Health Outcomes and Socio-Economic Status among ... - Index of - IZA

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1. Introduction We are concerned in this paper with measuring health outcomes among the elderly in Zhejiang and Gansu provinces, China, and examining the relationships between different dimensions of health status and measures of socio-economic status (SES). China has undergone a health revolution over the past 50 years, with life expectancy having risen from 46 in the 1950s to just over 71 in 2000 (Wagstaff et al., 2009; World Health Organization, 2009). Driving this change, under 5 mortality fell dramatically from 225 per 1,000 live births in 1960 to 64 in 1980 to 22 in 2007 (Wagstaff et al., 2009; UNICEF, 2009). Most of this decline was due to an increasing control over infectious disease and under-nutrition. As a result, infectious diseases have been progressively replaced by chronic diseases as the major source of ill-health and mortality (Hossein, 1997; Lopez et al., 2006). As China has been passing through its health transition, it has also been undergoing a nutrition transition, which has both good and bad sides (Popkin et al., 1993, 1995a; Popkin, 1999, 2002). Among the principle dimensions of this transition has been a dramatic rise in body mass index among adults and a large change in diet towards more fatty foods (Popkin et al., 1995b). For instance Luo (2003), using the China Health and Nutrition Survey (CHNS), documents an increase in overweight for adults over 20 years from 1989 to 1997; for women from 11% to 21% and from 6% to 17% for men. 1 At the same time, Luo shows that the fraction of adults elderly who are undernourished (a BMI under 18.5) has fallen, particularly so for those over 60 years, from 19% to 13% for women and 20% to 12% for men, from 1991 to 1997. Related to the health and nutrition transitions has been China’s demographic transition. China’s elderly will increase from under 10% of the total population in 2000 to 30% in 2050 1 Overweight is defined using World Health Organization standards of having a BMI 25 or above. 3
(Kinsella and He, 2009). The number of workers per pensioner has already fallen from over 12 in 1980 to 2 in 2005. This sharp demographic transition is likely to place stress on China’s health system, which has been focused on disease at younger ages and on infectious, not chronic diseases. In this paper, we use CHARLS pilot data to document health conditions among the elderly in Gansu and Zhejiang provinces, where the survey was fielded. We use a very rich set of health indicators that include both self-reported measures and biomarkers. We also examine correlations between these health outcomes and two important indicators of socio-economic status (SES): education and log of per capita expenditure (log pce), our preferred measure of household resources. While there exists a very large literature that examines the relationships between SES and health measures, little has been done on Chinese data to see whether correlations reported in many other countries are replicated in China, particularly so for the aged. While we cannot infer causality from these estimates, they tell us something important about the degree of health differentials by education and pce. In general education tends to be positively correlated with better health outcomes, as it is in other countries. However, unmeasured community influences turn out to be highly important, much more so than one usually finds in other countries. While it is not yet clear which aspects of communities matter and why they matter, we set up an agenda for future research on this topic. We also find a large degree of under-diagnosis of hypertension, a major health problems that afflicts the aged. This implies that the current health system is not well prepared to address the rapid aging of the Chinese population, at least not in Gansu and Zhejiang. 4
Page 1 and 2: DISCUSSION PAPER SERIES IZA DP No.
Page 3: IZA Discussion Paper No. 5152 Augus
Page 7 and 8: This paper documents in detail the
Page 9 and 10: particular. Income inequality is no
Page 11 and 12: egion within each classification. B
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Page 31 and 32: References Banks, James, Michael Ma
Page 33 and 34: Popkin, Barry, Keyou Ge, Fengying Z
Page 35 and 36: Figure 1 Relationship Between BMI a
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Page 39 and 40: numbers of IADLs with difficulty 0
Page 41 and 42: mean number of words recalled 2.5 3
Page 43 and 44: % reporting ''poor'' 10 20 30 40 42
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Page 53 and 54: Table 9. Regressions for CES-D 10 A
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Table 11. Regressions for "not very
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Table 13. Regressions for smoking:
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