Rapid Responses to:

PAPERS: Andreas Muller Education, income inequality, and mortality: a multiple regression analysis BMJ 2002; 324: 23 [Abstract] [Full text]

Rapid Responses: Submit a response to this article

Rapid Responses published:

Is education inequality important?

Neil R Parker   (4 January 2002)

Another Possible Explanation of Dr. Muller's Findings

James M. Howard   (15 January 2002)

Dr. Muller’s response to electronic comments

Andreas Muller   (17 February 2002)

INDIVIDUAL-LEVEL EDUCATION DOES NOT EXPLAIN THE ASSOCIATION OF STATE-LEVEL INCOME INEQUALITY WITH HE

Tony A Blakely, Ichiro Kawachi   (13 March 2002)

WHY IS EDUCATION LESS IMPORTANT AT THE INDIVIDUAL LEVEL OF ANALYSIS THAN AT THE STATE LEVEL?

Andreas Muller   (7 April 2002)

Is education inequality important? 4 January 2002
Neil R Parker, Director, Darling Downs Pubic Health Unit Toowoomba, Qld, Australia 4350 Send response to journal: Re: Is education inequality important?	Could the regression analysis be redone including education inequality a variable?
Another Possible Explanation of Dr. Muller's Findings 15 January 2002
James M. Howard 1037 North Woolsey Avenue, Fayetteville, Arkansas 72701-2046, U.S.A. Send response to journal: Re: Another Possible Explanation of Dr. Muller's Findings	I suggest another hypothesis may explain "Education, income inequality, and mortality: a multiple regression analysis," BMJ 2002; 324: 23. Excessive testosterone may be the cause. That is, failing to graduate high school and shortened life span may be coincident consequences of high testosterone. It is my hypothesis that human evolution is driven by increases in testosterone ("Androgens in Human Evolution. A New Explanation of Human Evolution," Rivista di Biologia / Biology Forum 2001; 94: 345-362). I suggest all hominid characteristics are directly influenced by increasing testosterone. Periodically, however, where circumstances are very favorable to reproduction, testosterone increases too rapidly. My work suggests this causes problems in increased infection rates, lowered sperm counts, etc. These circumstances may have produced the “bottleneck in hominid populations” which I connect with “Mitochondrial Eve” and excessive testosterone, which was overcome by mutations controlling sperm production, which I connect with “Y Chromosome Adam.” (“Mitochondrial Eve, Y Chromosome Adam, Testosterone and Human Evolution,” accepted for publication by Rivista di Biologia / Biology Forum.) Populations of high testosterone individuals sometimes experience very negative consequences. Within modern populations, I suggest these periodic, rapid increases in testosterone within populations produce the “secular trend,” which is real and vigorous at this time in the U.S.A. (“Secular Trends in Height Among Children During 2 Decades. The Bogalusa Heart Study,” Archives of Pediatric and Adolescent Medicine. 2000; 154: 155-161). The secular trend is the increase in size and earlier onset of puberty. The phenomena Muller examined are current problems, i.e., byproducts of the secular trend. Testosterone is connected with numerous conditions Muller describes as “economic resource deprivation, risk of occupational injury, and learnt risk behaviour.” For example, domestic violence is connected to testosterone: “Testosterone levels were significantly associated with levels of both verbal aggression and physical violence self-reported by the men.” (Psychoneuroendocrinology 2000; 25:721-39), learning disabilities: “The matched analysis further substantiated the association between testosterone secretion and learning disabilities. Thus, it is possible that some learning disabilities may be associated in part with abnormal testosterone levels.” (Physiol Behav 1993; 53: 583-6), adolescent smoking: “In this study, salivary testosterone was positively associated with cigarette smoking among 201 subjects 12-14 years of age.” (J Behav Med 1989; 12: 425-33), impulsive behavior: “…the results of this study support a positive relationship between testosterone and impulsivity.” (Physiol Behav 2001; 73: 217-21), and infection rates: “…male gender was significantly associated with an increased incidence of pneumonia after injury…” (J Trauma 2001; 50: 274-80). I suggest the presence of testosterone also accounts for phenomena described by Dr. Muller. In the U.S.A. blacks males disproportionately fail to graduate high school and exhibit “economic resource deprivation, risk of occupational injury, and learnt risk behaviour.” I suggest this is due to the fact that black males produce significantly more testosterone than other groups vis-à-vis females and white males. “Mean testosterone levels in blacks [males] were 19% higher than in whites [males], and free testosterone levels were 21% higher. Both these differences were statistically significant.” (J Natl Cancer Inst 1986; 76: 45-8). Black boys were affected most by the secular trend in the Bogalusa Heart Study. I suggest increases in testosterone within certain groups may also explain Dr. Muller’s findings.
Dr. Muller’s response to electronic comments 17 February 2002
Andreas Muller, Department of Health Services Administration University of Arkansas at Little Rock Send response to journal: Re: Dr. Muller’s response to electronic comments	Makenbach correctly points out that the causal order of income inequality and educational attainment is debatable in my cross-sectional analysis. He writes that educational attainment can be viewed as a “confounder, or an intermediary between income inequality and mortality.” To shed some light on this issue, I related age-adjusted mortality rates (1989-90) to Gini coefficients for families in 1969. The lagged income inequality measure accounts for 10.7% of the variation in age-adjusted mortality rates. The addition of the educational attainment measure increased the value to 54.7%. The 44-percentage point increase in accounted variance suggests that education makes an independent contribution and appears to be a strong confounder. Parker raises the question whether educational inequality is considered in the regression analysis. The answer is: yes in a simple way. States with low high school completion rates also tend to have low completion rates for advanced degrees. In an earlier draft of the paper I included a second educational attainment measure, the percent of persons 18 years of age and older with associate and higher educational degrees. The variable had the expected negative effect on mortality, however the measure was statistically insignificant when the analysis was weighted by population size. Dropping the second education variable did not alter the main finding of the study. Jerret cautions that the disappearance of the income inequality effect may be due to spatial autocorrelation bias. I examined this possibility by looking at contiguous and non-contiguous states (AL, AR, IA, KS, KT, ME, MI, MD, NV, NM, NY, ND, SC, WA, WY). A regression model allowing for different slopes showed no statistically significant differences between state types for the income inequality, the educational attainment, and the per capita income measure. This suggests that spatial autocorrelation may not be a problem, although the issue deserves further examination. Howard suggests an unexpected interpretation of the strong education- mortality effect reported in my paper. He argues that differences in testosterone levels among racial groups may account for the effect. Several findings do not agree with his interpretation. A substantial education-mortality effect is present among females (b=7.05, se=1.42) who produce substantially lower amounts of testosterone than men. A further analysis by race indicates that the education-mortality effect is quite similar for the Caucasian (b=6.47; se=1.11) and the African American population (b=7.51; se=1.67). The similarity in size of the regression coefficients suggests that the education-mortality effect is unlikely to reflect racial differences in testosterone levels. According to Wilkinson’s comments, the substantial education- mortality effect represents contextual effects of social status and dominance that may result in higher rates of violence, and perhaps other forms of social dysfunction. This prediction may be correct, but will need to be empirically tested. We should not ignore potentially simpler explanations of the same phenomenon. Part of the education-mortality effect might simply be a reflection of conditions typically associated with low levels of education: unstable employment in low wage jobs, employment in riskier jobs, inadequate access to health care, smoking, unhealthy diet, living in areas lacking health promoting resources. Exposure to these conditions over a lifetime will deplete the stock of health and shorten life span. There is a need for more detailed analyses that sort out the relative importance of what may turn out to be different aspects of a broader explanation.
INDIVIDUAL-LEVEL EDUCATION DOES NOT EXPLAIN THE ASSOCIATION OF STATE-LEVEL INCOME INEQUALITY WITH HE 13 March 2002
Tony A Blakely, Senior Research Fellow Wellington School of Medicine and Health Sciences, University of Otago, Ichiro Kawachi Send response to journal: Re: INDIVIDUAL-LEVEL EDUCATION DOES NOT EXPLAIN THE ASSOCIATION OF STATE-LEVEL INCOME INEQUALITY WITH HE	INDIVIDUAL-LEVEL EDUCATION DOES NOT EXPLAIN THE ASSOCIATION OF STATE- LEVEL INCOME INEQUALITY WITH HEALTH Muller (2002) has shown in an ecological study that lack of high school education accounts for the state-level association of income inequality with mortality in the US.[1] The implicit inference is that education at the individual-level is responsible for the income inequality association. However, ecological studies are weak study designs to assess the independent associations of ecological (income inequality) and individual-level (education) variables with an individual-level outcome (health status). In particular, aggregate data are prone to problems of collinearity rendering it difficult to tease apart independent effects. Multi-level study designs overcome this limitation by including data at both the individual and ecological levels.[2 3] We have previously reported results for the association of income inequality at the state-level with self-rated health using Current Population Survey (CPS) data.[4 5] However, we have not previously reported the effect of controlling for education at the individual-level among adults in the 1995 and 1997 CPS as shown in the Table below. Controlling for education attenuated but did not completely explain the relationship between levels of state income inequality and self-rated health. Our results do not support the contention that education at the individual-level fully confounds or mediates the association of income inequality with health. The remaining portion of the income inequality association may be due to contextual effects (e.g. average educational attainment, historical and political features that vary by states in the US) or other individual-level characteristics such as lifestyle. Table: Odds ratios of fair/poor self-rated health by quintile of state-level income inequality, with and without adjustment for individual education Quintile of Model 1 Model 2 inequality White males (n=59,341) High 1.22 (1.00-1.50) 1.16 (0.97-1.39) Medium-high 1.44 (1.18-1.76) 1.38 (1.15-1.65) Medium 1.25 (1.02-1.54) 1.20 (0.99-1.45) Medium-low 0.99 (0.79-1.25) 0.98 (0.79-1.22) Low 1.00 1.00 White females (n=62,404) High 1.29 (1.01-1.64) 1.19 (0.95-1.48) Medium-high 1.41 (1.12-1.78) 1.34 (1.08-1.65) Medium 1.40 (1.09-1.78) 1.33 (1.07-1.66) Medium-low 1.11 (0.86-1.45) 1.11 (0.87-1.41) Low 1.00 1.00 Model 1 = Age and income as covariates at the individual-level, and quintile of average income as covariate at state-level Model 2 = Model 1 plus individual-level education REFERENCES 1. Muller A. Education, income inequality, and mortality: a multiple regression analysis. BMJ 2002;324:23-25. 2. Diez-Roux A. Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health 1998;88:216-222. 3. Blakely T, Woodward A. Ecological effects in multi-level studies. J Epidemiol Community Health 2000;54:367-374. 4. Blakely T, Kennedy B, Kawachi I. Socio-economic inequality in voting participation and self-rated health. Am J Public Health 2001;91:99-104. 5. Blakely T, Kennedy B, Glass R, Kawachi I. What is the lag time between income inequality and heath status? J Epidemiol Community Health 2000;54:318-319.
WHY IS EDUCATION LESS IMPORTANT AT THE INDIVIDUAL LEVEL OF ANALYSIS THAN AT THE STATE LEVEL? 7 April 2002
Andreas Muller, Professor, Health Services Administration University of Arkansas at Little Rock, 2801 S. University Ave., Little Rock AR 72204 Send response to journal: Re: WHY IS EDUCATION LESS IMPORTANT AT THE INDIVIDUAL LEVEL OF ANALYSIS THAN AT THE STATE LEVEL?	Blakely and Kawachi show in a new analysis of self-reported health that education measured at the level of individuals does not fully account for income inequality measured at the state level. Lack of high school education completely accounted for the income inequality effect in my study of U.S. states. The discrepancy in findings is not surprising since our analyses are at different levels of aggregation, use different methods of analysis, and measure different outcomes among other differences. More specifically, my paper points out that the large education- mortality effect in part reflects other factors than education. [1 p3] I briefly mention the results of an expanded regression analysis that controls for additional confounders: (1) percent of persons 15 years old and older married, (2) percent of employed persons 16 years and older in high injury risk occupations, (3) percent of persons 18 years and older current smokers, (4) percent of persons without health insurance, (5) percent African American, or Latino population, and (6) percent of population foreign born. The additional control variables improve model fit significantly (R2 adj. = .82) and reduce the direct effect of education by 40%. However, the education effect remains significant (b = .066; p = .01) while the income inequality effect continues to be spurious (b = .54; p = .89). The more detailed analysis was submitted to BMJ, but not published. The results of the expanded regression analysis are available upon request. Blakely and Kawachi’s new data provide only weak support for the causal contribution of income inequality when education is added as control. The odds ratios comparisons (model 2) indicate that five out of eight comparisons are statistically insignificant. In addition, exposure to medium levels of income inequality results in poorer self-rated health than exposure to high levels of income inequality. This is an unexpected result that suggests that some confounding variables may be at work. My question is: will the income inequality effect disappear with a more complete model specification? My guess is that it will be further reduced and the education effect will stay intact. This result would also be consistent with studies of individuals that found education to be an important predictor of mortality. [2 3 4] REFERENCES 1. Muller A. Education, income inequality, and mortality: a multiple regression analysis. BMJ 2002;324:1-4. 2. Backlund E, Sorlie PD, Johnson NJ. A comparison of the relationships of education and income with mortality: the national longitudinal mortality study. Soc Sci Med 1999;49:1373-84. 3. Elo IT, Preston SH. Educational differentials in mortality: United States, 1979-85. Soc Sci Med 1996;42:47-57. 4. Hoyert DL, Arias E, Smith BL, Murphy SL, Kochanek KD. Deaths: Final data for 1999. National Vital Statistics Reports, vol. 49 no 8.Hyattsville, Maryland: National Center for Health Statistics. 2001.