中国农村儿童死亡率的环境成因：用竞争风险方法进行的研究

Environmental Determinants of Child Mortality in Rural China: A Competing Risks Approach Hanan Jacoby* & Limin Wang**1 Abstract We use a competing risk model to analyze environmental determinants of child mortality using the 1992 China National Health Survey, which collects information on cause of death. Our primary question is whether taking into account of cause of death using a competing risk model, compared with a simple model of all-cause mortality, affects conclusions about the effectiveness of policy interventions. There are two potential analytical advantages in using cause of death information: (1) obtaining more accurate estimates and (2) validating causal relationships. Although, we do not find significant differences between estimates obtained from the competing risk model and those from simpler hazard models, we do find evidence supporting the causal interpretations of the effect of access to safe water on child mortality. Our analysis also suggests that a respondent-based health survey can be used to collect relatively reliable information on cause of death. Modifying future demographic and health survey (DHS) instruments to collect cause of death information inexpensively may be worthwhile for enhancing the analytical strength of the DHS. World Bank Policy Research Working Paper 3241, March 2004 The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org. * DECRG, World Bank, ** ENV, World Bank. We gratefully acknowledge the financial support from Swedish International Development Agency (SIDA), TF024884. We thank Kirk Hamilton, the task manager of this study, for suggestions and comments, Jostein Nygard and Adam Wagstaff for useful discussions. Alexandra Sears provided excellent assistance.  Introduction Environmental risk factors account for about one-fifth of the total burden of disease in low-income countries according to recent estimates (World Bank, 2001). WHO (2002) reports that among the 10 identified leading mortality risks in high- mortality developing countries, unsafe water, sanitation and hygiene ranked second, while indoor smoke from solid fuels ranked fourth. A number of econometric studies using household survey data from low income countries also find significant relationships between environmental factors and child morbidity and mortality (e.g., Wolfe and Behrman, 1982; Lee, et al. 1997). Such evidence suggests that public investments in health infrastructure can improve health outcomes, particularly child survival prospects. In this paper, we evaluate alternative empirical methodologies for estimating the impact of environmental factors on child mortality in micro-data. Our primary question is whether taking into account cause of death using a competing risk model affects conclusions about the effectiveness of policy interventions. Such information is critical for prioritizing public investments in order to maximize the health benefit for given resources, particularly in the context of achieving the targets set by the Millennium Development Goals (MDG) on child mortality and environment. To our knowledge, no previous econometric study of child mortality has explicitly modeled cause of death. Indeed, distinguishing child deaths by cause may seem unwarranted. After all, the objective of policy should be to prevent child deaths from any cause; as long as a child's life is saved, it should be a matter of indifference whether the averted death would have been the result of diarrhea disease or respiratory infection.2 By this reasoning, it is sufficient to analyze the determinants of all-cause mortality, which only involves estimating a simple child survival-time (or hazard) model. Nevertheless, there are a couple reasons why accounting for cause of death using a competing risks framework might be advantageous. First, this type of survival model is more flexible than an all-cause model and might, therefore, give more accurate estimates of environmental impacts. Second, separate estimates of the effect of environmental factors by cause of death may provide a way to detect the presence of confounding 2This is not to deny the epidemiological interest in the particular pathways by which an intervention prevents a death. 2  factors -- in other words, of endogeneity bias. Certain environmental variables should have no effect on the probability of dying from certain causes. If it turns out that they do, then these environmental variables may be picking up unobserved attributes of households or communities that are correlated with mortality outcomes, the presence of which would invalidate any causal inferences.3 Information on cause of death is collected in only a few DHS, usually by self- reports combined with verbal autopsy methods. Aside from the well-known reliability issue, one problem is that, given the typical DHS sample size, the number of deaths from any particular cause is likely to be small. For this reason, we turn to a national health survey conducted in China in 1992, and modeled after the DHS, which does provide information on cause of child death. An advantage of the China survey is its vast sample size