Events & Issues
New
Delhi, 14 October 2021
Rule of Data
A GLOBAL REVOLUTION
By Dr S. Saraswathi
(Former Director,
ICSSR, New Delhi)
Union
Health Ministry has no information on vaccine doses procured and administered
by private hospitals. Despite reservation of 25% of vaccine allocation to
private sector and freedom to purchase vaccine directly from manufacturers,
only 6% of the doses administered were in private hospitals. It shows the
urgent need to improve our data base for better results.
We are
living in the age of information that is called data by researchers from which
inferences can be drawn and conclusions reached. The quality, quantum, and
coverage of data determine how effectively
the decisions arrived at will fulfil our aims and objectives. The value of data
is in their use. Every organ, agency, or institution in a democratic set up is ultimately
accountable to the public for every decision and action. Hence, it is virtually
a global revolution towards the rule of data or Data Raj.
Covid-19
pandemic has taught us the importance of recording correct and complete facts
pertaining to prevalence and control of the disease. This lesson on data
quality, as the key priority, is a by-product that has awakened us to realise
that much of the work in epidemic control centres is around data generation, retrieval,
and updating. Disparities in the assessment
of Covid-19 cases, availability of vaccines and medicines, number
vaccinated, and number of deaths by different agencies raise the question of
reliability of available data. Each State has its own mechanism for reporting
and its quality is not comparable.
Data
quality, it is found in the experience of users, has six dimensions - accuracy, completeness, consistency,
timeliness, validity, and uniqueness. Weakness of available data even in one of
these aspects will show on the results and make them questionable. But, none of
these can be guaranteed by any system of data collection. Our aim itself has to
be lowered and kept as elimination of errors, omissions, bias, inconsistencies,
delay, and doubts in data. Properties or criteria used must be common in the
coverage of different areas or subjects within an investigation.
Public
healthcare has presently become the context for widespread discussion on data
quality. It is for this reason that the WHO extends
its support to member-States to strengthen their capacity to collect, compile,
manage, analyse, and use health data accumulated from total population-based surveys like
household enumeration and
surveys, civil registration systems of vital events like birth and death,
and administrative and medical
activities of health centres at different levels, total usage of vaccines and
medicines, and number of available medical, paramedical and health work force.
Comparisons
are possible between regions, countries, States/provinces, districts, zones,
wards and so on, on the basis of available data. These help concerned
authorities to make appropriate changes and adjustments in the methods adopted
for containing the epidemic.
Estimates
of death due to Covid-19 by different agencies vary widely. There is gross
under-estimation in many nations causing under-preparation for fresh
challenges. Given the magnitude of the problem, accuracy of data on Covid-19 death
is of paramount importance, but there are still controversies regarding cause
of death. Sense of shame in reporting death, and a tendency to claim undue
credit for controlling the pandemic and setting models may have led to
under-counting and manipulating death data.
Regular
and reliable data on existing health facilities are central to quality and
availability of health services. Health sector planning is impossible without
data. But, public perception that associates sickness only with doctors,
hospitals, and medicines has taken a long time to realise that the patient
becomes a patient in an environment subject to several socio-economic-cultural
influences.
Government
of India has released the National Guidelines for Data Quality in Surveys to
provide comprehensive guiding principles
and best practices for mitigating
errors and biases that may occur during
designing the project, conducting the surveys and
analysing the responses.
The initiatives for the guidelines came from the National Data Quality
Forum (NDQF) housed at the ICMR. The guidelines are specifically meant for
demographic, health and nutrition surveys for advanced data quality monitoring
and data analysis and to improve the capacity of data collection agencies. Machine learning techniques are also imparted
to guide in the application of guidelines.
Data
Raj has come to stay in all policy-making exercises, budgeting, fund distribution,
and evaluations besides health and diseases.
Another
area clamouring for data is the “Quota Policy”, which links opportunities for
education and employment with the numerical strength of castes and communities.
The demand for caste data, knowing well the administrative difficulties and complicated nature of caste phenomenon itself to yield a true
picture of caste divisions, and the adverse impact of such a census on
social unity is a typical instance of misapplication of data rule for
political advantage.
Agricultural
progress depends much on data base and its availability and understanding by
those in this sector. Data based decisions at farm level can enhance resource
utilisation and conservation practices which will improve production and lessen
costs. Expanding this to regional level will help formulation of long-term policies. Union Minister for Agriculture
and Farmers’ Welfare has been emphasising the importance of preparing farmers’
database. Since agriculture is not an organised sector, collection of data from
individual land owners involves tremendous effort. But, the outcome will be as
much valuable.
Quality
Control (QC) of data is given primary importance in the US. It refers to
application of methods or processes that determine whether the data collected meet
the requirements for reaching the set goals and meeting the quality criteria
prescribed. It is necessary in any planning anywhere.
Censuses,
Sample Surveys, and administrative data are the three main sources of data in
modern countries. In India, most
important government sources of data include the Reserve Bank of India, Ministry of Statistics & Programme
Implementation, Survey of India, India Weather
Data, and National Portal of India. Census, National Sample Survey,
National Rural Health Survey, Election Commission, National Crime Research Bureau
are producing enormous and continuous data. That they provide basis for short
and long term planning and decision-making on related issues for the government
needs no mention. The National Data Bank of Socio-Religious Categories has also
been developed.
The
first major data treasury built on scientific principles is the Survey of India
set up in 1767 for “exploring the unknown territory” by English expansionists.
It has grown as a rich resource for geospatial data and the nation’s principal
mapping agency. Countrywide census operation began in 1871. Along with this,
ethnographic surveys have been conducted to study the population.
Data
may be both quantitative and qualitative in the form of statistics or documents
and records. Court records and judgements, administrative reports and budget
papers, proceedings of parliament and legislatures, reports of events and
speeches and many such records provide immense data. These have to be authentic
and unbiased versions to be usable for policy-making or research.
The world
is going through a data revolution touching creation, collection, preservation,
and use in every sphere. Just as authentic data lead to improvement and
progress, circulation of fake and false data will lead us to decline and fall.
---INFA
(Copyright,
India News & Feature Alliance)
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