Need for Health Data Standardization in Nutrition Informatics

With numerous organisations working on related topics without any community-wide development guidelines to guarantee interoperability and cohesiveness between nutri-informatics and other biological resources, nutri-informatics suffers from a lack of standardisation. Nutrition is underrepresented in many of the resources for nutri-informatics that are accessible. This might be because nutrition is a broad and diverse area, which makes data modelling more challenging. Through the use of computational nutrition data resources and biomedical ontologies, methods to formalise nutrition research language and connect standardised terminologies across biomedical domains have been started.

Reference: //academic.oup.com/database/article/doi/10.1093/database/baab003/6119904

Data standardization is the process of converting data into a standard format and utilizing it all across. This study examined the selection and adaptation of available food composition to a single database to enable cross-country nutritional intake comparisons. The dietary intake of 9000 participants from eight Latin American countries was investigated using a standard study protocol. Data from 24-h recalls were entered into the Nutrition Data System for Research (NDS-R) program and a food matching standardized procedure was conducted by each country. This study is expected to result in a unique dataset for Latin America, that will allow cross-country comparisons of the regions energy, macro- and micro-nutrient intake. 

//www.ncbi.nlm.nih.gov/pmc/articles/PMC4586568/

I totally agree with the topic. According to the recommendations by different international agencies it is necessary to do assessment of nutritional status and dietary pattern among community. It supports the national policies to acquire improvement in the field of strategy making, implementation and necessary changes.

However, as the inadequate data availability and standardization, it is challenging to understand the nutritional status accurately.

For instance, NFHS data BMI of the adolescent is compared with the standard adult BMI range. Therefore, the data may not be accurate. There are suggestions to compare with growth chart of adolescents in WHO GROWTH CHART 2007 in the national survey. There are some mismatches among standard values by WHO, CDC& IOTF to compare the BMI of 18-19 years in the case of obesity. But WHO standards are found to be more reliable even if both are having similar trends(Bentham et al., 2017).

In various national survey availability of nutritional data is scarce. Even though the anthropometric data is available in NFGS, DLHS, CES, NNMB etc. data on body composition, micro nutrients such as iron Vit. A, C etc., bio markers of NCDs such as BP are not recorded.

Reference:

//www.thelancet.com/journals/lancet/article/PIIS0140-6736(17)32129-3/fulltext

The Academy of Nutrition and Dietetics (AND) defines nutrition informatics as: “the effective retrieval, organization, storage and optimum use of information, data and knowledge for food and nutrition related problem solving and decision making.”

In the earliest years of food and nutrition software, food and nutrition managers may have focused on applying software to automate repetitive processes, save labor, and improve accuracy. Today, the benefits of automation continue. But hospital food service software offers much more.

The ADA lists these areas for application of nutrition informatics:

• Foodservice systems management
• Inventory management systems
• Educational/instructional/data intake
• Communication
• Nutrient analyses
• Nutrition research
• Clinical nutrition care
• Reimbursement for nutritional care

• Standardized Language for nutritional care developed by the American Dietetic Association is an important foundation for common communications among healthcare professionals as well. The ADA offers standardized language used for nutrition diagnoses and nutrition interventions. There are 62 diagnoses, falling into three categories: Intake, Clinical, or Behavioral-Environmental. Interventions may take the form of: Food and/or Nutrient Delivery, Nutrition Education, Nutrition Counseling, or Coordination of Nutrition Care. According to Health Project Partners, “Creating a nationally recognized nomenclature is critical to the processing of patient information from one data point to another and has timely application to nutrition care.”

  The ADA formed a Nutrition Informatics Work Group in 2007, and the Clinical Nutrition Management Dietetic Practice Group of the ADA has a Food and Nutrition Informatics subunit.

  Vision Software is a leader and innovator in healthcare informatics, having pioneered the first real time interface for A/D/T, order entry, laboratory, and pharmacy data in 1987, and the first nutrition risk screening system in 1991. At Vision, we believe in informatics… it’s more than just software™.

  Reference :

• //www.vstech.com/healthcare-initiatives/food-nutrition-informatics/

Nutrition informatics (NI) is the effective retrieval, organization, storage, and optimum use of information, data and knowledge for food-and-nutrition-related problem-solving and decision-making. There is a growing opportunity to facilitate technology-enabled behavioral change interventions to support NI research and practice. This paper highlights the changing landscape of food and nutrition practices in India to prepare a NI workforce that could provide some valuable tools to address the double burden of nutrition. Management and interpretation of data could help clarify the relationships and interrelationships of diet and disease in India on both national and regional levels. Individuals with expertise in food and nutrition may receive training in informatics to develop national informatics systems. NI professionals develop tools and techniques, manage various projects and conduct informatics research. These professionals should be well prepared to work in technological settings and communicate data and information effectively. Opportunities for training in NI are very limited in developing countries. Given the current progress in developing platforms and informatics infrastructure, India could serve as an example to other countries to promote NI to support achieving SDGs and other public health initiatives.

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Nutrition informatics (NI) is the effective retrieval, organization, storage, and optimum use of information, data and knowledge for food-and-nutrition-related problem-solving and decision-making.Management and interpretation of big data could help to clarify the relationships and interrelationships of diet and disease as these relate to the socioeconomic and agricultural changes occurring in India on both national and regional levels. Data mining could yield targeted, evidence-based recommendations and guide interventions.The US National Academy of Sciences has laid out a National Food Data System [51]. They state that features of this system should include comprehensiveness, representativeness, timeliness, openness, flexibility, accuracy, suitability for causal analysis, and fiscal responsibility. The goals of this system would be to have a reliable, multisource, interconnected data system to support research and to guide policy. Data from both national and local government sources, along with those of food industry sources, can be coordinated into a similar structure for India. NI data can promote understanding of the drivers of food choices in various regions, how those choices affect health outcomes, and the likely efficacy of implementing food and nutrition policies.

//www.ncbi.nlm.nih.gov/pmc/articles/PMC8230128/

A nutrition transition is taking place in India as a result of demographic and socioeconomic developments. An analysis of food consumption patterns across the nation revealed a general increase in processed meals, snack foods, and sweets intake. Changes in eating habits, particularly in metropolitan areas, have been linked to an increase in the prevalence of obesity and diabetes nationwide. 

In order to prevent diet-related disorders of both under-nutrition and over-nutrition, it may be helpful to understand the reasons for and effects of these changes. The variety in dietary practices and cooking styles across the nation makes it difficult to give suitable nutrition guidance. Therefore,  health data standardization on Nutrition Informatics may make it easier to understand how diet and disease are related to the socioeconomic and agricultural changes taking place in India.

Reference:

Joshi, A., Gaba, A., Thakur, S., & Grover, A. (2021). Need and Importance of Nutrition Informatics in India: A Perspective. Nutrients, 13(6), 1836. //doi.org/10.3390/nu13061836

@mangalareddy23 Have there been any studies done in the Indian/Asian context?

Researchers use informatics to combine data from electronic health records (EHRs), patient surveys, wearable technology, mobile applications, and other tools to facilitate the inference of optimal health recommendations. Nutri-informatics initiatives frequently fall under the category of clinical nutrition. 

Many clinical settings have concentrated on converting to electronic resources for nutritional data documentation and storage, making static clinical measurements widely accessible. However, the variety of methods for gathering nutritional data within EHRs and the absence of interconnection among them restricts their use in research. Additionally, the availability of nutrition data within EHRs is frequently lacking, making it difficult to assess any potential effects of nutrition on health outcomes. With numerous organizations working on related topics without any community-wide development guidelines to guarantee interoperability and cohesiveness between Nutri-informatics and other biological resources, Nutri-informatics suffers from a lack of standardization. Nutrition is underrepresented in many of the resources for Nutri-informatics that are accessible. This might be because nutrition is a broad and diverse area, which makes data modelling more challenging.

Nutri-informatics investigations will continue to struggle with incompatible data if nutrition data production does not standardize terminology, documentation specifications, or requirements for data reuse. The community must promote standards for nutrition data production, reuse, and publication in order to support Nutri-informatics. Utilizing ontologies is one method for creating the structure and standardization needed in Nutri-informatics.
To achieve broad acceptance and buy-in from stakeholders, sustained collaboration and communication across the nutrition field are required in addition to standard creation. These attempts to promote increased employment of Nutri-informatics resources and community standards would substantially benefit from working groups, instructional workshops, and community decision-making procedures.

References:- 

//www.ncbi.nlm.nih.gov/pmc/articles/PMC4765562/

//academic.oup.com/database/article/doi/10.1093/database/baab003/6119904

The term ‘nutri-informatics’ describes approaches to understand the interactions between an organism and its nutritional environment via bioinformatics-based integration of nutrition study data sets. Nutri-informatics aims to computationally integrate and analyze nutrition study data sets in order to disentangle the interactions between an organism and its nutritional environment. Fueled by an interest in how food, nutrients and nutrition sociology impact health, and a recent push toward ‘big data’, nutri-informatics is essential to incorporating nutrition into computational biomedical sciences. Nutri-informatics suffers from a lack of standardization with a wide array of groups working on similar projects with no community-wide development principles to ensure interoperability and cohesion between nutri-informatics and other biomedical resources.

Opportunities for training in NI are very limited in developing countries. Given the current progress in developing platforms and informatics infrastructure, India could serve as an example to other countries to promote NI to support achieving SDGs and other public health initiatives.

Given the volume and complexity of the information and the urgency of developing solutions to the health problems encountered within a transitioning food environment, NI could provide some valuable tools. Management and interpretation of big data could help to clarify the relationships and interrelationships of diet and disease as these relate to the socioeconomic and agricultural changes occurring in India on both national and regional levels. Data mining could yield targeted, evidence-based recommendations and guide interventions.

References:

//www.researchgate.net/publication/348776713_The_landscape_of_nutri-informatics_A_review_of_current_resources_and_challenges_for_integrative_nutrition_research

//www.ncbi.nlm.nih.gov/pmc/articles/PMC8230128/

Over the last few decades, informatics has developed and is a crucial part of research, utilising the effectiveness and power of computation to enhance the knowledge derived from growing amounts and types of data.Nutrition is still underrepresented in informatics resources, in contrast to other study areas like genetics.One of the most important aspects of human life is nutrition, which has an impact on people in ways that go far beyond merely providing them with nutrients.For instance, body image, interpersonal interactions, and cultural practises are all influenced by nutrition. Despite this, difficulties with nutrition informatics and nutrition data have limited integrated computational investigations.

With numerous organisations working on related topics without any community-wide development guidelines to guarantee interoperability and cohesiveness between nutri-informatics and other biological resources, nutri-informatics suffers from a lack of standardisation. Nutrition is underrepresented in many of the resources for nutri-informatics that are accessible. This might be because nutrition is a broad and diverse area, which makes data modelling more challenging. Through the use of computational nutrition data resources and biomedical ontologies, methods to formalise nutrition research language and connect standardised terminologies across biomedical fields have been started. Numerous nutrition-related ontologies have been started, but they are still in the early stages of development and need more work from nutritionists and biomedical ontologists.

Standardization of data is essential as it makes sure that more accurate data is collected and analyzed.It helps in comparing data around the world and can make research easy. For India, information from sources in the national and municipal governments as well as the food business can be combined into a similar structure with the help of standardisation. Standardization also makes nutrition informatics reliable and adaptable by a number of people.

Standardisation of data in nutrition informatics can be applied in various nutrition informatics platforms in india like
1. Integrated Child Development Services—Common application software (CDS-CAS) introduced by POSHAN Abhiyan
2. The National Institute of Nutrition (NIN)’s Nutrition Atlas
3. Nutrify India Now is a mobile app developed by the NIN, which guides in assessing the nutrients provided by foods consumed.

REFERENCES

1. //www.ncbi.nlm.nih.gov/pmc/articles/PMC8230128/
2. //academic.oup.com/database/article/doi/10.1093/database/baab003/6119904?login=false

Nutrition informatics is the management of the information dietetics professionals need to practice successfully while tailoring recommendations to meet each patient's specific needs.

Data standards are the principal informatics component necessary for information flow through the national health information infrastructure. With common standards, clinical and patient safety systems can share an integrated information infrastructure whereby data are collected and reused for multiple purposes to meet more efficiently the broad scope of data collection and reporting requirements. Common data standards also support effective assimilation of new knowledge into decision support tools, such as an alert of a new drug contraindication, and refinements to the care process. 

Reference:-
//www.ncbi.nlm.nih.gov/books/NBK216088/

//www.usfhealthonline.com/resources/health-informatics/what-do-nutrition-informaticists-do/#:~:text=Nutrition%20informatics%20is%20the%20management,(electronic%20health%20records)%20regulations.

//www.ncbi.nlm.nih.gov/pmc/articles/PMC8230128/

Data standards are the principal informatics component necessary for information flow through the national health information infrastructure.

With common standards, clinical and patient safety systems can share an integrated information infrastructure whereby data are collected and reused for multiple purposes to meet more efficiently the broad scope of data collection and reporting requirements.

Common data standards also support effective assimilation of new knowledge into decision support tools, such as an alert of a new drug contraindication, and refinements to the care process.

This chapter provides both a short overview introducing data standards to the lay reader and a more technical review of the specific data standards required for the informatics-oriented professional.

the lack of common data standards has prevented information sharing between commercial clinical laboratories and health care facilities, between pharmacies and health care providers regarding prescriptions, and between health care organizations and payers for reimbursement

The medical device industry is well versed in developing user interfaces that make devices safer, more effective, and easier to use by employing a voluntary standard for human factors design established by the Association for the Advancement of Medical Instrumentation (AAMI) and approved by the American National Standards Institute (ANSI) (Association for the Advancement of Medical Instrumentation

application interface tools, and standardization in the representation of medical literature should allow such requests and retrievals to be completed through a fully automated system so that reliance on a librarian is not necessary, and information access is available to clinicians around the clock

Hence it is crucial for data to be standardized.

Source 

Reference-

//www.ncbi.nlm.nih.gov/books/NBK216088/

India is undergoing a nutrition transition, driven both by demographic and socioeconomic changes. Understanding the causes and consequences of these changes could be important in preventing diet-related diseases of both under and over-nutrition. Assessment of food consumption patterns around the country found an overall increase in processed foods and snack foods, with increases in consumption of sweets varied by region. Changes from traditional dietary patterns have been associated with increasing the incidence of obesity and diabetes throughout the country, especially in urban areas. The development of appropriate nutrition guidance remains challenging. One reason for this is the diversity of dietary patterns and cooking methods regionally throughout the country, making identifying an “average” Indian diet virtually impossible. An analysis of dietary patterns found twenty-nine distinct diet patterns, each one differently related to the range of nutrition problems experienced in India.

References :

//www.ncbi.nlm.nih.gov/pmc/articles/PMC8230128/