The aim of our project is to examine and explore the world of children and adolescents malnutrition through the analysis of Open Data. Everything is pursued not only by studying the direct consequences of malnutrition, such as eating disorders and other health diseases but also by analyzing the economic, cultural, and geographical context. The desired result is to get the whole picture that can grasp all the aspects of such a complex and delicate theme.
When one starts approaching the studies concerning eating behavior of children and adolescents, the multifactoriality that determines the "eating experience" must be taken into consideration. In fact, for children, this experience represents not only a mere “way of sustenance” but an opportunity for learning and discovering their own bodies and routines. In young adults, the eating experience becomes an internalized behavior that has a decisive impact on the quality of life. Healthy eating habits are therefore essential for the physical growth and health of the child, but also for psychological and emotional development. The experience is influenced by factors like culture, health status and temperament. A fundamental part in the process of shaping one’s eating behavior is the social component of feeding, which is first acquired through direct experience within the family context. Therefore, it depends on the eating behaviors of family members and on their culture.
A direct consequence of unhealthy eating habits acquired in the family context may translate in a multiplicity of facets that a problem related to eating behavior can have:
- Overeating.
- Poor eating or not gaining sufficient weight.
- Feeding behavior problems.
- Unusual choices. Pica, or the ingestion of non-food substances
- Unhealthy food choices
Besides, an optimal diet for children and young adults should include regular eating patterns and a variety of foods, able to guarantee a good mood and good behavior to enhance learning and other fundamental social activities. Overall, an unhealthy diet seems to depend on several factors such as poverty, urbanization, climate change, and poor food choices.
In our modern society, characterized by technological advancement, the problem is no longer just providing access to food for all children or providing access to an adequate amount of food, but providing access to both quality food and qualitative nutrition education that allows one to maintain a healthy dietary pattern over time. Adolescents are the ones particularly affected by inappropriate marketing and advertising campaigns, especially young people who live in highly urbanized areas and therefore have easier access to ultra-processed foods, highly sweetened beverages, and fast food. An interesting finding in this research regards the spread of this issue in children and adolescents who live in marginal regions and in conditions of poverty. The consequence is the global increase of obesity(overweight). The proportion of overweight children between the ages of 5 and 19 has doubled from one in 10 to one in 5 over a period of time from 2000 to 2016. This trend is also confirmed in high-income countries. E.g., in the United Kingdom, childhood obesity is twice as high in poor areas as in richer areas. Another factor that has a dramatic impact on the global percentage of malnutrition is the one linked to the climate crisis. In particular, the areas affected by drought see a dramatic decrease in both quantity and quality of the available food.
In the attempt to provide a solution to the crisis caused by the spread of malnutrition, Unicef is gathering strength in promoting food education policies through cooperation between the public and private sector. The idea is to rely on schools and parents to monitor children's eating behavior, but also to adopt socio-economic measures that can widely promote healthy nutritional practices in families. Among the most effective ones, there are sugar taxes, whose aim is to discourage the excessive use of sugar. Additionally, food suppliers are encouraged to apply sustainable policies to their goods and to deliver them with exhaustive and easily readable labels. In conclusion, Unicef is fighting to obtain a radical change in how children and teenagers’ diet is perceived, aware of the seriousness and importance of this sensible yet underestimated topic.
To deepen the medical and sanitary aspect of the general malnutrition problem, we put our efforts in our project in showing the diseases that are a direct consequence of wrong nutritional choices/patterns. Scientific evidence proves that Children that follow unhealthy eating behaviors, can develop serious chronic diseases. The analysis of the link between these diseases and bad nutrition was carried out to highlight the seriousness of the problem and to raise awareness about it. Here the listing of the recurrent health issues related to malnutrition, based on scientific researches:
-Diabetes mellitus type II
-Iron deficiency
-Iodine deficiency
-Protein energy malnutrition
-Liver cancer
-Asthma
-Vitamin A deficiency
-Cardiovascular diseases
To see the entire explanatory table about the general analysis click here.
we used different datasets from different sources to fully cover the several aspects that malnutrition among children implies:
World Bank Group:
The World Bank strives to enhance public access to and use of data that it collects and publishes. The accurate and reliable data came from recognized international sources and are organized in datasets listed in The World Bank Data Catalog (the “Datasets”). The Datasets are collections of data, managed by The World Bank and provided in different machine-readable formats. They can be downloaded and reused. Data are fully described, and a large list of indicators and their description is provided. It is possible to search for relevant data using keywords such as indicators country names etc. Metadata and resources for each dataset can be found on its dataset page. For some tabular resources, an API is also provided, allowing programmatic access to the underlying data.
IHME:
IHME states to provide datasets that are relevant and scientifically valid evidence to improve health policy and practice, guaranteeing that datasets are unimpeded by political, financial, or other types of interference. Their data, collected worldwide, are the result of rigorous measurements and adhere to the principles of scientific inquiry. They try to use data as recent as possible and they make them open, transparent, and accessible to a wide as possible audience. All global, regional, and national datasets are freely available and downloadable.
World Health Organization:
for what concerns the overview of the dataset, the data are fully described and there are a large number of indicators to choose among, moreover, each dataset is accompanied by its corresponding metadata. Data are accurate and reliable since they come from population-based sources (household surveys, civil registration systems of vital events) and institution-based sources (administrative and operational activities of institutions, such as health facilities). Availability and quality of data are guaranteed.
UNICEF:
UNICEF’s Data & Analytics (D&A) team is the global go-to for data on children. It leads the collection, validation, analysis, use, and communication of the most statistically sound, internationally comparable data on the situation of children and women around the world. D&A upholds the quality, integrity, and organization of these data and makes them accessible as a global public good on the data.unicef.org website. The platform gives the possibility to query and download your own datasets through the selection of indicators, filters, and visualization models. A list of all indicators is provided and organized by the thematic sector. The datasets obtained by download are free to reuse.
FAO:
FAO makes a significant contribution to global discussion related to food and agricultural statistics through its participation in international and regional statistical fora. Their statistics data tackle issues related to food and agriculture, but also hunger, malnutrition and rural poverty. The Organization adopts Corporate Statistical Standards that set out the principles and recommended best practices to ensure that FAO statistical production processes and statistical outputs are of the highest possible quality. In 2014 it was also adopted the Statistics Quality Assurance Framework(SQAF) based on the Fundamental Prinicples of Official Statistics and the Principles Governing International Statistical Activities (CCSA), which guide other national and international organizations, like Eurostat. Staticians adopt high-standard professional values and ethics in statistics.
Our World in Data:
Our world in data is an Open Access and Open source resource. The goal of the project is to make the knowledge on the big problems accessible and understandable. As stated on their homepage, Our World in Data is about Research and data to make progress against the world’s largest problems. They have a major interest in: poverty, disease, hunger, climate change, war, existential risks, and inequality. It collects research articles and visualizations licensed under CC BY, data is available for download. Furthermore, all code they write is open-sourced under the MIT license and can be found on GitHub.
UN IGME:
The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) was formed in 2004 to share data on child mortality, improve methods for child mortality estimation, report on progress towards child survival goals, and enhance country capacity to produce timely and properly assessed estimates of child mortality. UN IGME’s independent Technical Advisory Group (TAG), comprised of leading academic scholars and independent experts in demography and biostatistics, provides guidance on estimation methods, technical issues, and strategies for data analysis and data quality assessment. UN IGME updates its child mortality estimates annually after reviewing newly available data and assessing data quality. The web portal contains the latest UN IGME estimates of child mortality at the country, regional and global levels, and the data used to derive them.
World Bank Group:
The license used is Creative Commons Attribution 4.0 International License (CC BY 4.0). The site includes a “Legal” section that provides all the legal regulations and privacy information. The Bank maintains appropriate technical and organizational safeguards against unauthorized processing of personal data and against accidental loss, destruction, or damage.
IHME:
Data made available for download on IHME website can be used, shared, modified, or built upon by non-commercial users in accordance with the IHME FREE-OF-CHARGE NON-COMMERCIAL USER AGREEMENT. Hereby users are granted of a free-of-charge, non-exclusive, royalty-free license to download the IHME Data and the Data Sets to create analyses using the IHME Data and Data Sets whether alone or in combination with other data (“Results”), for non-commercial purposes only, and to use such Results for internal research purposes and for publication in articles, posters, dissertations, briefs, reports, websites, or similar types.
World Health Organization:
Data published by WHO are distributed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Intergovernmental Organization (CC BY-NC-SA 3.0 IGO) license. The CC BY-NC-SA 3.0 IGO license allows users to freely copy, reproduce, reprint, distribute, translate and adapt the work for non-commercial purposes. Some other material including reproduction and translation of figures, tables, maps, photos; reprint and translation of complete works and licensing of materials or other technical information in electronic database products and services can be used through request.
UNICEF:
the content on the site is licensed under a Creative Commons Attribution-NonCommercial 3.0 IGO license, that allows users to freely copy, reproduce, reprint, distribute, translate, and adapt the work for non-commercial purposes.
FAO:
The website provides datasets and databases from different platforms. Specific statistical databases are covered by the Open Data Licensing Policy, and governed by the Statistical Databases Terms of Use. All other content on the website (expect where otherwise indicated) can be copied, printed, downloaded for private study, research, and teaching purposes, and for use in non-commercial products or services. The organization provides the email address copyright@fao.org to submit the request for translation and adaptation rights and for resale and other commercial use rights, and to ask about licenses of the content on the website. However, the license used is not explicitly mentioned.
Our World in Data:
All the visualizations and texts present on the website are licensed under CC BY 4.0, all the materials can be transformed and adapted for any purpose. All data are available to download. Furthermore, all the code is open-sourced under the MIT license and can be found on GitHub here: https://github.com/owid.
UN IGME:
The license used is not explicitly mentioned. The website provides a “legal” page to check the legal aspects, but it leads to a page error. However when selecting and analyzing the dataset, the user can download it in different formats.
World Bank Group:
One of the priorities that guide the World Bank Group is to work with countries to end poverty and boost prosperity for the poorest people, helping create sustainable economic growth and generate greater equity. Data are human-centered, equality and transparency are guaranteed.
IHME:
Ethical behavior is the core of IHME, they commit to working to produce evidence of discrimination and injustices and other critical public health issues that demand an urgent response. They also claim to undertake research to fight inequity, address racial disparities and they aim at making their organization more diverse and inclusive.
World Health Organization:
The WHO Office of Compliance, Risk Management and Ethics (CRE) promotes transparency and management of corporate-level risk, within the framework of WHO’s ethical principles. To this end, CRE promotes the practice of the ethical principles derived from the international civil service standards of conduct for all WHO staff and associated personnel. Great stress is given to contractors and collaborators to not perpetuate sexual exploitation and abuse of any kind. CRE provides clear and action-oriented advice in a secure and confidential environment where individuals can freely consult on ethical issues.
UNICEF:
The dataset is in accordance with the highest standards of integrity, including honesty, truthfulness, and fairness. UNICEF aims to strengthen countries’ capacities to make informed decisions and lead initiatives based on the best available data. Key to this role is supporting countries’ collection of data on children and women and the Sustainable Development Goals through UNICEF’s global household survey program, the Multiple Indicator Cluster Surveys (MICS). They give great importance to ethics: “We believe that smart demand, supply and use of data drives better results for children. When the right data are in the right hands at the right time, decisions can be better informed, more equitable, and more likely to protect children’s rights.”.
FAO:
The Food and Agriculture Organization (FAO) is a specialized agency of the United Nations that leads international efforts to defeat hunger and poverty, ethical values are at the core of the FAO’s statistical work. Fao is dedicated to collecting, analyzing, interpreting and disseminating food and agriculture statistics that are relevant for decision-making. Their aim is to develop and implement methodologies and standards to assist countries and to help them in better planning their investments. Moreover all FAO employees who have access to or are associated with the processing of personal data are obliged to respect the confidentiality of official business matters, including personal data..
Our World in Data:
The goal of the project, as stated by the creators is “to make the knowledge on the big problems accessible and understandable”. For this reason, they want to provide full accessibility to important knowledge, focusing on the main problems the world is facing. They believe that a better use of the research work we have is possible, by exploiting the digital medium they want to share knowledge to help solving major problems.
UN IGME:
The UN IGME is led by the United Nations Children’s Fund (UNICEF) and includes the World Health Organization (WHO), the World Bank Group and the United Nations Population Division of the Department of Economic and Social Affairs as full members. So the organization share the same aim of UNICEF in supporting countries and to strengthen their capacities to make informed decisions.
Mashup and output datasets
In order to manage the mash-up of different datasets we followed the Guidelines on FAIR Data Management in Horizon 2020. In accordance with these guidelines, we pursued the objective to make our research data findable, accessible, interoperable and re-usable (FAIR).
Findable:
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
F1. (Meta)data are assigned a unique identifier: both the data we retrieved in the original datasets, the mashed up data and the metadata we created according to the DCAT-AP are compliant with this point, presenting URI.
F2. Data are described with rich metadata: we associated a rich amount of metadata compliant with the DCAT-AP specification, including not only all the mandatory classes with their respective mandatory properties but also some recommended and optional properties that were useful for our data.
F3. Metadata clearly and explicitly include the identifier of the data they describe: for each dataset that is part of a catalogue and for our own dataset we associated to the metadata a unique identifier of the data described by means of the DCAT-AP optional property for datasets dct:identifier.
Accessible:
Once the user finds the required data, she/he needs to know how can they be accessed.
A1. (Meta)data are retrievable by their identifier using a standardised communications protocol: all the data we collected and mashed up and the relative metadata are retrievable through the HTTP or its extension HTTPS. Moreover, we provided also an explicit and clear contact protocol in the metadata by means of the names and emails of the data and metadata providers.
Interoperable:
Data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation: we used JSON for the representation of the mashed up data and RDF with the XML syntax to describe and structure the metadata.
I2. (Meta)data use vocabularies that follow FAIR principles: the annotation format we used allow to use machine-readable terms from any controlled vocabulary. We used the ISO standard vocabulary to represent nations, the Linked Open Data vocabulary specification called DCAT-AP. These vocabularies are documented and resolvable using globally unique and persistent identifiers.
I3. (Meta)data include qualified references to other (meta)data: JSON and the RDF schema account for the data exchange and cross reference among metadata respectively.
Reusable:
the ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.
R1. Meta(data) are richly described with a plurality of accurate and relevant attributes: our data and metadata are described through a rich and vary series of labels including the date of collection and modification of the data, the licence, the publisher, the creator, their content.
R1.1. (Meta)data are released with a clear and accessible data usage license: all data we used were released with the specification of the usage license except for those from the UNHCR and UCDP. License in specified for the dataset and respective metadata we created (Creative Common License CC BY 4.0).
R1.2. (Meta)data are associated with detailed provenance: our project includes information about the provenance of data in a machine-readable format in the metadata codification. The website presents also a description of the workflow that led to your data.
R1.3. (Meta)data meet domain-relevant community standards: we used the ISO standard for geographic information.
The principles mentioned above include three types of entities: data, metadata and infrastructure. Given the analysis, we can state that our research data are almost 100% compliant with the FAIR principles, with the few exceptions due to the lack of license specification.
Sustainability:
The NutriData (SE abbiamo il dataset unico finale) dataset contains datasets that derive from different sources, concerning all the factors involved in the topic about children malnutrition: food supply adequacy, mortality, diseases and other physical pathologies, wealthiness of countries. The project has been created for the final examination for the Open Access and Digital Ethics course of the Master Degree in Digital Humanities and Digital Knowledge at the University of Bologna: the dataset is not actively maintained, while the datasets used for this project are currently maintained by the relative institutions or organisations. NutriData is distributed under Creative Commons Attribution 4.0 International License (CC BY-SA 4.0)
In order to achieve a wider understanding of the data, we created six different interactive visualizations:
a colorpleth world map, centered on Europe, showing the percentage of food adequacy supply during a two year time period, from 2018 to 2020. a line chart showing daily per capita supply of calories from 1960 to 2013, with the possibility of focusing on the nation of interest. a bubble map showing the distribution of the five different types of malnutrition in children and young adults around the world, from 1986 to 2019; with the possibility to choose malnutrition type and year. a time series barchart, showing the percentage of five pathologies strictly related to malnutritions until 2019, with the possibility of focusing on the nation of interest. two line charts: one shows the mortality rate and life expectancy from 1990 to 2019, while the other shows the gross domestic product from 1960 to 2020 with the possibility of choosing the nation of interest. All the graphs were realized using Amcharts 5 library for data visualization, since it is very flexible and natively integrates with Javascript.
In order to offer better reusable and interoperable data, we provided them with their metadata, following the DCAT_AP version 2.0.0 documentation. The RDF assertion for the metadata, that follows the Turtle serialization, has been released and can be downloaded and consulted.
The NutriData project is the result of an extensive research over all the factors which revolve around children and young adults malnutrition. We performed a multi-level analysis starting from the contextual data regarding global food supplies, the next step was to focus on the data regarding the distribution of the different types of malnutrition and the related health conditions over the world, at the end we extended the research with contextual information, in order to have extra details to enrich our point of view. To highlight the different trends regarding obesity and malnutrition, we decided not to focus on a single geographical area in this way we were able to see through the visualizations the direct correlation between contextual information such as gdp and the different types of malnutrition. This project led us to believe that (heterogenous, reliable, accessible) data are fundamental to better understand a multifactorial and complex topic like this. Hopefully this project will help in raising awareness, and educating the younger generations to a more ethical approach to food and nutrition.