Statistical Journal of the IAOS最新文献

{"title":"A Review of Recent Nowcasting Methods Compensating for Vital Statistics Reporting Lags.","authors":"Matthew Williams, Taylor Lewis, Sahar Zangeneh, Lauren M Rossen","doi":"10.1177/18747655251388904","DOIUrl":"https://doi.org/10.1177/18747655251388904","url":null,"abstract":"<p><p>Producing timely, accurate, and granular estimates of public health outcomes is essential for effective public health surveillance and mitigation efforts. However, the production of these estimates is potentially delayed by reporting lags, which many times occur naturally in a data collection cycle. In this article, we review published nowcasting methods (i.e., predicting current events while accounting for reporting lags) with a focus on methods relevant for nowcasting vital statistics. Our aim is to classify the underlying modeling strategies and contexts, identify certain advantages and limitations, and present ideas for further research and development using promising statistical modeling techniques that, to the best of our knowledge, have not yet been utilized in any vital statistics nowcasting approaches.</p>","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"41 4","pages":"1291-1299"},"PeriodicalIF":0.6,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13693846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149277420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate change and global health outcome indicators: A scoping review.","authors":"Denise Thomson, Meghan Sebastianski, Megan Nuspl, Megan Green, Vijendra Ingole, Gillian Flower, Antony Brown, Myer Glickman, Richard J Webster","doi":"10.1177/18747655251342655","DOIUrl":"10.1177/18747655251342655","url":null,"abstract":"<p><strong>Background: </strong>The impact of climate change on human health is not evenly distributed and is affected by regional geography and vulnerability of the local population. Official statistics that report these uneven impacts are needed to facilitate strategic planning and resource allocation.</p><p><strong>Purpose: </strong>Identify globally defined indicators of the impacts of climate change on human health to inform the design of official statistics.</p><p><strong>Methods: </strong>We followed recognized methods guidance for scoping reviews.</p><p><strong>Results: </strong>Reviewing 4415 unique records, we extracted 73 unique and 33 repeated indicators from 20 sources. Temperature-related indicators were the most common (27%, 29/106), but many were repeated. Injury or illness indicators were more frequent than mortality indicators, with 59% (43/73) and 37% (27/73) respectively. Following breakdown of the categories into smaller, more specific outcomes, mortality from extreme weather events (n = 10) and illness due to zoonoses/vector-borne diseases (n = 9) were the most prevalent indicators. There was an absence/gap of indicators for five secondary categories.</p><p><strong>Conclusion: </strong>Synthesis of climate-sensitive health indicators is crucial for establishing a cohesive official statistics framework to monitor the health impacts of climate change-related events. The abundance (and gaps) of indicators across categories of health effects aids in prioritization of developing new indicators and improving data availability.</p>","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"41 3","pages":"901-922"},"PeriodicalIF":0.6,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"‘Good data are used data’: Interview with Stefan Schweinfest1","authors":"Pieter Everaers","doi":"10.3233/sji-240050","DOIUrl":"https://doi.org/10.3233/sji-240050","url":null,"abstract":"","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":" 60","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the 4th population census in Ethiopia: Some insights into the feasibility of the Post-Enumeration Survey","authors":"Giancarlo Carbonetti, Paolo Giacomi, Filomena Grassia, Alessandra Nuccitelli","doi":"10.3233/sji-240024","DOIUrl":"https://doi.org/10.3233/sji-240024","url":null,"abstract":"While national registry systems are evolving worldwide and, in some cases, replacing reliance on censuses, in countries where well-established population registers are lacking, the population and housing census remains the primary source of detailed data on the number of people, their spatial distribution, age and gender structure, living conditions, and other key socio-economic characteristics. The quality of the census findings is crucial for several reasons, including building public trust in the national statistical system. In many developing countries, conducting a Post-Enumeration Survey appears to be the only feasible way to evaluate the census results. Indeed, the lack or incompleteness of reliable demographic data from alternative sources precludes the use of other methods. This paper discusses some aspects of the feasibility of a Post-Enumeration Survey in Ethiopia. In particular, the paper reports on the main critical issues that emerged from the pilot surveys carried out in the framework of a cooperation project – funded by the Italian Agency for Development Cooperation – aimed at providing methodological support and technical assistance for the preparation of the 4th Ethiopian Population and Housing Census.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Food price inflation nowcasting and monitoring","authors":"Luís Silva e Silva, Christian A. Mongeau Ospina, Carola Fabi","doi":"10.3233/sji-230083","DOIUrl":"https://doi.org/10.3233/sji-230083","url":null,"abstract":"Rising food prices may rapidly push vulnerable populations into food insecurity, especially in developing economies and in low-income countries, where a substantial share of the financial resources available to the poorest households is spent on food. To capture soaring food prices and help in designing mitigating measures, we developed two complementary products: a nowcasting model that estimates official food consumer price inflation up to the current month and a daily food price monitor that checks whether the growth rate of a few basic food commodities exceeds a statistical threshold. Both products were designed with the consideration that the rapid acquisition of data and the automated extraction of insights are indispensable tools for policymakers, particularly in times of crisis. Our framework is characterized by three key aspects. Firstly, we leverage two non-traditional data sources to emphasize the importance of real-time information: a crowdsourced repository of daily food prices and textual insights obtained from newspapers articles. Secondly, our framework offers a global perspective, encompassing 225 countries and territories, which enables the monitoring of food prices dynamics on a global scale. Thirdly, results are made accessible daily via an intuitive and user-friendly interactive dashboard.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using machine learning algorithms to identify farms on the 2022 Census of Agriculture","authors":"Gavin Corral, Luca Sartore, Katherine Vande Pol, Denise A. Abreu, Linda J Young","doi":"10.3233/sji-230089","DOIUrl":"https://doi.org/10.3233/sji-230089","url":null,"abstract":"As is the case for many National Statistics Institutes, the United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) has observed dwindling survey response rates, and the requests for more information at finer temporal and spatial scales have led to increased response burdens. Non-survey data are becoming increasingly abundant and accessible. Consequently, NASS is exploring the potential to complete some or all of a survey record using non-survey data, which would reduce respondent burden and potentially lead to increased response rates. In this paper, the focus is on a large set of records associated with potential farms, which are operations with undetermined farm status (farm/non-farm) and are referred to here as operations with unknown status (OUS). Although they usually have some agriculture, most OUS records are eventually classified as non-farms. Those OUS that are classified as farms tend to have higher proportions of producers from under-represented groups compared to other records. Determining the probability that an OUS record is a farm is an important step in the imputation process. The OUS records that responded to the 2017 U.S. Census of Agriculture were used to develop models to predict farm status using multiple data sources. Evaluated models include bootstrap random forest (RF), logistic regression (LR), neural network (NN), and support vector machine (SVM). Although the SVM had the best outcomes for three of the five metrics, the sensitivity for identifying farms was the lowest (13.8%). The NN model had a sensitivity of 80.5%, which was substantially higher than the other models, and its specificity of 45.3% was the lowest of all models. Because sensitivity was the primary metric of interest and the NN performed reasonably well on the other metrics, the NN was selected as the preferred model.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAOSTAT Food Value Chain Domain implementation: Input Output modelling and analytical applications","authors":"Silvia Cerilli, Michele Vollaro, Veronica Boero, Olivier Lavagne d’Ortigue, Jing Yi","doi":"10.3233/sji-230079","DOIUrl":"https://doi.org/10.3233/sji-230079","url":null,"abstract":"The recent increasing attention to the economic and policy analysis of the food systems from international fora, public institutions and academia calls for the availability of information and data capable of informing about the interrelations across economic sectors and within value chains. The international policy agenda is pushing for a more effective application of measures at country and regional level in line with the recommendations of the 2030 Agenda and its Sustainable Development Goals, for which more systematic and integrated data about economic, social and environmental impacts of policies are requested. The Food Value Chain Domain recently published in FAOSTAT responds to this call. Its data and information shed light on the distribution of final domestic food expenditures across industries (Agriculture, Food Processing, Wholesale, Retail, Accommodations and Food Services) and primary factors (e.g.: Labour, Gross Operating Surplus) on the relative food value chain. The FAOSTAT Domain offers therefore robust and granular information on both the farm and the post-farm gate component of the Food Value Chain. The applied Global Food Dollar methodology, that FAO is contributing to upscale at global level, is based on Leontief decomposition approach on the Input-Output tables. Moreover, whenever the Input-Output table are not available, it is now possible to impute them from Supply-Use tables by applying a conversion methodology, developed by FAO in compliance with European (EUROSTAT), United Nations (UNSD) and international statistical standards as the System of National Accounts. This allows to extend the analysis to several African, Asian, and Latin American countries that produce on regular basis only Supply and Use Tables, and not Industry by industry Input Output Tables. The potential time and data coverage of the methodology is therefore significantly expanded. The aim of this paper is to describe the conceptual framework of the conversion methodology of Supply-Use Tables into Input-Output Tables of the Global Food Dollar methodology, and the potential implementation scope of these methodologies. Preliminary analytical findings of the applied methodologies are presented as well. The new methods and data presented in this paper, being based on data compliant with the International Statistical Standards, as the System of National Accounts, and therefore comparable across countries, associated to larger data availability, have the potential to effectively support food policies at international, regional and national level, as well as contribute to a decision making in line with the 2030 Agenda.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":" 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141131389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open Science and the impact of Open Access, Open Data, and FAIR publishing principles on data-driven academic research: Towards ever more transparent, accessible, and reproducible academic output?","authors":"Gaby Umbach","doi":"10.3233/sji-240021","DOIUrl":"https://doi.org/10.3233/sji-240021","url":null,"abstract":"Contemporary evidence-informed policy-making (EIPM) and societies require openly accessible high-quality knowledge as input into transparent and accountable decision-making and informed societal action. Open Science1 supports this requirement. As both enablers and logical consequences of the paradigm of Open Science, the ideas of Open Access, Open Data, and FAIR publishing principles revolutionise how academic research needs to be conceptualised, conducted, disseminated, published, and used. This ‘academic openness quartet’ is especially relevant for the ways in which research data are created, annotated, curated, managed, shared, reproduced, (re-)used, and further developed in academia. Greater accessibility of scientific output and scholarly data also aims at increasing the transparency and reproducibility of research results and the quality of research itself. In the applied ‘academic openness quartet’ perspective, they also function as remedies for academic malaises, like missing replicability of results or secrecy around research data. Against this backdrop, the present article offers a conceptual discussion on the four academic openness paradigms, their meanings, interrelations, as well as potential benefits and challenges arising from their application in data-driven research.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"133 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistics for the public good: What it means and why it matters","authors":"Sofi Nickson","doi":"10.3233/sji-230116","DOIUrl":"https://doi.org/10.3233/sji-230116","url":null,"abstract":"Official statistics are widely considered to be public goods, however this paper explores a higher aspiration: that they also serve the public good. To achieve this goal, and provide value to societies worldwide, there is a need for discussion around what it truly means for statistics to serve the public good. This paper shares initial perspectives on the matter from the United Kingdom Office for Statistics Regulation (OSR) before demonstrating how serving the public good fits with customer-centric perspectives on value, and calling for interested parties to join this discussion so that we may work together in service of statistics for a global good.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"83 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140456291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Address matching using machine learning methods: An application to register-based census","authors":"Zahra Rezaei Ghahroodi, Hassan Ranji, Alireza Rezaee","doi":"10.3233/sji-230099","DOIUrl":"https://doi.org/10.3233/sji-230099","url":null,"abstract":"Today, most activities of the statistical offices need to be adapted to the modernization policies of the national statistical system. Therefore, the application of machine learning techniques is mandatory for the main activities of statistical centers. These include important issues such as coding business activities, address matching, prediction of response propensities, and many others. One of the common applications of machine learning methods in official statistics is to match a statistical address to a postal address, in order to establish a link between register-based census and traditional censuses with the aim of providing time series census information. Since there is no unique identifier to directly map the records from different databases, text-based approaches can be applied. In this paper, a novel application of machine learning will be investigated to integrate data sources of governmental records and census, employing text-based learning. Additionally, three new methods of machine learning classification algorithms are proposed. A simulation study has been performed to evaluate the robustness of methods in terms of the degree of duplication and purity of the texts. Due to the limitation of the R programming environment on big data sets, all programming has been successfully implemented on SAS (Statistical analysis system) software.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"314 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140457531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}