to be complementary in safeguarding the future of migratory waterbirds in Taiwan.

Spatial variability in the structure of fish assemblages associated with Laminaria hyperborea forests in the NE Atlantic

Understanding fish-habitat associations is critical for ecosystem-based approaches to management and conservation. Kelp species, which are estimated to inhabit around 25% of the world's coastline and underpin highly productive and biodiverse ecosystems, are widely recognised as important nursery and foraging habitats for coastal fish species. However, quantitative assessments of fish assemblages within kelp forests are lacking for many regions. Here, we used Baited Remote Underwater Video (BRUV) and Underwater Visual Census (UVC) to quantify fish assemblages in Laminaria hyperborea forests at eight shallow subtidal rocky reefs that spanned 9° of latitude in the United Kingdom (UK). Fish assemblages were a ubiquitous and conspicuous component of kelp forest communities at all sites. BRUV surveys recorded marked regional-scale variability, with latitudinal shifts in assemblage structure and particularly distinct assemblages recorded in southwest England. These patterns were largely due to greater abundances of Pollachius spp. (i.e. saithe and pollock) in northern regions and higher numbers of Gobiusculus flavescens (two-spotted goby) and labrid species (i.e. rock cook, goldsinny and ballan wrasses) in southern regions. Unlike BRUVs, UVC surveys did not detect significant regional-scale variation in assemblage structure but did detect significant site-level differences, highlighting differences between the two techniques. BRUV surveys also recorded clear latitudinal trends in richness, with more taxa recorded at lower latitudes. Fish assemblages in the NE Atlantic have been, and will continue to be, impacted by ocean warming and fishing activities. Greater spatiotemporal coverage of kelp forest surveys and ongoing robust monitoring is needed to better understand and manage future ecological changes.