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HEALTH STATISTICS

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
  • Using arithmetical average

(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

AIDS
Ever wondered how many AIDS sufferers are there in India? If conservative politicians are to be believed, AIDS epidemic will not spread among the morally upright people of India. If non-governmental organisations (NGOs) and international agencies are to believed then the situation is at crisis point. With both sides throwing unrealistic numbers, millions of dollars have been wasted and precious time lost because no concrete data is available. This is not unique to India. It is fashionable to invest in AIDS for donors, who often clamour to assist countries and institutions in developing countries (see box: Questioning priorities).

Questioning priorities

AIDS is huge political issue, both in India and globally. Donors clamour to take up AIDS programmes in politically important countries and regions. Take the case of funding the AIDS programme in the newly liberated East Timor. Initially many donors expressed interest to set up the country’s AIDS programme. It has sparked a public health debate as many health specialists question whether donors are too focussed on combating HIV/AIDS while neglecting more basic needs in the impoverished country. "It makes much more sense for donors to concentrate on bread and butter issues" such as reducing infant and maternal and expanding immunisation, says one doctor in Dili. According to UN officials, so far only seven known AIDS cases within East Timor’s 779,000 people has been detected. Meanwhile the US Agency for International Development (USAID) and Australian Agency for International Development Aid (AusAID) have developed comprehensive programmes, investing in US $2 million and US $590,000 respectively.

"We know it’s extremely important to pay attention to the epidemic in the early stages" says a USAID spokesman. Adds a spokesman from AusAID "There’s fair chance that the figure of seven is an underestimate." Yet with no reliable data, consultants are already rushing ahead to devise elaborate programmes. "The cart is very much before the horse at this stage," says the Dili doctor.

Source: Anon 2002, Timor’s Health Priorities Questioned, Intelligence, Far Eastern Economic Review, July 25, p 8.

On August 8, 2000 the Ministry of Health and Family Welfare and its nodal agency for managing the AIDS programme — the National AIDS Control Organisation (NACO) — criticised the Joint United Nations Programme on HIV/AIDS (UNAIDS) — for what it says are "exaggerated" figures of HIV-infected people in the country. With increasing pressure from the parliament, the then health minister, C P Thakur accused UN agencies of mis-reporting facts and creating confusion. "I am at a loss to understand how there can be so many different estimates by different UN agencies," an anguished Thakur told reporters at a press conference. Thakur said the NACO, which is supervised by his ministry, generates epidemiological data from field studies and it would be "advisable" for UN agencies to use these figures.

The government's main objection was to the figures in the latest UNAIDS report on the global HIV/AIDS epidemic, which show that 310,000 Indians died of AIDS in India in 1999. However, the report did not explain the source of the figure. Six years earlier, NACO had officially questioned the basis on which UNAIDS calculated that India then had 1.75 million people infected with the AIDS virus. Explaining how UNAIDS arrived at the figure for the number of Indians who died of AIDS in 1999, Gordon Alexander, a senior UNAIDS official in India said. "We arrived at the number of 3.1 million using an internationally accepted model based on experience in various parts of the world." However, because there are huge differences in the assumed parameters, to begin with, the idea of extrapolating and applying different basal conditions in 'universal models' to a 'specific country' is questionable. The numbers that thus would be arrived at would be unreliable. According to Alexander, while there was room for discussion on the figures, the idea was to "emphasise the need for prevention and support and a care system for HIV patients."

Official Indian estimates for the year put the number of AIDS deaths to a modest 11,000, though some experts have questioned the reliability of this figure too. The health minister admitted that these were projections. "We have to develop a proper model for estimation of AIDS deaths based on the number of infections in the country," he said. "It is not always easy to get actual reports on deaths as the cause of death is always recorded as due to opportunistic infections like tuberculosis, meningitis, pneumonia etc."

About the number of estimated cases of AIDS, NACO said that there were as many as 3.5 million reported HIV infection cases in the country. However, NACO's former director Prasada Rao denied that figure and attributed it to a "typographical error." If India did have the hundred of thousands of HIV-infected people as estimated, there should have been many more cases of people afflicted with diseases that mark the final phase of full-blown AIDS. Rao said there was no evidence of this happening anywhere in India.

Some public health groups have an explanation for the confusion over AIDS statistics. According to Purshottaman Mullolli of the Joint Action Council (JAC), the "conflicting statistics" could be "attributed to... a deep conspiracy to inflate figures in order to justify the expending of all too readily available loans from the World Bank." An umbrella group of NGOs working in the areas of human rights and HIV, the JAC has campaigned against a NACO programme that targets so-called high-risk groups, leading to their social ostracisation. "The fact is that far from alleviating problems, a scare is being created in the country," he said. This has led to the import of expensive AIDS-related medical equipment even as basic health services in the country are starved of essential supplies, he added.

Two years after this episode, NACO, UNAIDS, and host of other programmes that run in the country still lack information on the number of people that suffer from AIDS in India. NACO has proposed to strengthen its annual National Sentinel Surveillance Survey by including sexually transmitted disease clinics, antenatal clinics, intravenous drug users sites and homosexual sites.

Earlier this year, the health ministry said there were 3.97 million people infected with the virus that could lead to AIDS. The figures, derived from a report by the ministry’s Sentinel Surveillance Survey, said the spread of the virus had been contained. However a report released by the Futures Group International for USAID says the cumulative new HIV cases for 2000-2025 in India range from 30 million (mild epidemic) to 140 million (severe epidemic).3 Health minister Shatrughan Sinha has been quick to dismiss such figures. Especially with more funds coming in from the Bill and Melinda Gates Foundation giving NGOs more reasons to rev up their strident attack.

Statistics is a weapon in political battles over social problems like AIDS. Advocates take different positions and use numbers to make their points. It is common to hear a debate with contradictory statements – "It’s a big problem!," "No, it’s not!". The debate continues.

Cancer
Yet another compelling evidence of how the government plays with statistics is seen in the data on cancer released by Indian Council for Medical Research (ICMR) in November 2001. The National Cancer Control Programme (NCCP) says that 700,000 new cases of cancer are detected each year and around 300,000 people die. It predicts that more than 1.4 million people will be suffering from cancer by the year 2026, listing environmental conditions as one of the most important reasons of the prevalence of cancer in the present era. In 1965, the K N Rao Cancer Assessment Committee had recommended the establishment of a National Cancer Registry Programme (NRCP), which would provide the mortality and morbidity data and help study the distribution of cancer in different parts of India. It was only in 1972, 17 years after its recommendation that NRCP was set up. There are two main types of cancer registries in the country — the Population Based Cancer Registry (PBCR) that provides information about the disease in an area and the Hospital Based Cancer Registry (HBCR) that provides information about the stage at which the patient enlists in a hospital and the treatment that is administered. It would be safe to assume that the data available on cancer would be updated, concise and precise. Unfortunately that is not the case. The last report that was released from the ICMR's stable was in 1992 and it contained 13-year-old data.

This kind of data is certainly not in a position to aid the government in devising prevention strategies.4 The ICMR cancer registry suggests that cancer is rising in India, but the capacity of health facilities in government hospitals is sufficient to meet the increasing numbers. If this is so, then why is it that the number private cancer hospitals have increased from 11 in 1990 to 42 in 2001?5

Death, birth and other things
Roughly, how many people die in India every year? The Registrar General of India that documents the numbers of deaths in hospitals or those reported to municipal offices provides the only authentic record. Unreported deaths never make it to the final list. Yet figures for death and birth rates are projected and guesstimates and calculated based on decadal averages. The number of actual deaths in epidemics is not known.

Often people themselves over- report deaths and disease. An interesting anecdote has been mentioned in P Sainath’s seminal book, Everybody loves a good drought. In a village called Bansajal, in Sarguja district of Chattisgarh, the total number of deaths from all causes was eight as against the reported figure of 25. On being questioned, the sarpanch (village head) responded, "Unless we have news of people dying like flies, we don’t get a single hand pump". In the absence of actual data, this kind of tactics brings relief but shifts the focus from the real problem.6

System Error
The Ministry of Statistics and Programme Implementation (MOSPI) is responsible for gathering data from the grassroots and compiling them for various ministries. The Central Statistical Organisation (CSO) coordinates and lays down norms and standards for statistics and data collection. It also provides grants to various non-governmental organisations for undertaking research and surveys. The National Statistical Survey Organisation (NSSO) conducts economic census surveys. The NSSO has a specialised Survey Design and Research Division (SDRD) and a Field Operations Division (FOD).7

At the national level, the Central Bureau of Health Intelligence (CBHI), under the Directorate General of Health Services (DGHS), within the Ministry of Health and Family Welfare, is the sole organisation, which deals with the collection, compilation, analysis and dissemination of the information on health conditions in the country. It covers various aspects of health including health status, health resources, utilisation of the health facilities etc. It produces the Health Information of India, a compilation of data from the Registrar Generals Office’s, NSO, NFHS, CSO, and reproductive and child health surveys. Apart from these inputs, it compiles reports that it has received on various diseases and health infrastructure figures from the states. The main problem with the Health Information of India is that all it only presents is data from government hospitals. Private hospitals that cater to 60 per cent of India’s urban demands and about 40 per cent of the rural needs are not covered. Though private hospitals need to report epidemics and outbreaks of at least 16 notifiable diseases under the state law, very few outbreaks are ever reported by private hospitals. These diseases include tuberculosis, cholera, diarrhoea, malaria, rabies and other infectious and communicable diseases. In Mumbai city for example, when leptospirosis broke out in 2000 and 2001, patients went first to private clinics, which had never encountered a case of the disease earlier. As a result they treated patients for malaria, which led to many deaths. Had the municipal body been notified of this, an epidemic could have been prevented.

Clearing the numerical fog

How solid is the statistical support for research reports, news items, or political assertions? Often, not. Here are a few tips on how to cut through the numerical fog. While reading statistics ask questions and look out for conscious biases.

Questions to ask while reading statistics

  • Who is the author? What is the source of the report?
  • What is the basis of this information?
  • What’s missing?
  • Is there a qualitative/quantitative check done?
  • Does the study present any review of regional/global data or similar studies by other organisations?

Things to watch out for while reading statistics

  • Are the questions being asked
  • relevant?
  • Is the source of data reliable?
  • Is all the data reported?
  • Is the data presented in context and interpreted correctly?
  • Are accepted statistical procedures and techniques employed?

Although every ministry and department and their specialised agencies collect data, very little meaningful data on the overall picture exists. The problem in India’s statistical system is a combination of data frauds, poor statistical knowledge and lack of political will to report the true picture. The crux of the problem is that data is called for only when new programmes are being proposed and old one’s renegotiated. Since most programmes like malaria, tuberculosis and AIDS have either been there for too long or are assured sustained funding, there is little or no pressure from decision-makers for acquiring good quality data that would reflect ground realities. Also statisticians have not evolved methods to correlate trends between demographic, social, health and environment variables. Paying attention to details in statistics and understanding actually what is being written could help in understanding them better (see box: Clearing the numerical fog).

In all states, at least 24 registers are maintained by a sub-centre (the smallest health unit in a district). For the grassroots workers the rigmarole of reporting the same data to different authorities in different sections and departments takes its toll. Hence there is a lot of parallel reporting of the same data. Often these grassroots workers are not even informed of project status. In some cases reports continue to come into state offices even though the programme has ended many years ago. In Maharashtra, the state health officials noticed this problem and in the early 1990s devised a comprehensive 16 page format for the sub-centre to report to the district and state office.

Department of Corrections
The problem arises in the way in which programmes and policies are designed in the absence of good data. All health programmes (except infectious epidemics) are target driven. So without knowing how large the problem is, targets and achievement indicators are set. Often a disease may be absent locally but yet work and effort made by the sub-centre and PHC needs to be shown. Take the case of tuberculosis. Policy makers thought that there could be no cooking of data because the treatment protocol involves close monitoring and effort from the health facility. Yet, the health centres need to show a designated number of people in a population. The health workers conduct X-ray examination of lungs, which may or may not be the conclusive tests for TB. In order to meet targets, even non-TB patients are accounted for as TB patients. In the case of leprosy any pale or non-sensitive patch of skin is treated for leprosy. In many states, there is an unwritten rule of not mentioning malaria and meningitis in any report. All these are termed as "fevers". These remain ignored and could arise from any cause like malnutrition, tetanus and heat stress. Health officials can be pulled up for malaria and meningitis deaths but not fever deaths. In Orissa, Chattisgarh and Bihar it is obvious that most "fever" cases are malaria and some meningitis cases. In a recent case in Thane, 20 tribal people died due to malnourishment and the district health authorities conveniently called these deaths as "fever" deaths.8

States too have their limitations. At one end, structural changes and conditionalities imposed by donors like the World Bank has frozen new recruitments and is asking state governments to reduce their large workforce. At another level, programmes have failed because reduced workfroce meant job cuts of grassroots workers which in turn has reduced programme effectiveness. Take the example of the World Bank sponsored Enhanced Malaria Control Programme (EMCP).9 It has suffered in meeting its objectives because in many critical areas, workforce was extremely limited or even absent.

Statistics and data are meaningful only if they are produced and reported, acted upon at the right time. Most data that is reported takes an extremely long time to pass from one desk to any desk, from one office to another, where files make tortuous journeys from district to state to the central offices. Time lag is often several years. How much can one rely upon this data is all a big question mark. Data is presented in the most unintelligent manner, often without any crosschecking and with absolutely no analysis. The Central Bureau of Health Intelligence depends upon the states to give them reports. Not all states send their reports on time. The CBHI seldom crosschecks with state offices, though states often questions district level data in case a certain anomaly is noticed.

All that matters in generating good meaningful data is who is collecting the data, who is using it and how well is it leading into programme strategies. If more immediate data like epidemics, especially those that are expected to occur in a region during a specific season are reported more intensively during a certain period, a better picture on the outbreaks can emerge and more prompt reporting can be made. The problem with outbreaks and epidemics is that they follow the usual course of reporting and lack absolutely any urgency in an emergency situation. In the recent Japanese encephalitis epidemic in Assam a report generated from the local Malaria Research Centre took more than 10 days before any assistance from the Centre could be sent.10 In Bangladesh, the Matlab programme, a community-based maternity-care delivery system, has been conducting long term population based assessments on diarrhoeal diseases found that on an average 3 per thousand people suffer from cholera and in peak season 9 per thousand contract the disease. This would mean that annually at least 300,000 people would suffer from cholera in Bangladesh alone. Yet the annual incidence report for June to August by the Bangladesh health authorities to the Weekly Epidemiological Record and Disease Outbreak News published by the WHO was zero cases of cholera.11,12

Long-term programmes like TB, leprosy and immunisation programmes need to look at trends, while the combination of short-term and long-term assessment need to assess the overall demographic picture. Based on this health facilities and infrastructure and budget need to be designed.

How good data helps

  • Improves efficiency in planning and control
  • Streamlines budget allocation
  • Gives a baseline for future planning
  • Costs only 5-7 percent of project budget
  • Helps prioritise vulnerable sections within the target population
  • Assists in integrating programmes with common goals

Strategies can evolve only when data is presented into information to forecast scenarios, based on which decision can be taken and programmes can be committed. The real shame is that despite famed statisticians and econometricians who set up institutes and chaired committees, they have failed to inspire bureaucrats, make simple formulae and workable methods for grassroots level workers. The Planning Commission, boasting of the finest mathematical minds, today is a organisation bungling outdated data, based on which it designs programmes and policies for ministries. The National Human Development Report-2001 released earlier this year is a prime example of regurgitating old meaningless data and basing policies upon it.13 With little or no data, funding and investment priorities get skewed.

Simply put, what cannot be measured cannot be accounted for, what cannot be accounted cannot be managed, and what is not managed cannot be controlled. Stronger collaborations across ministries can be made to do comprehensive assessments on poverty, land holding, income, food scarcity, nutrition etc., based on which vulnerable areas and populations can be mapped. Several opportunities to re-strategise the Indian health system exist with the government and civil society. The proposed Infectious Diseases Information Surveillance project by the World Bank can revamp data collection and statistics reporting with respect to infectious diseases.14 The AIDS programme is still in its infancy can resolve methods of estimating AIDS in India. Participation from civil society and communities can improve quality of data.

So right from birth, sickness, productivity and growth to death, almost everything is a guesstimate.


References

1. Sanjay Reddy and Thomas Pogge 2002, How Not To Count The Poor, discussion paper, Columbia University, New York, USA, www.socialanalysis.org, as viewed on June 14, 2002.
2
. Peter Svedberg 2000, Hunger in India:Facts and challenges, in Little Magazine, The Gnome, Delhi, special issue on Hunger, Vol 10, No 15.
3
. Nicholas Eberstadt 2002, The future of AIDS, in Foreign Affairs, November/ December.
4
. Vibha Varshney 2001, Faceless Figures, in Down to Earth, Society for Environmental Communications, New Delhi, Vol 10, No 15, p 38.
5
. Anon 2002, Health India Directory, Saffola Healthy Heart Foundation, Saroj Ojha, Delhi.
6
. P Sainath 1998, Everybody Loves a Good Drought, Penguin Books, New Delhi.
7
. Central Statistical Organisation (CSO), www.mospi.nic.in as viewed on August 10, 2001.
8
. Pranay Lal 2001, Virus attack, in Down To Earth, Society for Environmental Communications, New Delhi, Vol 10, No 12, p 26.
9
. The World Bank 1997, Project Appraisal Document on Malaria Control Project, Report No 16571-IN, South Asia Region, The World Bank, New Delhi.
10. Vas Dev Singh 2002, director, Malaria Research Centre, Assam, July 10, personal communication.
11. World Health Organisation 2002, Disease Outbreak News, issues January to September.
12. WHO Weekly Epidemiological Record, World Health Organisation, issues January to September 2002.
13. Planning Commission 2002, National Human Development Report 2001, Government of India, New Delhi.
14. The World Bank 2001, India-Integrated Disease Surveillance Project, South Asia Regional Office, The World Bank, New Delhi and the Ministry of Health & Family Welfare, Government of India, New Delhi, Report No PID10512, July 9.

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