Chia-Ling Wu

Bio: Chia-Ling Wu is an academic researcher from National Taiwan University. The author has contributed to research in topics: Educational attainment & East Asia. The author has an hindex of 8, co-authored 15 publications receiving 2491 citations.

The links between education and health.

Abstract: University of Illinois, Urbana The positive association between education and health is well established, but explanations for this association are not. Our explanations fall into three categories: (1) work and economic conditions, (2) social-psychological resources, and (3) health lifestyle. We replicate analyses with two samples, cross-sectionally and over time, using two health measures (self-reported health and physical functioning). The first data set comes from a national probability sample of U.S. households in which respondents were interviewed by telephone in 1990 (2,031 respondents, ages 18 to 90). The second data set comes from a national probability sample of U.S. households in which respondents ages 20 to 64 were interviewed by telephone first in 1979 (3,025 respondents), and then again in 1980 (2,436 respondents). Results demonstrate a positive association between education and health and help explain why the association exists. (1) Compared to the poorly educated, well educated respondents are less likely to be unemployed, are more likely to work full-time, to have fulfilling, subjectively rewarding jobs, high incomes, and low economic hardship. Full-time work, fulfilling work, high income, and low economic hardship in turn significantly improve health in all analyses. (2) The well educated report a greater sense of control over their lives and their health, and they have higher levels of social support. The sense of control, and to a lesser extent support, are associated with good health. (3) The well educated are less likely to smoke, are more likely to exercise, to get health check-ups, and to drink moderately, all of which, except check-ups, are associated with good health. We conclude that high educational attainment improves health directly, and it improves health indirectly through work and economic conditions, social-psychological resources, and health lifestyle. he positive association between education and health is well established, but explanations for this association are not. Well educated people experience better health than the poorly educated, as indicated by high levels of self-reported health and physical functioning and low levels of morbidity, mortality, and disability. In contrast, low educational attainment is associated with high rates of infectious disease, many chronic noninfectious diseases, self-reported poor health, shorter survival when sick, and shorter life expectancy (Feldman, Makuc, Kleinman, and Cornoni-Huntley 1989; Guralnik, Land, Fillenbaum, and Branch 1993; Gutzwiller, LaVecchia, Levi, Negri, and Wietlisbach 1989; Kaplan, Haan, and Syme 1987; Kitagawa and Hauser 1973; Liu, Cedres, and Stamler 1982; Morris 1990; Pappas, Queen,

Education, age, and the cumulative advantage in health

Abstract: The positive association between educational attainment and health is well established, but the way in which the education-based gap in health varies with age is not. Do the health advantages of high educational attainment and disadvantages of low educational attainment diverge or converge with age? The cumulative advantage perspective predicts a diverging SES gap in health with age, but past evidence does not allow us to accept or reject the hypothesis. We address this issue in two samples, cross-sectionally and over time, with three health measures. The first data set consists of a 1990 telephone interview of a national probability sample of U.S. households. There are 2,031 respondents, aged 18 to 90. The second is a national probability sample of U.S. households in which 2,436 respondents aged 20 to 64 were interviewed by telephone in 1979 and reinterviewed in 1980. We find that the gap in self-reported health, in physical functioning, and in physical well-being among people with high and low educational attainment increases with age. The health advantage of the well educated is larger in older age groups than in younger. Health advantages of high income and disadvantages of low income also diverge with age, but household income does not explain education's positive effect.

Staphylococcal cassette chromosome mec in MRSA, Taiwan.

Abstract: To determine the predominant staphylococcal cassette chromosome (SCC) mec element in methicillin-resistant Staphylococcus aureus, we typed 190 isolates from a hospital in Taiwan. We found a shift from type IV to type III SCCmec element during 1992–2003, perhaps caused by selective pressure from indiscriminate use of antimicrobial drugs.

Managing multiple masculinities in donor insemination: doctors configuring infertile men and sperm donors in Taiwan.

Abstract: This article investigates how doctors configured infertile men and sperm donors in the development of donor insemination (DI) in Taiwan. In the initial stage (1950s-1970s) doctors adjusted clinical procedures to repair the deformed gender identities of infertile men. To expand DI in the late 1970s and early 1980s, doctors stressed the positive eugenics of DI by spotlighting the high intelligence of donors, playing down biological patrilineage and re-emphasising the contribution of men of higher rank in society. In the mid-1980s, when donors came to be seen as potential carriers of fatal diseases like acquired immune deficiency syndrome, doctors managed to associate risky donors with socially stigmatised men, and therefore perpetuate the conventional hierarchy of masculinities. As the intracytoplasmic sperm injection emerged in the early 1990s doctors quickly presented infertile men as universally longing for biological fatherhood and hence devalued DI in an attempt to augment paternal masculinity. These diverse configuration activities come together to create a socio-technical network of DI that most of the time perpetuates the reigning gender order, rather than destabilising it. I argue the importance of incorporating various types of participants in analysis to understand the changing dynamics of multiple masculinities along with the development of DI.

Measuring Social Class in US Public Health Research: Concepts, Methodologies, and Guidelines

Abstract: Increasing social inequalities in health in the United States and elsewhere, coupled with growing inequalities in income and wealth, have refocused attention on social class as a key determinant of population health. Routine analysis using conceptually coherent and consistent measures of socioeconomic position in US public health research and surveillance, however, remains rare. This review discusses concepts and methodologies concerning, and guidelines for measuring, social class and other aspects of socioeconomic position (e.g. income, poverty, deprivation, wealth, education). These data should be collected at the individual, household, and neighborhood level, to characterize both childhood and adult socioeconomic position; fluctuations in economic resources during these time periods also merit consideration. Guidelines for linking census-based socioeconomic measures and health data are presented, as are recommendations for analyses involving social class, race/ethnicity, and gender. Suggestions for research on socioeconomic measures are provided, to aid monitoring steps toward social equity in health.

Socioeconomic status in health research: one size does not fit all.

Abstract: Problems with measuring socioeconomic status (SES)—frequently included in clinical and public health studies as a control variable and less frequently as the variable(s) of main interest—could affect research findings and conclusions, with implications for practice and policy.Wecritically examine standard SES measurement approaches, illustrating problems with examples from new analyses and the literature. For example, marked racial/ethnic differences in income at a given educational level and in wealth at a given income level raise questions about the socioeconomic comparability of individuals who are similar on education or income alone. Evidence also shows that conclusions about nonsocioeconomic causes of racial/ethnic differences in health may depend on the measure—eg, income, wealth, education, occupation, neighborhood socioeconomic characteristics, or past socioeconomic experiences—used to “control for SES,” suggesting that findings from studies that have measured limited aspects of SES should be reassessed. We recommend an outcome- and social group–specific approach to SES measurement that involves (1) considering plausible explanatory pathways and mechanisms, (2) measuring as much relevant socioeconomic information as possible, (3) specifying the particular socioeconomic factors measured (rather than SES overall), and (4) systematically considering how potentially important unmeasured socioeconomic factors may affect conclusions. Better SES measures are needed in data sources, but improvements could be made by using existing information more thoughtfully and acknowledging its limitations.

Socioeconomic Disparities In Health: Pathways And Policies

Abstract: Socioeconomic status (SES) underlies three major determinants of health: health care, environmental exposure, and health behavior. In addition, chronic stress associated with lower SES may also increase morbidity and mortality. Reducing SES disparities in health will require policy initiatives addressing the components of socioeconomic status (income, education, and occupation) as well as the pathways by which these affect health. Lessons for U.S. policy approaches are taken from the Acheson Commission in England, which was charged with reducing health disparities in that country.