14.74 Lecture 4: Health Esther Duflo February 17, 2004

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1 14.74 Lecture 4: Health Esther Duflo February 17, The burden of disease The burden of disease is large, and very unequally distributed: See table 2 in page 1 of the handout: In 1990, 50 million people died. 40 million of them in the developing world. Ischaemic heart disease is the leading cause of death worldwide In developing countries, 16.3 million deaths were due to infectious diseases such as: perinatal conditions: Lung infection (pneumonia, TB) : Diarrhoeal diseases: The vast majority of these deaths are preventable. Infectious diseases are the leading causes of year of life lost: All deaths are not comparable. The death of a child deprives her and the world of a very long life. The death of an 100 year old man with grandchildren and greatgrandchildren may involve the same sense of loss, but we have a sense that he lived a full life. We would like to weight these death differently. What is a natural metric? How do we compute year of life lost (YLL)? We need to estimate how long someone should have lived. The key questions to answer: Do we assume that everybody should have lived the same length of life? Or should it vary by age, gender, socioeconomic class, ethnicity? What are the pro and cons? What value does it reflect? The global burden of disease project (WHO and Harvard School of Public Health) assumes that an individual would have lived the life of a Japanese person of the same gender (82.5 years for women, 80 years for men). 1

3 million deaths were due to infectious diseases such as: perinatal conditions: Lung infection (pneumonia, TB) : Diarrhoeal diseases: The vast majority of these deaths are preventable.

2 What are the biggest culprits for the lost of year of life (figure 10). What are the important changes in the rankings and where do they come from? 2 The relationships between health and wealth: macropicture The global burden of disease goes further, and computes DALY : disability adjusted life years which takes into account the loss in the value of life coming from a disability. This requires one to make some choices which may be contested (ranking and weighting of disabilities), but allows a fuller picture of the burden of disease. Figure 12: The regional inequities in the burden of disease. India and SubSaharan Africa bear 40% of the burden of disease, even though they comprise only 26% of the world population. Lost DALYs mean that people cannot work and be productive (remember Abu). Can country poverty therefore be attributed in part to bad health? Economist Jeffrey Sachs argues that it is the case. For the case of malaria, he observes that countries with endemic malaria are poorer, and also grow less fast (0.4% per year on average, against 2.3% in nonmalaria endemic counties). After controlling in a regression for education, level of income, macroeconomic policy, etc, he finds that countries with incidence of malaria grow 1% more slowly. Accordingtothisview, weneedtospendonhealth care to improve health, and improvement in income will follow. The commission on macroeconomics and health, which Jeffrey Sachs headed, estimated that about 8 million lives a year could be saved by increasing annual global health investments by US $66 billion over the next ten plus years. Such an increase in public health investment could save millions of lives and would generate US $360 billion in annual economic benefits. What are the potential difficulties with this reasoning? 2

value of life coming from a disability. This requires one to make some choices which may be contested (ranking and weighting of disabilities), but allows a fuller picture of the burden of disease.

3 3 The (complex) relationship between health and wealth. The view from Rajasthan The question of what determines health and what health determines seems too large to be investigated only with macro data... The paper that we circulated before the class is the result of a twoyear project to learn more, in more detail. This project was lead by Professor Banerjee and myself, and Professor Deaton, from Princeton University. We worked with a local NGO, Seva Mandir, who was keen to find out what was the most important thing for THEM to do. What data was collected: Why do you think we collected data on each of these aspects? What are the big lessons to take away from this data? Is there anything you think we could look at in the data, but you did not find in the paper (perhaps I can answer now)? 3

.. The paper that we circulated before the class is the result of a twoyear project to learn more, in more detail.

4 Is there anything else you would like to know, but for which we would need more data? Seva Mandir asked us to do this survey because they wanted to know what to do to improve health status in the area. On the basis of this discussion, what would you recommend? What makesyouthinktheywillbeeffective? Let s take all of these suggestions in turn: Are we sure they would work? What doubts can we have about each of them? 4

in the area. On the basis of this discussion, what would you recommend?

5 4 Finding out what works: the value of experiments This conversation should have convinced you that, using only data from observations, we can form intelligent hypotheses, but not resolve them. Before spending all of our money on something, how do we find out whether or not it will work? Why do we have problems teasing out causal relationship in reallife data? To address these, we need to compare comparable people, some of whom were exposed to a particular policy and some of whom were not. How would we do it to test a new drug? Why not do it to test an intervention in this context? To test the effect of a policy, we can to use randomized evaluation, where a randomly selected treatment group receives a treatment, while the other group does not (this is the comparison group). We will collect data on both the treatment and the comparison group, and compare the results. Because the treatment and the comparison group have been randomly selected, we can be sure that any difference between them is due to the treatment. Example: Iron supplementation in Indonesia. base level of anemia: figure 1 design: About 3,000 households. Households are randomly selected to be in the placebo or treatment group. Iron is distributed at home in blister packs. Compliance is strictly enforced (over 90%). remark: is it a program that could be scaled up? why do we care about the results then? we will compare those who were supposed to take the pill to those who were not supposed to take it. Why? figure 2, table 3: effect on hb level in blood. tables 4 to 7: results on work, health, happiness. How do we read these results? 5

To address these, we need to compare comparable people, some of whom were exposed to a particular policy and some of whom were not. How would we do it to test a new drug?

6 5 Experiments in Udaipur What experiments do you propose to do in Udaipur? The experiments we propose: Public Health: chlorination of wells Nutrition: iron fortification of flour Health care: second ANM in public centers Health investment: incentives for vaccinations. Design: we will work in 120 villages, randomly divided into enough groups to test interventions against each other, and in combination. What will the results from these experiments tell us about policy in Rajasthan? About the broader policy debate about whether one should invest in health care? 6

centers Health investment: incentives for vaccinations.

14.74 Lecture 5: Health (2)

14.74 Lecture 5: Health (2) Esther Duflo February 17, 2004 1 Possble Interventons Last tme we dscussed possble nterventons. Let s take one: provdng ron supplements to people, for example. From the data,