Abstract
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). We are CHARLS pilot data to document health conditions, using 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. 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.
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Notes
Overweight is defined using World Health Organization standards of having a BMI 25 or above.
The contact and interview rate was 86% of those households that were chosen to be sampled. This is much better than HRS-type surveys in the US and Europe, which now tend to be in the 60 or even 50% range, and compares favorably with other surveys done in Asia.
Spouses who are under 45 years old are dropped from this analysis.
Here we use the sample weights allowing for household non-response using local community dummies to predict household nonresponse. We do not incorporate non-response for the biomarkers in the weights. The results using weights that do so using inverse probability weights (IPW) are similar in nature. Incorporating non-response with IPW requires an assumption of selection on observables, which is very strong and unlikely to be met. Using more standard selection methods is best using exclusion restrictions, which we do not plausibly have.
Heights were measured using a lightweight SECA aluminum height board, the SECA 214 portable stadiometer. Weights were measured using a portable digital scale, the Beaver Tech HTS7270. Blood pressure was taken with a digital meter, the Omron HEM 712c meter.
A linear spline allows different slopes to the left and right of the knot point with the two lines being joined at the knot point. The first coefficient reported is the slope to the left of the knot point and the second coefficient is the change in the slope between the two knot points.
Cohort effects would arise because younger birth cohorts have more schooling and also faced better health conditions when they were babies and in the fetus, compared to older cohorts. There is an accumulation of evidence now that better health conditions when young are associated with better health in old age (for instance Barker 1994; Gluckman and Hanson 2005; Strauss and Thomas 2008, for an economist’s perspective).
We tried a specification with interactions between level of schooling dummies and age to help get at causality (see Witoelar et al. 2009, for such an exercise for Indonesia), but except for IADLs, these were generally not significant.
In an expanded working paper (Strauss et al. 2010) we add a specification using dummies for province and rural residence within province. Clearly this is a coarse definition of location.
In a few cases there was no variation in the dependent variable among observations within the PSU. In these cases we refined the PSU to be a more aggregate area, usually the county or city.
Since sample size differs depending on the health measure used, we compute means of the independent variables for the ADL sample, which is the largest, at 1,250 men and 1,225 women. For each dependent variable we use the actual regression sample; see the regression tables for sample sizes for each. Sample sizes will differ because of missing observations. For the biomarkers, such as blood pressure, some 30% of the sample were not measured. This can be because the elder person was too frail or a younger-aged person was working and not able to be measured. As noted above, this corresponds with the experience of the HRS and related surveys.
The sources are the Matlab Health and Socioeconomic Survey (1996); the China Health and Nutrition Survey (1991); the Indonesia Family Life Survey (2000); the South African Demographic Health Survey (1998); the Mexican Family Life Survey (2002); and the NHANES3 (National Health and Nutrition Examination Survey III) (1988–1994).
We use standard levels of significance to mean a probability value of .05 or less.
With community fixed effects, testing the joint significance of the community dummies is not straightforward. Because there are in our case few observations per cluster, we cannot cluster the standard errors after estimation using community fixed effects and use an F-test to test for the joint significance of clusters (Wooldridge, personal communication). To test the community dummies we reestimate the model with community dummies and robust standard errors, without clustering, and do the F-test.
It could be that the significance of the community dummy variables represents the impact of province and rural/urban location. This is not the case for our results. When we stratify the sample by the two provinces and run separate regressions for each province using community fixed effects within each province, the community dummies are still generally significant. Results are available upon request.
Because of the way the questionnaire is designed, those who report taking medicines are a subset of those who report a positive doctor diagnosis.
Our physical activities and ADL assessments include: walking for 100 m; stooping, kneeling, crouching; extending arms above shoulder level; lifting weights like a heavy bag of groceries; picking up a small coin from a table; climbing several flights of stairs without help, standing from a sitting position without help, dressing without help; bathing or showering; cutting food and eating; going to the bathroom without help (including sitting down and getting up); controlling urination and defecation; and getting into and out of bed. The IADL assessments are having difficulties with doing household chores; preparing hot meals; shopping for groceries; managing money; making phone calls (if they have a phone); and taking medications.
The answers for CES-D are on a four-scale metric, from rarely, to some days (1–2 days), to occasionally (3–4 days) to most of the time (5–7 days). We score these answers in the way suggested by the US National Institute of Mental Health researcher (Lenore Radloff) who created the CES-D, using numbers from 0 for rarely to 3 for most of the time, for negative questions such as do you feel sad. For positive questions such as do you feel happy, the scoring is reversed from 0 for most of the time to 3 for rarely.
Only 2% of women report that they currently smoke.
Here smoking is defined as smoking cigarettes or cigars. Current smoking prevalence for older men in CHARLS is quite close to rates of 55% reported from the China Health and Nutrition Survey (CHNS), for other provinces; see Kenkel et al. (2009). The CHNS rates are for all adult ages.
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Comments from Richard Suzman and David Weir are greatly appreciated, as are suggestions from two referees and the Editors. Earlier versions were presented at the annual meetings of the Population Association of America, April 2009, Detroit, the World Congress of the International Association of Gerontology and Geriatrics, July 2009, Paris, and the 2nd International Conference on Health and Retirement in China, July 2009, Beijing. This research was supported by grants from the National Institute of Aging, the China National Natural Science Foundation and the World Bank, China.
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Strauss, J., Lei, X., Park, A. et al. Health Outcomes and Socio-economic Status Among the Elderly in China: Evidence from the CHARLS Pilot. Population Ageing 3, 111–142 (2010). https://doi.org/10.1007/s12062-011-9033-9
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DOI: https://doi.org/10.1007/s12062-011-9033-9