dataesr/scientific-paragraphs-categorization huggingface dataset:
Research in context Evidence before this study Rigorous, comparable, and timely estimates of vaccine coverage are needed to inform vaccination policies, programmes, and investments. WHO and UNICEF gather country-reported administrative and household survey data each year, among other immunisation indicators, through the Joint Reporting Form for immunisation, and annually produce the WHO-UNICEF Estimates of National Immunization Coverage (WUENIC) for member states. This estimation process, which has been described as a rule-based approach combining heuristics with expert assessment and decisions, has some strengths, including familiarity for key stakeholders and the ability to integrate expert opinion on vaccine coverage and its drivers. Compared to statistical models, however, the WUENIC approach does not produce quantitative estimates of uncertainty and adjusts only for relatively large discrepancies between country-reported data and household survey coverage estimates.
). We utilised survey and administrative data on vaccine coverage via a multistep modelling approach that includes bias adjustments for discordance between survey data and administrative data, and propagated uncertainty through each estimation step. Last, we did secondary analyses to further examine relationships between changes in sociodemographic development and vaccine coverage, and explored trends in those children who never received a DTP dose (referred to as zero-dose children,  9, 18, 19  proxying for children currently not reached by routine immunisation programmes). This study offers a crucial benchmark for global patterns in vaccine-specific and overall childhood vaccination trends before COVID-19, strengthening our collective understanding of past progress and challenges as the world pursues greater equity in immunisation access and delivery.
Overview 1] [22]  The Global Health Data Exchange (GHDx) will be updated simultaneously with the release of new GBD rounds; content in these resources will always be synchronous. This analysis complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement,  23  with further information provided in the appendix (section 1). All data processing and modelling were done in R statistical software, with more details provided in the appendix (sections 2-4) by analytical step; the source code will be made accessible upon publication and data are available on the GHDx website.
Data We defined vaccine coverage as the proportion of children who received at least the stated vaccine dose (eg, DTP1) through a routine immunisation programme; we excluded campaign doses when possible. Of 3118 total sources reviewed, 975 unique sources from 1980 to 2019 were used in this analysis, resulting in 55 326 country-cohort-year-vaccine-dose-specific data points across vaccines (appendix; tables S1-S3). We primarily used the GHDx to collate available coverage data sources as described in the appendix (sections 2. 1-2. 3; figure  S2 ). These sources included household surveys (eg, Demographic and Health Surveys, Multiple Indicator Cluster Surveys, other multi-country survey series, and country-specific surveys) and official country-reported coverage data from the JRF. We excluded sources without data on children aged 12-59 months (aside from countryreported data, which reflect target population ages) and sources that were not nationally representative (ie, geographically or focused on a subgroup of the target population) or did not include dose-specific vaccine coverage from at least one vaccine in or after the country-reported national introduction year.
We then reviewed all vaccine coverage observations from sources meeting these criteria and excluded data obtained before introduction of each vaccine or judged to be implausible. Complete inclusion and exclusion criteria and details about all reviewed data sources are summarised in the appendix (section 2. 3; figure  S2 , tables S1-S3). For survey data, children with either home-based records or parental recall indicating vaccine receipt were considered vaccinated. Where individual-level microdata were available, we estimated coverage as the proportion of vaccinated children by vaccine, dose, and age in years, accounting for survey design (appendix section 2. 4). We extracted survey report tabulations if microdata were unavailable. Age-cohort-specific coverage data from children aged 12-59 months were assigned to the year of expected vaccine receipt using country-specific vaccine schedules and vaccine introduction years reported through the JRF.
Vaccine-specific coverage estimation We modelled vaccine-specific coverage using ST-GPR,  20  a statistical method that enables non-linear trend estimation and incorporates data uncertainty into final estimates (appendix section 3. 2). We used the HAQ Index and GBD mortality estimates from conflict and terrorism per capita as covariates in the first stage of each model, along with a covariate based on country-reported stockouts or other disruptions derived from discontinuities in administrative data (appendix section 3. 7). Relative to DTP3, country-reported data on DTP1 were sparse or not routinely collected from 1980 to 2000. We developed a time-varying model to impute DTP1 from reported DTP3 coverage and trends in DTP1-3 dropout, and used continuation-ratio ordinal regression to ensure internal consistency between DTP1 and DTP3 for the full time period (appendix section 3.
3). 28 ince more recently introduced vaccines had comparatively less available data, we used the more data-rich DTP3 and MCV1 models to inform estimation of HepB3, Hib3, PCV3, RotaC, MCV2, and RCV1 coverage (appendix section 3. 6). We modelled the coverage ratio of newer vaccines relative to reference vaccines (DTP3 or MCV1) on the basis of schedule similarity, using observations from both survey data and unadjusted country-reported data. We used ST-GPR to estimate full time series for each scale-up ratio, then multiplied these ratios by corresponding DTP3 or MCV1 estimates to produce final estimates for all newer vaccines while propagating uncertainty. We assumed 0% coverage for each vaccine before its formal introduction in national immunisation schedules, with the exception of Hib3, PCV3, and RotaC in China.
41, 42 y quantifying uncertainty and synthesising multiple sources of coverage data while adjusting for biases, statistically derived coverage estimates provide a robust platform to track progress towards global targets. Moreover, IA2030 marks an evolution in global immunisation priorities, recognising the limitations of the more topdown GVAP  46  and championing locally tailored approaches for each community. IA2030 also emphasises the need to identify and reach zero-dose children historically missed by routine immunisation services, and that strengthening of such services must occur in tandem with bolstering service access and primary care integration. 47 To support these efforts, the action-based IA2030 Monitoring and Evaluation Frame work supports the use of data not only to track progress but also to continuously improve routine immunisation programmes at all levels of implementation.
9 This shift in strategy aims to further develop the reach, equity, and sustainability of global immunisation systems within the contexts of primary care and universal health coverage. For these efforts to be successful, however, data must be of sufficient quality to inform policy decisions. As such, our estimates can serve as a useful comparator to those produced by WUENIC:  48  areas of divergence might indicate low data availability or quality, resulting in high sensitivity to methodological assumptions. Countries where coverage gains have outpaced the average pace of progress could provide valuable insights for breaking through coverage plateaus. For example, studies of vaccine coverage in Burkina Faso emphasise the importance of leadership and communication from vaccination centres in promoting effective services.
Acknowledgments The boundaries and names shown and the designations used on the maps do not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city, or area or of its authorities or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The authors alone are responsible for the views expressed in this Article and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated. We wish to acknowledge the members of the  WUENIC Working Group , expressing our gratitude for the invaluable insights and thoughtful discussions that they have shared with us over the course of this work.
Olatunji O Adetokunboh  acknowledges the support of the  South African Department of Science and Innovation , and  National Research Foundation ,  South Africa. Saira Afzal  acknowledges support from the  Department of Community Medicine, King Edward Medical University, Lahore, Pakistan. Syed Mohamed Aljunid acknowledges the  Department of Health Policy and Management, Faculty of Public Health, Kuwait University, Kuwait , and  International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia ,  Kuala Lumpur, Malaysia , for the approval and support to participate in this research project. Sonu Bhaskar  acknowledges funding support from the  NSW Ministry of Health  and institutional support from  NSW Health Pathology. Till Winfried Bärnighausen was supported by the  Alexander von Humboldt Foundation  through the  Alexander von Humboldt Professor award , funded by the  German Federal Ministry of Education and Research.
The per-protocol population is all randomized, vaccinated subjects without major protocol deviations (i. e. , non-compliance with the selection criteria or with the allocated vaccine or the time between the two doses, missing primary endpoint, or subjects with positive, doubtful or missing NP antibody serology at inclusion or day 28). Missing data for the primary endpoint was replaced by the geometric mean value of antibody levels observed in the group of the concerned subject. Other missing data were not replaced. Safety assessment was analyzed among the safety population (i. e. , all randomized subjects). Adverse reactions and events were described using frequencies and percentages. SAS software (version 9. 4, SAS Institute, Cary, NC) was used for the statistical analyses. R freeware (version 3.
6. 3) and GraphPad Prism software (version 9. 2. 0, San Diego, California USA) were used for the graphs. Statistical significance was considered when the lower bound of the two-sided 95% CI of the primary endpoint was above the pre-defined margin for non-inferiority analyses, and when the p-value of the other endpoints was below 0¢05. In case of multiple comparisons, Bonferroni correction was performed. Supplemental statistical analysis methods are reported in supplement 2. This study is registered with ClinicalTrials. gov, NCT04900467; N°EudraCT: 2021-002,174-52.
Contributors OL, CJ, AR, XDL, LB and TS designed the study. AT, CP, OL, CJ, ML, JM, OE, ZMV, CC, BL, HA, AA, KL, CSM, EBN, MB, TB, PL, BB OB and MM acquired, analyzed or interpreted the study. CJ, OL, MC, CP, AR, LN, ML, JM, OE, ZMV, CC, BL, HA, AA, KL, CSM, EBN, MB, TB, PL, BB, OB drafted the manuscript. OL, TS, CJ, AR and XDL critically reviewed the manuscript for important intellectual content. MC and AR did the statistical analysis. OL obtained funding. CP, AT, FD were in charge of administrative, technical and material support. OL, CJ and TS supervised the study. CJ and OL had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Data sharing The individual participant-level data that underlie the results reported in this article will be shared after de-identification (text, tables, figures, and appendices). This clinical trial is ongoing, and all individual participant-level data will not be available until after the immune persistence assessments have been done. The data will be made available immediately after publication and finalization of the complete clinical study report, for at least 6 months. Researchers who provide a scientifically sound proposal will be allowed to access the de-identified individual participant data. Proposals should be sent to the corresponding authors. These proposals will be reviewed and approved by the sponsor, investigator, and collaborators on the basis of scientific merit. To gain access, data requesters will need to sign a data access agreement.
Literature review A PRISMA-compliant systematic review of the literature was conducted around four topics: (1) vaccines for SARS-CoV-2 for ORL-HNS PBFHW; (2) vaccines for SARS-CoV-2 for all PBFHW; (3) risk and prevention of SARS-CoV-2 infection in ORL-HNS; and (4) SARS-CoV-2 infection in PBFHW. MEDLINE, EMBASE, and Web of Science databases were searched on December, 28th for studies in English, Italian, German, French, or Spanish that reported data obtained from human subjects. Example search keys are shown in Online Resource 3. Searches for topic 1 delivered no results. Due to a lack of high-quality studies on topics 2 and 3, the systematic review was extended from what has been recommended for CCSs  [13]  to include all studies published on the topics. Upon evaluating the literature recovered through the systematic review, a collection of 84 articles, representing the highest and most recent evidence on the topics, was prepared and distributed to all authors to be revised over 1 week (see Online Resource 4 for the full references of the distributed literature).
Although there are different ways to account for emissions, all approaches aim to yield estimates that are accurate, comparable, comprehensive, and complete  (Ibrahim et al 2012 , Ramaswami et al 2012). Several guidelines have been developed, each having a slightly different perspective on how a city should account for their emissions  (Arioli et al 2020). The original city-scale guidelines borrowed much from the IPCC framework for nations  (IPCC 2006 , ICLEI 2009). Using this framework, emissions are estimated by combining activity data with sectoral emission factors obtained from, for instance, the IPCC emission factor database (www. ipccnggip. iges. or. jp/EFDB/main. php), to provide emissions by economic sector. From this perspective, only scope 1 GHG emissions are inventoried (i. e. those emissions directly resulting from activities that take place within a city's jurisdiction).
We derive scope 1 CO 2 emission from the City of Indianapolis' Thrive report and GHG Inventory (2018, 2021) (detailed in table  S2 ). We directly compare the (a) city-wide totals  (2010, 2013, 2016) , (b) city reported scope 1 sectoral emissions (2013) to (c) Hestia-Indianapolis emissions. In our sub-city case study, we use the annual sum of the on-road sector CO 2 emissions from the granular Hestia-Baltimore data product (for the city domain only; Roest et al 2020). A city-scale integrated approach cannot be used to estimate emissions for 2014 given atmospheric observation limitations for that year. We assume that Hestia-Baltimore provides a realistic spatial representation of on-road emissions that would result from an application of an integrated approach. These emissions have been compared to the City of Baltimore SRI and differences were diagnosed by Roest et al  (2020).
Hestia on-road emissions are estimated for each road segment as detailed in  Gurney et al (2020a). Briefly, the developers used an inverse distance weighting of average annual daily traffic (AADT) from the Federal Highway Administration's vehicle counting station data, which were disaggregated by road type. The AADT were then used with the road segment length to estimate the vehicle miles travelled for each road segment, which provides a relative metric to distribute county-level FFCO 2 emissions from the 2011 EPA National Emissions Inventory (NEI). Local gap-filled traffic data were used to distribute emissions in time to the hourly scale, though only annual emissions are used in this study. More details on the on-road emission methods can be found in the Vulcan  (Gurney et al 2020a)  and Hestia-Baltimore (Roest et al 2020).
We also use a housing market typology (HMT) map employed by the city to target interventions and guide municipal investments based on neighborhood conditions (https://planning. baltimorecity. gov/mapsdata/housing-market-typology). Additionally, we utilize the Environmental Protection Agency's (EPA) National Air Toxic Assessment (2014) (www. epa. gov/ national-air-toxics-assessment) which lists zip-codes where air toxins and emission source types pose respiratory health risks for each state. Zip codes used in this study were highlighted as having poor air quality due primarily to on-road emissions. More information on data is provided in the SI. For our seasonal/whole-city case study (figure  2 ), we extract and aggregate monthly scope 1 CO 2 emissions and uncertainties for the City of Baltimore (∼240 km 2 ) from those reported in Yadav et al (2021).
Yadav et al (2021) used an integrated approach, for the Baltimore/Washington DC region, to discern the impact of COVID-19 on CO 2 emissions. Yadav et al (2021) estimated daily emissions at a native resolution of 2 km 2 /daily for these metropolitan regions (∼18 000 km 2 for both domains). Emissions were estimated for January-May 2020 along with coincident time-periods for 2019 and 2018 (the averages providing baseline months). The integrated approach used high accuracy atmospheric observations from in-situ tower networks (figure 4(a)) and 2015 granular emissions from Vulcan  (Gurney et al 2020a). A summary of the method is provided in the SI. To diagnose cause and effect relationships at the wholecity scale, we compare features in emission proxy data with the extracted and summed CO 2 emission estimates.
Nested national to regional systems could (a) use various levels of data more quickly, (b) enable policy levers at different scales of government, (c) foster private-public relationships, (d) avoid truncation issues associated with transportation (e. g. on-road, aviation, and commercial marine shipping), and (e) allow for an assessment of the progress of action across the county. In this manner, they complement existing tools/methods. To provide the type of emission data shown herein, continued progress is needed. This involves grappling with observational constraints (e. g. in-situ, flask, low-cost, and aircraft GHG measurements, along with satellite retrievals), improving latency issues, enhancing workforce capabilities, tackling costs, and engaging stakeholders. Presently, implementation is prohibitively expensive for most city governments.
Timber production and transformation Useful data for monitoring forest functioning and dynamics can also be collected from the sensors installed on machines deployed for timber production. Most modern harvesters and forwarders can already interact with a digital forest inventory management system, both getting information (e. g. , for organizing harvest operations of marked trees or forwarding piled logs along predefined paths) and feeding new data to the system such as diameters and lengths of the logs produced. The latter data are generally georeferenced, are communicated between computers in forest machines in the StanForD file structure standard  (Arlinger et al. 2010) , and are used for invoicing timber produced and delivered; however, the same data can also return a detailed account on the quantity of the round wood yielded in the harvested plot  (Rossit et al.
Data generated during harvesting operations and integrated with information provided by other types of sensors at tree or plot levels could also be valuable in forest management and monitoring. For this purpose, it is essential to automatically relate single logs and quality data to the original standing tree. Among the available options, radio-frequency identification technology (RFID) seems to be the most effective solution for marking trees in the forest (and processed logs) linking their identifier to the entity in the database of a digital forest inventory management system  (Pichler et al. 2017). Furthermore, RFID tags have been shown to survive the harsh conditions of timber procurement, effectively transferring the information from the standing tree to the products delivered as logs to the sawmills  (Picchi et al.
2019 ) and catchment scales  (Bogena et al. 2010). Recently, an increase in the deployment of inexpensive and stand-alone sensors in ecological research studies is already contributing to databases linked to biogeography and plant traits that can be used to develop valuable neural network models to predict soil conditions  (Hashimoto et al. 2015). Microbial activity and SOM decomposition, as well as root respiration, are affected by soil moisture availability. Soil gas emissions can be measured in the field with IR and laser-based gas analyzers connected to multiple automated dynamic chambers  (Wingate et al. 2010; Courtois et al. 2019 ). These gas analyzers are able to record simultaneous and continuous measurement of up to five greenhouse gases (N 2 O, CH 4 , CO 2 , NH 3 , and H 2 O) as well as their isotopic compositions  (Midwood and Millard 2011).
2018 ). In addition, testing for shifts in peaks and interactions requires data covering a diversity of species from diverse climates  (Wolkovich et al. 2013). Indeed, there is a strong need for observational field data outside the temperate midlatitudes and a need for the measurement of climatic drivers beyond temperature  (Wolkovich et al. 2013 ). In the past, this has been hampered by the need for time-consuming ground-level studies, but advances in remote photo and video technologies can help increase worldwide coverage of observational data. The lack of standardized metadata, field protocols, databases, and baselines currently limits the extensive use of cameras to provide effective measurements of global biodiversity change through a global camera network. Modest investments and collaborative efforts carried out to overcome these limitations could harness the power of remote-camera technology and expand current local-camera and crowdsourcing projects (see section 3) into nationally or internationally coordinated efforts  (Steenweg et al.
2017). Forests support a diverse array of sounds produced by mammals, birds, amphibians, and insects that can be studied within the soundscape ecology. Microphone networks can provide an additional layer of data, complementing that obtained with wildlife camera networks. Acoustical data may help to enable the understanding of coupled nature-human dynamics across different spatial and temporal scales  (Pijanowski et al. 2011 ) by describing how the sounds of a forest change over the season or over the long term in response to forest management and changes in climate. For example, relatively inexpensive, open-source field-deployable microphone recording systems, e. g. , the acoustic detector Audio-Moth  (Hill et al. 2018 ) based on artificial intelligence algorithms, can be deployed in the environment, and recorded data can be analyzed with a range of open-source software.
Forest productivity In past decades, changing climate has been identified as a major driver of shifts in forest productivity  (Beer et al. 2010). Such observations have been made throughout the world by combining dendrochronological, observational, flux, and satellite data  (Babst et al. 2019). Forest growth and productivity can also be characterized using remotely sensed data via physiological measurements, dimension analysis, and relationships of growth to foliage, concentrations, and light  (Coops 2015). Top-of-atmosphere measurements of solar radiance from satellite observations are used to estimate the photosynthetically active radiation (PAR), which, successively, following light use efficiency modelling approaches, is used to estimate gross primary production (GPP) and net primary productivity (NPP) as indicators of forest productivity.