Death by numbers
Statistics (or the lack of it) leads to mismatched budgets, creates inequities
and skews up health priorities
Strategies can evolve only when data is presented into information to forecast scenarios
Does the number of children who die
before their first birthday decrease each year? How many children don’t get a square
meal in India? Do malaria, dengue, cholera, leprosy and other forgettable diseases occur
only in pockets sporadically every other year? Is AIDS undercontrol or is it increasing
everywhere in India uniformly? All these answers are provided by statistics.
Statistics give a snapshot of societal trends, influencing and shaping
public opinion and defining to a very large extent how a government will act. But can
these numbers be trusted? Are they as objective as they are thought to be or can they be
manipulated to serve a particular subjective interest? How can one spot bad statistics?
How often are statistics concocted in the backrooms of hospitals and shady government
offices? When do statistics become unreliable?
The Beginning
In America in late 1800s, immigration and health officials fed the public with imagined
figures and stories of the problem of migrants who brought diseases and infections, crime,
and prostitution and ate into America's prosperity. In order to counter this, analysts
devised scientific methods to count births, deaths, and marriages, which tried to reflect
the true health of the state. Those who conducted such numeric studies — came to be
called statisticians and their "art", statistics. Over time, social research
became more theoretical and more quantitative. As researchers collected and analysed their
data, they began to see patterns and trends. When complexity in technique increased, the
possibility of manipulation increased. Statisticians devised different methods to
interpret the same data differently. Often, methods of assessment produced conflicting
results. These arise because the results obtained from surveys are vastly different or the
tools used to analyse the data produces different results (see box: Divide and rule).
Table: Divide and rule |
The lack of
standard protocols for assessment and bad measuring systems result in manipulated outcomes
Q. Does the mean incidence
of cancers stay unchanged, increase or drop, using four different statistical methods? |
Cancer incidence |
| |
1995 |
1996 |
Cervical |
100 |
200 |
Prostrate |
200 |
100 |
|
|
|
(100+200)/2 (200+100)/2
Mean incidence 150 150
A. Incidence of cancer remains unchanged
Arithmetical average of percentages, in the
first period (or base year)= 100%
| |
1995 |
1996 |
| Cervical |
100% |
200% |
| Prostrate |
100% |
50% |
| Mean incidence |
100% |
125% |
A.There is 25 per cent increase in incidence of cancer |
|
- Arithmetical average of percentages, in the second period
(second year as base year)= 100%
| |
|
|
| Cervical |
50% |
100% |
| Prostrate |
200% |
100% |
| Mean incidence —> |
125% |
100% |
| |
100%—> x = 80% |
A.There is 20 per cent decrease in cancer incidence
Geometrical average of percentages using either period
v(50% ¥ 200%) = v(200% ¥ 50%) = 100%
A. Incidence of cancer remains unchanged |
|
|
Deadly Deception
Poverty
Poverty data globally is extremely poor and unreliable according to a recent paper by
Sanjay Reddy and Thomas Pogge, economists from University of Columbia, New York. They have
criticised the World Bank’s World Development Report, a respected document on poverty
and other social data, because of its use of an arbitrary international poverty line
unrelated to any clear conception of what poverty is. It employs a misleading and
inaccurate measure of purchasing power "equivalence" that creates serious and
irreparable difficulties for international and inter-temporal comparison of income
poverty. It extrapolates incorrectly from limited data and creates an appearance of
precision. The systematic flaws introduced by these three factors lead to a large
understatement of the extent of global income poverty and to correct inference that it has
decline. Says Sanjay Reddy, "Such estimates give a skewed global picture. The
discrepancy of under-estimating poverty is larger for poorer countries like India,
especially for poorer states within India. All reports that have shown that poverty has
declined and the gap between rich and poor has decreased is based on flawed data and need
to be re-examined".1
Eminent economist Peter Svedberg of
Stockholm University and author of Poverty and Undernutrition: Theory, Measurement and
Policy (Oxford University Press, 2002) has severely criticised the many incorrect
measures and yardsticks used by influential organisations like the Food and Agriculture
Organisation. Data from such organisations has influenced food, hunger and nutrition
policies and programmes in cou tries like India.2
Apart from international agencies like the
United Nations and the World Bank, information on poverty in India is also estimated by
national agencies like National Statistical and Survey Organisation (NSSO), Central
Statistical Organisation (CSO) and Ministry of Rural Development, using different
parameters. But here too the numbers and data are often flawed. Poverty is defined by
income earned over a period of time. Most poor people however still subsist by making a
living by extracting food and other items from forests, rivers and other sources. How does
one account for people who live in non-monetised economies? How many such people access
daily needs from these sources? How many of them are malnourished, vulnerable to diseases,
or have access to health services? This essential information is not available to policy
makers because it is never recorded.
A number of projects aimed at targeting
poverty rely on information provided by these agencies. However, if these numbers
themselves do not project a true picture of the nature and extent of poverty, any
intervention that bases its objectives on these numbers is bound to fail.
Malaria
Malaria is a classic example of how the largest disease control programme in the
developing world has been executed for over 40 years in the absence of data and quality
information. According to the data provided by the National Anti Malaria Programme (NAMP),
two to three million cases of malaria are reported every year. The World Health
Organisation’s South East Asia Regional Office (WHO-SEARO) estimates that there are
15 million cases and 19,500 deaths in India annually, five times more than governmental
estimates. This problem of unreliable information about malaria is not restricted to
India. Globally, malaria incidence figures remain speculative. As in India, Thailand and
Brazil have a fairly good surveillance system, yet only half the clinical cases are
reported. A study suggests that figures from Africa represent only about 5 to 10 per cent
of the total prevalence. The WHO estimates that the figures could be greater by as much as
three-fold.
Depending on numbers for the control of
malaria creates other problems too. The Annual Parasite Index (API) is a measure of the
malarial parasite that is present in the bloodstream of a population. It is an indicator
of the persistence of the malarial pathogen in the human blood across seasons. API figures
reflect how many carriers of malaria exist in a community. Once this is determined for a
large population, susceptible population can be identified. However, in case of a large
management unit like a city or a district, if API varies widely in different pockets,
pockets with high API get averaged out with pockets with low API. Areas of potential
outbreak thus remain unidentified.
A large number of malaria cases are not
reported; physicians prescribe anti-malaria regimen without blood tests; and private
practitioners keep no records at all. Hence a large number of cases remain unreported.
Procedures to gather data for grassroots workers are too arduous (see box: Counting
conundrum).
| Counting conundrum |
Cumbersome
reporting procedures lead to misreporting. The operational manual for the malaria action
programme, published by the National Malaria Eradication Programme (NMEP), New Delhi
reveals the complexity of these procedures. It provides broad guidelines for the different
tiers of workers involved in malaria control for collecting data. Different forms need to
be filled in by all the multipurpose workers (MPW), surveillance workers, health
inspectors, technicians, zonal and district malaria officers. The forms cover the numbers
of case, and examinations, family health registers, tour journal, monthly reports,
positive and remedial steps taken, survey reports, spraying report, fever treatment depot
forms etc. A separate set of forms is used for urban areas (which is covered under the
Urban Malaria Scheme). In case of an epidemic, consequent follow-up reports are also sent
in different proformas, making the entire process of reporting very tedious. Most reports
are sent from the state office to the central office every six months. In case of an
outbreak in a remote area such as villages in Assam or Orissa, a report takes anytime
between a week to a fortnight to reach the National Anti Malaria Programme (NAMP) in
Delhi. By this time, the outbreak becomes an epidemic. |
| Source: Directorate of National Malaria Eradication Programme 1995, Operational
Manual for Malaria Action Programme (MAP), Ministry of Health and Family Welfare, New
Delhi |
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