Need for Big Data in Tribal Health with examples of big data , their correct format, need for trained skill personnel , need for right software etc . It also shows how much potential is being missed upon and talks about the need to have detailed ABDM everywhere .

1. The Need for Big Data in
Tribal Health for its better
understanding and
improvement .
Dr Rishabh Kumar Rana
MBBS. , MD. , CCIP.
Fellow Public Health Research
( FPHS India )
Assistant Professor , SNMMCH , Dhanbad.
India

2. Tribal Health in
India: The Big Data
Opportunity
India's tribal communities face unique healthcare challenges. Harnessing
the power of big data can unlock transformative insights to improve tribal
health outcomes and bridge the gap in access to quality care.

3. Defining Big Data: Understanding the
Concept
Volume
Big data refers to the massive and ever-
growing datasets that cannot be managed or
analyzed using traditional data processing
techniques.
Velocity
Big data is characterized by the rapid speed
at which data is generated, collected, and
processed, requiring real-time or near-real-
time analysis.
Variety
Big data encompasses a wide range of data types,
including structured, unstructured, and semi-
structured data from diverse sources, such as
social media, sensors, and transactions.
Veracity
The quality and reliability of big data,
ensuring the data is accurate, relevant, and
trustworthy, is a critical component of
effective data-driven decision-making.
Favaretto M, De Clercq E, Schneble CO, Elger BS. What is your definition of Big Data? Researchers' understanding of the phenomenon of the decade. PLoS One.
2020;15(2):e0228987. Published 2020 Feb 25. doi:10.1371/journal.pone.0228987

4. Why Tribal Health Needs
Big Data in India
India's tribal communities face significant health disparities compared to
the general population. Big data analytics can help bridge this gap by
providing insights into the unique challenges and needs of tribal
populations, enabling targeted interventions and improved healthcare
access.
Leveraging large-scale data on demographics, disease patterns, social
determinants, and access to care can empower policymakers and
healthcare providers to make data-driven decisions that address the root
causes of health inequities in tribal regions.

5. The Census: The Origin of
Big Data
1 Decennial Surveys
The census, a decennial survey conducted in countries around the world, has
been the foundation for gathering large-scale population data for centuries.
2 Technological
Advancements
The increasing use of tabulating machines and later, computers,
revolutionized the way census data was collected, stored, and analyzed,
laying the groundwork for modern big data practices.
3 Data Explosion
As the scope and detail of census questionnaires expanded, the volume of
data generated skyrocketed, highlighting the need for more advanced data
management and processing capabilities.
https://www.ibm.com/history/punched-card-tabulator

6. Census Data and the Rise of IBM and
Computers
In the late 19th century, the U.S. government's
decennial census data posed a major data
processing challenge. This led to the
development of punch card technology by
Herman Hollerith, which laid the foundation for
the modern computer industry.
Hollerith's company eventually became
International Business Machines (IBM), a
pioneer in data processing solutions. The rapid
growth of census data drove the innovation and
adoption of early computing technologies that
transformed business and society.

7. Examples of Big Data Applications in
Healthcare
Predictive
Analytics
Big data can be used
to develop predictive
models that
anticipate healthcare
needs, identify high-
risk patients, and
optimize resource
allocation.
Personalized
Medicine
By analyzing vast
genomic and clinical
data, big data can
enable the
development of
tailored treatments
and therapies for
individual patients.
Remote
Monitoring
Big data from
connected health
devices can provide
continuous, real-time
monitoring of patient
health, leading to
earlier interventions
and improved
outcomes.
Disease
Surveillance
Big data analytics can
identify patterns and
trends in disease
spread, enabling
public health officials
to respond more
effectively to
outbreaks.

8. Components of Big Data: Variety,
Velocity, Volume, and Veracity
1 Variety
Big data encompasses a wide range of
data types, from structured numerical
data to unstructured text, images, and
multimedia content, requiring diverse data
management strategies.
2 Velocity
The speed at which data is generated,
collected, and processed is a crucial
component, as real-time or near-real-time
analysis is often required for timely
decision-making.
3 Volume
The sheer amount of data being
generated, often in the terabytes or
petabytes range, necessitates advanced
storage and processing capabilities to
effectively manage and extract insights.
4 Veracity
The quality and reliability of data are
essential, as big data initiatives must
contend with the challenges of incomplete,
inconsistent, or inaccurate information to
ensure the trustworthiness of insights.

9. SOME of the EXAMPLES of BIG DATA
from Health and challenges
1GB &
……..
100MB
150kb
India
US
UK GBD
CDC
NIH HMIS
NANHES
Census
Data MS EXCEL

10. An example of NANHES data base
in .CSV format
An example of
NANHES data and its
size 541 MB
https://www.kaggle.com/datasets?search=NANHES

11. An example of explaining the data set
https://www.kaggle.com/datasets?search=NANHES

12. Right Software needed to open right
format . .JSON format opened using
SPSS . Not labeled . 46k cases 300
variables . Without labelling

13. Right format to
store the data .
NANHES from CDC
and explaining all
codes
https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overviewlab.aspx?BeginYea
r=2019

14. 1059
variables
Right
Labelled
N=7031624

15. NFHS 5 Data

16. Big Data and ABDM: The Way Forward
for Tribal Health
Unlocking
Tribal Health
Insights
Big data analytics
can uncover
valuable insights
into the unique
health challenges
and needs of tribal
communities in
India. By leveraging
diverse data
sources,
policymakers can
develop targeted
ABDM
Integration
The Ayushman
Bharat Digital
Mission (ABDM)
aims to create a
centralized digital
health
infrastructure.
Integrating big data
capabilities with
ABDM can enhance
data-driven decision
making and improve
access to quality
Predictive
Analytics
Big data-powered
predictive models
can help anticipate
disease outbreaks,
identify high-risk
individuals, and
allocate resources
more efficiently in
tribal regions. This
can lead to
proactive,
personalized
Telemedicine
Expansion
Big data insights can
guide the expansion
of telemedicine
services to remote
tribal areas, bridging
the urban-rural
healthcare gap and
ensuring equitable
access to medical
expertise.

17. Resources Needed for Working with Big
Data
Powerful
Hardware
Robust servers, high-
capacity storage, and
advanced computing
power are essential
to handle the massive
volumes of data
associated with big
data initiatives.
Robust Data
Infrastructure
A scalable and flexible
data architecture,
including data lakes,
data warehouses, and
NoSQL databases, is
crucial for managing
diverse data sources
and formats.
Advanced
Analytics Tools
Specialized software
and tools, such as
machine learning
algorithms, data
visualization
platforms, and
predictive modeling
frameworks, are
needed to extract
Skilled Data
Professionals
A multidisciplinary
team of data
engineers, data
scientists, and
domain experts is
required to effectively
manage, analyze, and
interpret big data for
informed decision-

18. Constraints and Challenges
in Big Data Handling
Implementing big data solutions for tribal health in India faces significant
constraints, such as limited infrastructure, funding, and skilled
personnel. Integrating diverse data sources and ensuring data quality
and privacy are major challenges.
Inadequate internet connectivity and electricity supply in remote
tribal areas hinder real-time data collection and analysis. Budgetary
constraints limit investments in big data technologies and training
programs for healthcare workers.

19. https://orcid.org/0000-0002-3585-5346
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