As environments get better, genetics explain more of variance; as societies become more meritocratic, they become more unequal.
"Intelligence: Is it the epidemiologists' elusive 'fundamental cause' of social class inequalities in health?", Gottfredson 2004:
"Virtually all indicators of physical health and mental competence favor persons of higher socioeconomic status (SES). Conventional theories in the social sciences assume that the material disadvantages of lower SES are primarily responsible for these inequalities, either directly or by inducing psychosocial harm. These theories cannot explain, however, why the relation between SES and health outcomes (knowledge, behavior, morbidity, and mortality) is not only remarkably general across time, place, disease, and kind of health system but also so finely graded up the entire SES continuum. Epidemiologists have therefore posited, but not yet identified, a more general "fundamental cause" of health inequalities. This article concatenates various bodies of evidence to demonstrate that differences in general intelligence (g) may be that fundamental cause.
Conventional theories of social inequality posit that social class disparities in health result from disparities in material resources, such as access to medical care. Health demographers have become increasingly puzzled, however, by certain glaring failures of the poverty paradigm. The chief puzzle is why the relation between social class and health is so remarkably general across diverse times, places, and diseases and despite improvements in health care. In fact, greater equalization of health care and falling rates of morbidity and mortality tend to widen social class differences in health (Steenland, Henley, & Thun, 2002). Such paradoxes have led health demographers to posit some highly general and enduring- but still mysterious-fundamental cause of health inequalities that transcends the particulars of time, place, disease, material advantage, and social change (Adler et al., 1994).
I argue that g may be that fundamental cause because it meets six criteria that any candidate for the cause must meet: It has a stable distribution over time, is replicable (passed from one generation to the next), is a transportable form of influence, has a general (pervasive) effect on health, is measurable, and is falsifiable.
The second fact is that children's IQ predicts their later socioeconomic success better than do their parents' attributes. For instance, sons' education, occupation, and income correlate higher with their own IQ (true correlations of .68, .50, and .35) than they do with either their fathers' education (.43, .35, and .21) or occupation (.48, .44, and .29; Jencks et al., 1972, pp. 322, 337). The same predictive superiority of IQ over family background is found for the social pathologies, too (Herrnstein & Murray, 1994; Hirschi & Hindelang, 1977; see also Gordon, 1997). Third, most differences in both IQ and adult success occur among siblings in the same household, not between families (e.g., Jensen, 1980, p. 43, on IQ). Class-based theories of inequality do not discuss and cannot explain the large differences in life chances among siblings growing up in the same home, but g can. IQ differences among siblings produce essentially the same degree of inequality in adult success and pathology among the siblings as do comparable IQ differences among strangers (Murray, 1997, 1998; Olneck, 1977, pp. 137-138).
Why one person rather than another misreads a particular bus schedule on any particular day has many causes and is probably little related to individual differences in g. Nor is such misreading, by itself, likely to be particularly consequential. The crucial point, however, is that g's effects are pervasive and consistent. As gambling houses know well, even small odds in one's favor can produce big profits in the long term when they remain consistently in one's favor and other influences are more erratic. Information processing is involved in all daily tasks, even if only to a minor degree, so higher g always provides an edge, even if small. In contrast, other influences (fatigue, advice, etc.) tend to be more volatile and haphazard, and thus are likely to cancel each other out over time. As the NALS data illustrate, people with higher literacy (g) tend to perform better on all literacy tasks, whether they involve dealing with banks, restaurants, or social service agencies, deciphering financial options, or engaging rights and duties as a citizen. People who plan and budget somewhat less well will find themselves slowly slipping behind others who started with the same material resources. People who less often grasp information about their educational, employment, financial, and political options or who make more numerous or more serious mistakes in following the steps and filling out the forms necessary to implement them gradually foreclose opportunities they see others taking. When this slippage occurs in many realms of life, it compounds. When an influence is ubiquitous, it can therefore outweigh all others in the long run, even episodically stronger ones, that are less consistent or less consistently useful (help, contacts, extraverted personality, strong interest, etc.).
In fact, this is the statistical phenomenon that explains why adding more items to a mental test will increase its reliability. It also explains why even test items with only faint g loadings (meaning that g is only a small factor in correctly answering any particular one of them) can, when sufficient in number, yield a test that measures almost nothing but g (Lubinski & Humphreys, 1997, pp. 163-165).
Age-specific rates of illness and death are often two to three times higher for individuals in the lower social strata. For many years the so-called poverty paradigm has dominated thinking about why such disparities exist. Under this paradigm, the disparities are presumed to result from differences in access to health care and other such resources (Hummer, Rogers, & Eberstein, 1998). "Wealth Secures Health," as a recent headline in the APA Monitor put it (Clay, 2001). Social class is typically measured by level of education, occupation, income, or some combination thereof (Dutton & Levine, 1989). The poverty paradigm has foundered, however, on a growing number of contrary facts and vexing paradoxes, many of them pointed out decades ago (Kitagawa & Hauser, 1973; Syme & Berkman, 1976).
Prominent among the challenging facts is that the paradigm's key health resource- greater access to medical care- has surprisingly little relation to differences in health. The introduction of Medicaid and Medicare in the United States during the 1960s soon led to the poor making as many physician visits per year as the nonpoor, but large class differentials in health remained- even when the poor began to visit physicians at a higher rate than the nonpoor (Rundall & Wheeler, 1979, p. 397). Great Britain and other countries that had expected to break the link between class and health by providing universal health care were dismayed when the disparities in health not only failed to shrink but even grew (see The Black Report by Townsend & Davidson, 1982; also Link & Phelan, 1995, p. 86; Marmot, Kogevinas, & Elston, 1987, p. 132; Susser, Watson, & Hopper, 1985, p. 237).
It is now amply documented, first, that equalizing the availability of health care does not equalize its use. Perhaps most important, less educated and lower income individuals seek preventive health care (as distinct from curative care) less often than do better educated or higher income persons, even when care is free (Adler, Boyce, Chesney, Folkman, & Syme, 1993; Goldenberg, Patterson, & Freese, 1992; Rundall & Wheeler, 1979; Susser et al., 1985, p. 253; Townsend & Davidson, 1982, Chapter 4).
Second, greater use of medical care does not necessarily improve health (Marmot et al., 1987, p. 132; Valdez, Rogers, Keeler, Lohr, & Newhouse, 1985). To illustrate, when a large, federally funded, RAND-conducted, randomized controlled experiment tested the effects of subsidizing health care costs at different levels in six cities across the United States, participants with free care used more medical care than those with only partly subsidized care, but their health was no better after 2 years. Participants with free care had indiscriminately increased their use of inappropriate as well as appropriate care (Lohr et al., 1986, p. 72). Prenatal care provides another example that more care does not necessarily produce better outcomes, in this case for newborns (Fiscella, 1995).
Third, health depends more now than ever on private precaution and health lifestyle. The American Psychological Society (APS) noted in its 1996 Human Capital Initiative report on health (APS, 1996) that "seven of the 10 leading causes of death have aspects that can be modified by doing the right thing; that is, by making healthy choices about our own behavior" (p. 5) and that mortality "could be reduced substantially if people at risk would change just five behaviors: Adherence to medical recommendations (e.g., use of antihypertensive medication), diet, smoking, lack of exercise, and alcohol and drug use" (p. 15).
...The "paradox," Susser et al. (1985) noted, is that increased public health efforts at "prevention in many instances ha[ve] widened the disparity in health between the social classes," perhaps because "new preventive techniques have turned on personal behavior [e.g., not smoking] rather than on social engineering [e.g., controlling infectious disease by providing clean water and requiring immunizations for school entry]" (p. 254).
This puzzle is compounded by the "mystery of the SES- health gradient" (Adler et al., 1994, p. 22), which is that rates of illness and death are successively lower at successively higher rungs on the social ladder, and the monotonic decrease continues even among strata above the threshold of material resources required for adequate care. Moreover, the finely graded relation between class and health is found regardless of whether social class is measured by level of education, occupation, or income (Adler et al., 1993; Dutton & Levine, 1989), although education generally yields the tighter fit (e.g., Call & Nonnemaker, 1999, for adolescents' self-ratings of health; Cramer, 1987, for infant mortality; Hayward, Crimmins, Miles, & Yang, 2000, for 22 varied disabilities, diseases, and impairments among adults aged 51- 61; Pincus, Callahan, & Burkhauser, 1987, for 23 chronic diseases among adults aged 18 - 64). Although the SES- health relation often varies in strength, it is as clear in countries that provide universal health insurance as in those that do not, for diseases that are amenable to medical treatment as for those that are not, and for objective as well as subjective reports of illness (Adler et al., 1993, p. 3141; Dutton & Levine, 1989; Marmot et al., 1987).
Diseases involving different organ systems and with seemingly different etiologies nonetheless tend to be related in the same manner to social class. For example, a national sample of 10,538 adults aged 18 - 64 in 1978 yielded relative risk ratios between about 2.0 to 3.0 for cardiovascular, gastrointestinal, musculoskeletal, pulmonary, renal, and other diseases for adults with 1- 8 years of education compared with those with 12 years (ORs not shown, but calculated from Pincus et al., 1987, p. 870). Neoplastic (cancerous) and psychiatric illnesses were, respectively, less strongly and more strongly related to education level, the relevant ORs being 1.2 and 5.5 for adults with 1- 8 years of education. Relative risk for persons with more than 12 years of education was only about 0.7 for most of the diseases. All disease categories except neoplasms showed a dose-response relation (i.e., rates increased steadily at lower levels of education), and all diseases except neoplasms remained significantly related to education level after age, sex, race, and smoking were controlled for (Pincus et al., 1987). Even identical medical treatment for identical problems of equal severity yields SES-mortality gradients, as seen in a study of cancer survival in Boston (Syme & Berkman, 1976). The poverty paradigm has never developed a theory to explain the general relation it continues to describe (Hummer et al., 1998, p. 555; Mechanic, 1989, p. 20), perhaps partly because its different indicators of SES measure nothing obviously in common. Education is said to provide, among assorted other advantages, improved employment, gathering of information, health behavior, and negotiation of one's way through the health system, whereas income allows purchase of better health care, nutrition, transportation, and housing (Hummer et al., 1998, p. 560; Mechanic, 1989, pp. 10 22). Recent discussions of what the fundamental cause might be have included social support, social connectedness, social anxiety, chronic stress (allostatic load), sense of personal control or mastery, experience of control, self-esteem, nutrition, relative deprivation, stigmatization, self-perceived social status, resistance resources, coping strategies, and intrinsic problem-solving capacities (Adler et al., 1994; Adler & Snibbe, 2003; Clay, 2001; Dutton & Levine, 1989; Link & Phelan, 1995; Marmot et al., 1987; Pincus et al., 1987; and many papers in Adler, Marmot, McEwen, & Stewart, 1999). To my knowledge, none has been shown plausible for explaining the full pattern- especially the generality and occasional reversal- of class disparities across time, place, and disease.
Certain possibilities nonetheless seem to have been peremptorily dismissed. With few exceptions (e.g., Mechanic, 2000), any suggestion that class disparities in health may be due in part to genetic-based individual differences is today disapproved as blaming the victim (e.g., Cramer, 1995, p. 234) or "absolv[ing] the social structure of responsibility" (R. Wilkinson, 1996, p. 63).
Moreover, the association of knowledge with social class seems to be stronger when the information in question is more widely publicized by the mass media (Tichenor et al., 1970). This so-called knowledge-gap phenomenon was replicated yet again in three recent mass media campaigns on environmental issues in the Netherlands (Weenig & Midden, 1997). That study also found that diffusion was "remarkably similar" (p. 955) for all three issues, that it was characterized by a monotonic, negatively accelerated growth curve, and that the less educated groups "showed no signs at all of catching up" (p. 951) with the better educated. Feldman (1966) looked at health knowledge in particular and characterized his national survey as a case study in self-directed "adult learning" (p. 3) because "the mass media are the primary source of much of what people know about diseases" (p. 94; see also p. 136). He also noted that as the public had become better informed over time (e.g., about the warning signs of cancer), knowledge was not equalized because the previously informed had now become more fully informed. He found (Feldman, 1966, p. 116) that health knowledge is influenced to some extent by interest, gender, and age, after education is controlled, but surprisingly little by personal concern and experience with disease. Instead, he reported, education "is by far the strongest correlate" of knowledge (Feldman, 1966, p. 109). Both occupation and income, once again, were weaker correlates than education (e.g., p. 105). Education probably operates here mostly as a surrogate for g, because Beier and Ackerman (2003) found that knowledge of 10 different kinds of widely available health information (e.g., reproduction, aging, nutrition, safety) formed "one dominant factor" (p. 441), which in turn correlated about .90 (p. 443) with the g factor they derived from seven mental tests. Neither personality nor self-reported level of health knowledge had much relation to actual level of knowledge, and an education-income composite added nothing to its prediction after g was controlled for.
Feldman (1966, p. 97) also reviewed evidence that those who knew most before exposure to new information also gained most from the new exposure, so disparities in knowledge remained or grew. Education-related relative risk was higher for the less educated when the public as a whole was better informed about a disease. For example, in 1955, 48% of the public could name at least one symptom of diabetes, 62% could name at least one symptom of cancer, and 69% could name at least one symptom of polio (Feldman, 1966, p. 90). However, the relative risk of persons with 0 - 8 years of education (compared with those with 9 -12 years) not being able to name even one symptom was successively higher for the better known diseases-respectively, 1.7, 3.4, and 4.4 (ORs calculated from data in Feldman, 1966, p. 102). Moreover, the risk gradient for ignorance of the signs of cancer had steepened between 1945 and 1955, from 2.3 to 3.4 for the least educated, as more citizens had learned its signs (Feldman, 1966, p. 121).
It is very important to note that Feldman (1966, pp. 140 -148) also provided evidence that exposure is not just passive but that more educated people seek out and attend to more information, which is indicated by their more extensive use of newspapers, magazines, and books. NALS research shows similar differences in self-exposure (Kirsch & Jungeblut, n.d., p. 53; Kirsch et al., 1993, pp. 138 -140). This is exactly the double-barreled way that higher g promotes more learning-it increases exposure to learning opportunities and then allows for their fuller exploitation (cf. Rodgers et al., 1994; Rowe, 1997, on passive learning theory). That adopting health innovations involves active learning rather than just passive exposure accords with evidence that patients adopt birth control (Behrman, Kohler, & Watkins, 2002) and physicians adopt new antibiotics (Burt, 1987) more because of social learning and professional decision making than mere exposure to social influence.
...Health literacy researchers generally eschew any notion of intelligence for fear of inviting offensive distinctions onto the low-literate indigent and minority populations they study. There are several reasons, however, to believe that their measures are moderately to highly g loaded. First, the TOFHLA correlates highly with other tests of health literacy, such as the Wide Range Achievement Test-3 (.74; Parker et al., 1995), which in turn correlate moderately well with Full-Scale IQ (.53) and the Verbal Scale (.63) on the Wechsler Adult Intelligence Scale (WAIS; G. S. Wilkinson, 1993, p. 180). Second, TOFHLA literacy behaves in key ways like functional literacy and work literacy, which clearly are mostly g. For instance, like both the latter, the TOFHLA samples a wide variety of tasks that adults are routinely expected to perform, and comprehension is not improved by presenting tasks in oral rather than written form. Third, health literacy researchers were just as surprised as NALS researchers to discover that literacy is very general. They, too, soon concluded that low literacy reflects "limited problem-solving abilities" and began describing literacy as the "ability to acquire new information and complete complex cognitive tasks" (D. W. Baker, Parker, Williams, & Clark, 1998, pp. 796 -797).
A fourth sign that health literacy is largely g is seen in the strategies that health practitioners use to render health communications more comprehensible to low-literacy patients (Doak, Doak, & Root, 1996). Those strategies mirror the guidelines for simplifying written materials that were developed by Army researchers seeking to enhance the work literacy of low ability soldiers (Sticht, 1975). They constitute, in effect, a primer for reducing the complexity and information content of a communication. For example, omit all nonessential information; describe the specific behavior required of the individual; use simple vocabulary; require reading no higher than the fifth grade level; use simple line drawings (photographs contain distracting, irrelevant information); use several headings, arrows, or the like to summarize or draw attention to the most important pieces of information; and limit the number of type fonts and colors to minimize distraction. That is, provide no theory, require no inferences, provide only the bare minimum of information that must be understood to produce the desired behavior, and eliminate all else on the page that might distract rather than draw attention to it. Recall that degree of inference required, number of pieces of information used, and embeddedness in irrelevant and distracting material were all core elements of processing complexity in the NALS items.
Noncompliance or nonadherence to medical regimens has long vexed medical and health workers. Prescription drugs provide an example.
"Over half of the 1.8 billion prescriptions written annually are taken incorrectly by patients. . . . Because they are used improperly, an estimated 30 -50 percent of all prescriptions fail to produce desired results. . . . Approximately 10 percent of all hospitalizations and 23 percent of all nursing home admissions are attributed to a patient's inability to manage or follow drug therapy." (Berg, Dischler, Wagner, Raia, & Palmer-Shevlin, 1993, p. S5)
Worse yet, one study estimated that almost 30% of patients were taking their medication in a manner that seriously threatened their health (Roter et al., 1998).
Noncompliance of all sorts is particularly a problem in lowincome clinic populations, where rates frequently exceed 60% (Becker & Maiman, 1975, p. 10). Expense is seldom a barrier, but regimen complexity is (e.g., Berg et al., 1993, p. S8; Cameron, 1996; Dodrill, Batzel, Wilensky, & Yerby, 1987; Schulz & Gagnon, 1982). Noncompliance can impose high costs in morbidity and mortality, as exemplified in studies of death from myocardial infarction among heart patients in treatment (OR of 2.4 for poor adherence; Gallagher, Viscoli, & Horwitz, 1993). The problem here, then, is not lack of access to care but the patient's failure to use it effectively when delivered.
...Passing rates on even the simplest tasks tend to be low: 26% of the 2,659 patients did not understand information about when a next appointment was scheduled, 42% did not understand the directions for taking medicine on an empty stomach, and 60% did not understand a standard informed consent document (Williams et al., 1995). Relative risk of failing the TOFHLA items rose among the less literate groups, and the risk gradients were much steeper for the more complex tasks. ORs ranged from 6.0 to 70.5 for patients with inadequate literacy when compared with those with adequate literacy...Yet patients with low literacy still have shockingly low rates of knowledge about the most basic symptoms of their disease- ones, moreover, that often require them to take immediate action. For example, among 114 diabetics taking insulin daily, fully half of those with inadequate literacy but only 6% of those with adequate literacy did not know that feeling sweaty, nervous, and shaky is usually a sign that their blood glucose level is low. About 62% versus 27%, respectively, did not know that if they suddenly feel that way, they should eat some form of sugar (Williams, Baker, Parker, & Nurss, 1998). This is not esoteric knowledge but is absolutely basic for insulin-dependent patients knowing how to control blood glucose level on a daily basis. Among 402 patients taking daily medicine for hypertension, there were comparably large differences between the two literacy levels in knowledge of which blood pressure levels are high and which are normal. Such knowledge is essential because patients with hypertension are often expected to monitor their own blood pressure to make sure that it remains within safe limits.
The ORs for patients with inadequate literacy not knowing these sorts of facts about their disease ranged from 2.0 to 15.9 for those with diabetes and from 2.4 to 9.0 for those with hypertension (Gottfredson, 2002, p. 366).
However, simplifying treatment sufficiently to gain adherence from low-literate populations can lead to suboptimal therapeutic regimens. Such patient-driven simplification may explain the seeming failure of physicians to follow medical guidelines in some locales. For instance, a study in Philadelphia found that rates of prescriptions filled for suboptimal asthma drugs (bronchodilators) rather than the antiinflammatory drugs recommended for asthma (inhaled steroids, which do not produce an immediate response) were higher in zip code areas with lower average levels of education (Lang, Sherman, & Polansky, 1997). Education was the strongest demographic correlate of underprescription and underuse of recommended drugs (literacy was not measured).
Accidents are not visited on people randomly, nor are hazards evenly distributed to all occupations, ages, sexes, or locales. Most important here, the accident literature has clearly established that some individuals tend to have more accidents than others, even with the same level of exposure to the same hazards in the same environments (Boyle, 1980; Hale & Glendon, 1987, pp. 314 -316). Methodological constraints have made it difficult to identify what the pertinent individual differences are. Accident research has established, however, that the risk of accidents is higher among workers who have less knowledge or only a few months' or years' experience (after which time their risk plateaus) and when tasks are more complex, novel, or confusing (Boyle, 1980, p. 54; Hale & Hale, 1972). Recall that the same pattern was found for overall job performance.
Buffardi, Fleishman, Morath, and McCarthy (2000) recently confirmed that errors increase when tasks demand higher cognitive abilities. They found that error rates- human error probabilities (HEPs)- on work tasks in Air Force and nuclear power plant jobs generally correlated .50 to .60 with the number and level of cognitive abilities that the tasks required. A large study by AT&T estimated that it could reduce employee accidents by 17% and absences due to illness by 14% if it hired from the top 40% of applicants on an aptitude test (McCormick, 2001). And, as noted earlier, the Australian Veterans Health Studies found that IQ was the best predictor of motor vehicle deaths among veterans by age 40. Compared with men who were somewhat above average in IQ (IQ 100 -115), the MVA death rate for men of IQ 85-100 was twice as high (92.2 vs. 51.5 per 10,000), and for men of IQ 80 - 85 it was three times as high (146.7; O'Toole, 1990). (Like the United States, Australia does not induct anyone below about IQ 80 owing to low trainability.)
Of the 29 categories of unintentional death listed in Table 7, only one shows higher mortality at higher income levels (aircraft accidents, most of which involve personal planes), and the difference is only slight. Another four or five exhibit no clear social class gradient (falls, motorcycle and bicycle deaths, unintentional poisoning with solids or liquids, pedestrian-train accidents, and perhaps suffocation). For all the remainder-from choking on food, drowning, and dying in car crashes to accidental death by firearms, explosions, falling objects, natural disasters, and neglect-residence in lower income areas is associated with higher risk, and risk usually rises in a fairly regular manner down the income gradient. This breadth and monotonicity of the SES-accident relation replicates the generality of the SES- health relation for chronic disease ...Fires are seldom just accidental, however. Cigarettes are the most common cause (28%), and children playing with matches account for another 10% (S. P. Baker et al., 1992, pp. 162-163). Half of adult fatalities in house fires show evidence of high levels of blood alcohol (S. P. Baker et al., 1992, p. 164).
Advances in sanitation, medicine, ergonomics, health science, and much more have greatly reduced morbidity and mortality as well as increased options for how we live our life in developed nations. Even the poorest stratum of Americans has access to material goods and medical care that far exceeds what most people in the world today could ever hope for. But with each technological advance, some sectors of society benefit more than others. Triumph over the scourges of infectious disease and dire poverty has not equalized physical well-being. Technological and social advance greatly increase both the complexity of our life and the choices we have. Although we welcome more choice, both choice and complexity put a big premium on g. If we knew more about life's daily demands for continual learning, spotting of problems, and reasoning, especially in health self-care, we might know better how to structure environments, deliver services, and provide instruction. This might ease the burdens of complexity and promote wiser choices for everyone, but especially persons lower on the IQ continuum."
"Intelligence: Is it the epidemiologists' elusive 'fundamental cause' of social class inequalities in health?", Gottfredson 2004:
"Virtually all indicators of physical health and mental competence favor persons of higher socioeconomic status (SES). Conventional theories in the social sciences assume that the material disadvantages of lower SES are primarily responsible for these inequalities, either directly or by inducing psychosocial harm. These theories cannot explain, however, why the relation between SES and health outcomes (knowledge, behavior, morbidity, and mortality) is not only remarkably general across time, place, disease, and kind of health system but also so finely graded up the entire SES continuum. Epidemiologists have therefore posited, but not yet identified, a more general "fundamental cause" of health inequalities. This article concatenates various bodies of evidence to demonstrate that differences in general intelligence (g) may be that fundamental cause.
Conventional theories of social inequality posit that social class disparities in health result from disparities in material resources, such as access to medical care. Health demographers have become increasingly puzzled, however, by certain glaring failures of the poverty paradigm. The chief puzzle is why the relation between social class and health is so remarkably general across diverse times, places, and diseases and despite improvements in health care. In fact, greater equalization of health care and falling rates of morbidity and mortality tend to widen social class differences in health (Steenland, Henley, & Thun, 2002). Such paradoxes have led health demographers to posit some highly general and enduring- but still mysterious-fundamental cause of health inequalities that transcends the particulars of time, place, disease, material advantage, and social change (Adler et al., 1994).
I argue that g may be that fundamental cause because it meets six criteria that any candidate for the cause must meet: It has a stable distribution over time, is replicable (passed from one generation to the next), is a transportable form of influence, has a general (pervasive) effect on health, is measurable, and is falsifiable.
The second fact is that children's IQ predicts their later socioeconomic success better than do their parents' attributes. For instance, sons' education, occupation, and income correlate higher with their own IQ (true correlations of .68, .50, and .35) than they do with either their fathers' education (.43, .35, and .21) or occupation (.48, .44, and .29; Jencks et al., 1972, pp. 322, 337). The same predictive superiority of IQ over family background is found for the social pathologies, too (Herrnstein & Murray, 1994; Hirschi & Hindelang, 1977; see also Gordon, 1997). Third, most differences in both IQ and adult success occur among siblings in the same household, not between families (e.g., Jensen, 1980, p. 43, on IQ). Class-based theories of inequality do not discuss and cannot explain the large differences in life chances among siblings growing up in the same home, but g can. IQ differences among siblings produce essentially the same degree of inequality in adult success and pathology among the siblings as do comparable IQ differences among strangers (Murray, 1997, 1998; Olneck, 1977, pp. 137-138).
Why one person rather than another misreads a particular bus schedule on any particular day has many causes and is probably little related to individual differences in g. Nor is such misreading, by itself, likely to be particularly consequential. The crucial point, however, is that g's effects are pervasive and consistent. As gambling houses know well, even small odds in one's favor can produce big profits in the long term when they remain consistently in one's favor and other influences are more erratic. Information processing is involved in all daily tasks, even if only to a minor degree, so higher g always provides an edge, even if small. In contrast, other influences (fatigue, advice, etc.) tend to be more volatile and haphazard, and thus are likely to cancel each other out over time. As the NALS data illustrate, people with higher literacy (g) tend to perform better on all literacy tasks, whether they involve dealing with banks, restaurants, or social service agencies, deciphering financial options, or engaging rights and duties as a citizen. People who plan and budget somewhat less well will find themselves slowly slipping behind others who started with the same material resources. People who less often grasp information about their educational, employment, financial, and political options or who make more numerous or more serious mistakes in following the steps and filling out the forms necessary to implement them gradually foreclose opportunities they see others taking. When this slippage occurs in many realms of life, it compounds. When an influence is ubiquitous, it can therefore outweigh all others in the long run, even episodically stronger ones, that are less consistent or less consistently useful (help, contacts, extraverted personality, strong interest, etc.).
In fact, this is the statistical phenomenon that explains why adding more items to a mental test will increase its reliability. It also explains why even test items with only faint g loadings (meaning that g is only a small factor in correctly answering any particular one of them) can, when sufficient in number, yield a test that measures almost nothing but g (Lubinski & Humphreys, 1997, pp. 163-165).
Age-specific rates of illness and death are often two to three times higher for individuals in the lower social strata. For many years the so-called poverty paradigm has dominated thinking about why such disparities exist. Under this paradigm, the disparities are presumed to result from differences in access to health care and other such resources (Hummer, Rogers, & Eberstein, 1998). "Wealth Secures Health," as a recent headline in the APA Monitor put it (Clay, 2001). Social class is typically measured by level of education, occupation, income, or some combination thereof (Dutton & Levine, 1989). The poverty paradigm has foundered, however, on a growing number of contrary facts and vexing paradoxes, many of them pointed out decades ago (Kitagawa & Hauser, 1973; Syme & Berkman, 1976).
Prominent among the challenging facts is that the paradigm's key health resource- greater access to medical care- has surprisingly little relation to differences in health. The introduction of Medicaid and Medicare in the United States during the 1960s soon led to the poor making as many physician visits per year as the nonpoor, but large class differentials in health remained- even when the poor began to visit physicians at a higher rate than the nonpoor (Rundall & Wheeler, 1979, p. 397). Great Britain and other countries that had expected to break the link between class and health by providing universal health care were dismayed when the disparities in health not only failed to shrink but even grew (see The Black Report by Townsend & Davidson, 1982; also Link & Phelan, 1995, p. 86; Marmot, Kogevinas, & Elston, 1987, p. 132; Susser, Watson, & Hopper, 1985, p. 237).
It is now amply documented, first, that equalizing the availability of health care does not equalize its use. Perhaps most important, less educated and lower income individuals seek preventive health care (as distinct from curative care) less often than do better educated or higher income persons, even when care is free (Adler, Boyce, Chesney, Folkman, & Syme, 1993; Goldenberg, Patterson, & Freese, 1992; Rundall & Wheeler, 1979; Susser et al., 1985, p. 253; Townsend & Davidson, 1982, Chapter 4).
Second, greater use of medical care does not necessarily improve health (Marmot et al., 1987, p. 132; Valdez, Rogers, Keeler, Lohr, & Newhouse, 1985). To illustrate, when a large, federally funded, RAND-conducted, randomized controlled experiment tested the effects of subsidizing health care costs at different levels in six cities across the United States, participants with free care used more medical care than those with only partly subsidized care, but their health was no better after 2 years. Participants with free care had indiscriminately increased their use of inappropriate as well as appropriate care (Lohr et al., 1986, p. 72). Prenatal care provides another example that more care does not necessarily produce better outcomes, in this case for newborns (Fiscella, 1995).
Third, health depends more now than ever on private precaution and health lifestyle. The American Psychological Society (APS) noted in its 1996 Human Capital Initiative report on health (APS, 1996) that "seven of the 10 leading causes of death have aspects that can be modified by doing the right thing; that is, by making healthy choices about our own behavior" (p. 5) and that mortality "could be reduced substantially if people at risk would change just five behaviors: Adherence to medical recommendations (e.g., use of antihypertensive medication), diet, smoking, lack of exercise, and alcohol and drug use" (p. 15).
...The "paradox," Susser et al. (1985) noted, is that increased public health efforts at "prevention in many instances ha[ve] widened the disparity in health between the social classes," perhaps because "new preventive techniques have turned on personal behavior [e.g., not smoking] rather than on social engineering [e.g., controlling infectious disease by providing clean water and requiring immunizations for school entry]" (p. 254).
This puzzle is compounded by the "mystery of the SES- health gradient" (Adler et al., 1994, p. 22), which is that rates of illness and death are successively lower at successively higher rungs on the social ladder, and the monotonic decrease continues even among strata above the threshold of material resources required for adequate care. Moreover, the finely graded relation between class and health is found regardless of whether social class is measured by level of education, occupation, or income (Adler et al., 1993; Dutton & Levine, 1989), although education generally yields the tighter fit (e.g., Call & Nonnemaker, 1999, for adolescents' self-ratings of health; Cramer, 1987, for infant mortality; Hayward, Crimmins, Miles, & Yang, 2000, for 22 varied disabilities, diseases, and impairments among adults aged 51- 61; Pincus, Callahan, & Burkhauser, 1987, for 23 chronic diseases among adults aged 18 - 64). Although the SES- health relation often varies in strength, it is as clear in countries that provide universal health insurance as in those that do not, for diseases that are amenable to medical treatment as for those that are not, and for objective as well as subjective reports of illness (Adler et al., 1993, p. 3141; Dutton & Levine, 1989; Marmot et al., 1987).
Diseases involving different organ systems and with seemingly different etiologies nonetheless tend to be related in the same manner to social class. For example, a national sample of 10,538 adults aged 18 - 64 in 1978 yielded relative risk ratios between about 2.0 to 3.0 for cardiovascular, gastrointestinal, musculoskeletal, pulmonary, renal, and other diseases for adults with 1- 8 years of education compared with those with 12 years (ORs not shown, but calculated from Pincus et al., 1987, p. 870). Neoplastic (cancerous) and psychiatric illnesses were, respectively, less strongly and more strongly related to education level, the relevant ORs being 1.2 and 5.5 for adults with 1- 8 years of education. Relative risk for persons with more than 12 years of education was only about 0.7 for most of the diseases. All disease categories except neoplasms showed a dose-response relation (i.e., rates increased steadily at lower levels of education), and all diseases except neoplasms remained significantly related to education level after age, sex, race, and smoking were controlled for (Pincus et al., 1987). Even identical medical treatment for identical problems of equal severity yields SES-mortality gradients, as seen in a study of cancer survival in Boston (Syme & Berkman, 1976). The poverty paradigm has never developed a theory to explain the general relation it continues to describe (Hummer et al., 1998, p. 555; Mechanic, 1989, p. 20), perhaps partly because its different indicators of SES measure nothing obviously in common. Education is said to provide, among assorted other advantages, improved employment, gathering of information, health behavior, and negotiation of one's way through the health system, whereas income allows purchase of better health care, nutrition, transportation, and housing (Hummer et al., 1998, p. 560; Mechanic, 1989, pp. 10 22). Recent discussions of what the fundamental cause might be have included social support, social connectedness, social anxiety, chronic stress (allostatic load), sense of personal control or mastery, experience of control, self-esteem, nutrition, relative deprivation, stigmatization, self-perceived social status, resistance resources, coping strategies, and intrinsic problem-solving capacities (Adler et al., 1994; Adler & Snibbe, 2003; Clay, 2001; Dutton & Levine, 1989; Link & Phelan, 1995; Marmot et al., 1987; Pincus et al., 1987; and many papers in Adler, Marmot, McEwen, & Stewart, 1999). To my knowledge, none has been shown plausible for explaining the full pattern- especially the generality and occasional reversal- of class disparities across time, place, and disease.
Certain possibilities nonetheless seem to have been peremptorily dismissed. With few exceptions (e.g., Mechanic, 2000), any suggestion that class disparities in health may be due in part to genetic-based individual differences is today disapproved as blaming the victim (e.g., Cramer, 1995, p. 234) or "absolv[ing] the social structure of responsibility" (R. Wilkinson, 1996, p. 63).
Moreover, the association of knowledge with social class seems to be stronger when the information in question is more widely publicized by the mass media (Tichenor et al., 1970). This so-called knowledge-gap phenomenon was replicated yet again in three recent mass media campaigns on environmental issues in the Netherlands (Weenig & Midden, 1997). That study also found that diffusion was "remarkably similar" (p. 955) for all three issues, that it was characterized by a monotonic, negatively accelerated growth curve, and that the less educated groups "showed no signs at all of catching up" (p. 951) with the better educated. Feldman (1966) looked at health knowledge in particular and characterized his national survey as a case study in self-directed "adult learning" (p. 3) because "the mass media are the primary source of much of what people know about diseases" (p. 94; see also p. 136). He also noted that as the public had become better informed over time (e.g., about the warning signs of cancer), knowledge was not equalized because the previously informed had now become more fully informed. He found (Feldman, 1966, p. 116) that health knowledge is influenced to some extent by interest, gender, and age, after education is controlled, but surprisingly little by personal concern and experience with disease. Instead, he reported, education "is by far the strongest correlate" of knowledge (Feldman, 1966, p. 109). Both occupation and income, once again, were weaker correlates than education (e.g., p. 105). Education probably operates here mostly as a surrogate for g, because Beier and Ackerman (2003) found that knowledge of 10 different kinds of widely available health information (e.g., reproduction, aging, nutrition, safety) formed "one dominant factor" (p. 441), which in turn correlated about .90 (p. 443) with the g factor they derived from seven mental tests. Neither personality nor self-reported level of health knowledge had much relation to actual level of knowledge, and an education-income composite added nothing to its prediction after g was controlled for.
Feldman (1966, p. 97) also reviewed evidence that those who knew most before exposure to new information also gained most from the new exposure, so disparities in knowledge remained or grew. Education-related relative risk was higher for the less educated when the public as a whole was better informed about a disease. For example, in 1955, 48% of the public could name at least one symptom of diabetes, 62% could name at least one symptom of cancer, and 69% could name at least one symptom of polio (Feldman, 1966, p. 90). However, the relative risk of persons with 0 - 8 years of education (compared with those with 9 -12 years) not being able to name even one symptom was successively higher for the better known diseases-respectively, 1.7, 3.4, and 4.4 (ORs calculated from data in Feldman, 1966, p. 102). Moreover, the risk gradient for ignorance of the signs of cancer had steepened between 1945 and 1955, from 2.3 to 3.4 for the least educated, as more citizens had learned its signs (Feldman, 1966, p. 121).
It is very important to note that Feldman (1966, pp. 140 -148) also provided evidence that exposure is not just passive but that more educated people seek out and attend to more information, which is indicated by their more extensive use of newspapers, magazines, and books. NALS research shows similar differences in self-exposure (Kirsch & Jungeblut, n.d., p. 53; Kirsch et al., 1993, pp. 138 -140). This is exactly the double-barreled way that higher g promotes more learning-it increases exposure to learning opportunities and then allows for their fuller exploitation (cf. Rodgers et al., 1994; Rowe, 1997, on passive learning theory). That adopting health innovations involves active learning rather than just passive exposure accords with evidence that patients adopt birth control (Behrman, Kohler, & Watkins, 2002) and physicians adopt new antibiotics (Burt, 1987) more because of social learning and professional decision making than mere exposure to social influence.
...Health literacy researchers generally eschew any notion of intelligence for fear of inviting offensive distinctions onto the low-literate indigent and minority populations they study. There are several reasons, however, to believe that their measures are moderately to highly g loaded. First, the TOFHLA correlates highly with other tests of health literacy, such as the Wide Range Achievement Test-3 (.74; Parker et al., 1995), which in turn correlate moderately well with Full-Scale IQ (.53) and the Verbal Scale (.63) on the Wechsler Adult Intelligence Scale (WAIS; G. S. Wilkinson, 1993, p. 180). Second, TOFHLA literacy behaves in key ways like functional literacy and work literacy, which clearly are mostly g. For instance, like both the latter, the TOFHLA samples a wide variety of tasks that adults are routinely expected to perform, and comprehension is not improved by presenting tasks in oral rather than written form. Third, health literacy researchers were just as surprised as NALS researchers to discover that literacy is very general. They, too, soon concluded that low literacy reflects "limited problem-solving abilities" and began describing literacy as the "ability to acquire new information and complete complex cognitive tasks" (D. W. Baker, Parker, Williams, & Clark, 1998, pp. 796 -797).
A fourth sign that health literacy is largely g is seen in the strategies that health practitioners use to render health communications more comprehensible to low-literacy patients (Doak, Doak, & Root, 1996). Those strategies mirror the guidelines for simplifying written materials that were developed by Army researchers seeking to enhance the work literacy of low ability soldiers (Sticht, 1975). They constitute, in effect, a primer for reducing the complexity and information content of a communication. For example, omit all nonessential information; describe the specific behavior required of the individual; use simple vocabulary; require reading no higher than the fifth grade level; use simple line drawings (photographs contain distracting, irrelevant information); use several headings, arrows, or the like to summarize or draw attention to the most important pieces of information; and limit the number of type fonts and colors to minimize distraction. That is, provide no theory, require no inferences, provide only the bare minimum of information that must be understood to produce the desired behavior, and eliminate all else on the page that might distract rather than draw attention to it. Recall that degree of inference required, number of pieces of information used, and embeddedness in irrelevant and distracting material were all core elements of processing complexity in the NALS items.
Noncompliance or nonadherence to medical regimens has long vexed medical and health workers. Prescription drugs provide an example.
"Over half of the 1.8 billion prescriptions written annually are taken incorrectly by patients. . . . Because they are used improperly, an estimated 30 -50 percent of all prescriptions fail to produce desired results. . . . Approximately 10 percent of all hospitalizations and 23 percent of all nursing home admissions are attributed to a patient's inability to manage or follow drug therapy." (Berg, Dischler, Wagner, Raia, & Palmer-Shevlin, 1993, p. S5)
Worse yet, one study estimated that almost 30% of patients were taking their medication in a manner that seriously threatened their health (Roter et al., 1998).
Noncompliance of all sorts is particularly a problem in lowincome clinic populations, where rates frequently exceed 60% (Becker & Maiman, 1975, p. 10). Expense is seldom a barrier, but regimen complexity is (e.g., Berg et al., 1993, p. S8; Cameron, 1996; Dodrill, Batzel, Wilensky, & Yerby, 1987; Schulz & Gagnon, 1982). Noncompliance can impose high costs in morbidity and mortality, as exemplified in studies of death from myocardial infarction among heart patients in treatment (OR of 2.4 for poor adherence; Gallagher, Viscoli, & Horwitz, 1993). The problem here, then, is not lack of access to care but the patient's failure to use it effectively when delivered.
...Passing rates on even the simplest tasks tend to be low: 26% of the 2,659 patients did not understand information about when a next appointment was scheduled, 42% did not understand the directions for taking medicine on an empty stomach, and 60% did not understand a standard informed consent document (Williams et al., 1995). Relative risk of failing the TOFHLA items rose among the less literate groups, and the risk gradients were much steeper for the more complex tasks. ORs ranged from 6.0 to 70.5 for patients with inadequate literacy when compared with those with adequate literacy...Yet patients with low literacy still have shockingly low rates of knowledge about the most basic symptoms of their disease- ones, moreover, that often require them to take immediate action. For example, among 114 diabetics taking insulin daily, fully half of those with inadequate literacy but only 6% of those with adequate literacy did not know that feeling sweaty, nervous, and shaky is usually a sign that their blood glucose level is low. About 62% versus 27%, respectively, did not know that if they suddenly feel that way, they should eat some form of sugar (Williams, Baker, Parker, & Nurss, 1998). This is not esoteric knowledge but is absolutely basic for insulin-dependent patients knowing how to control blood glucose level on a daily basis. Among 402 patients taking daily medicine for hypertension, there were comparably large differences between the two literacy levels in knowledge of which blood pressure levels are high and which are normal. Such knowledge is essential because patients with hypertension are often expected to monitor their own blood pressure to make sure that it remains within safe limits.
The ORs for patients with inadequate literacy not knowing these sorts of facts about their disease ranged from 2.0 to 15.9 for those with diabetes and from 2.4 to 9.0 for those with hypertension (Gottfredson, 2002, p. 366).
However, simplifying treatment sufficiently to gain adherence from low-literate populations can lead to suboptimal therapeutic regimens. Such patient-driven simplification may explain the seeming failure of physicians to follow medical guidelines in some locales. For instance, a study in Philadelphia found that rates of prescriptions filled for suboptimal asthma drugs (bronchodilators) rather than the antiinflammatory drugs recommended for asthma (inhaled steroids, which do not produce an immediate response) were higher in zip code areas with lower average levels of education (Lang, Sherman, & Polansky, 1997). Education was the strongest demographic correlate of underprescription and underuse of recommended drugs (literacy was not measured).
Accidents are not visited on people randomly, nor are hazards evenly distributed to all occupations, ages, sexes, or locales. Most important here, the accident literature has clearly established that some individuals tend to have more accidents than others, even with the same level of exposure to the same hazards in the same environments (Boyle, 1980; Hale & Glendon, 1987, pp. 314 -316). Methodological constraints have made it difficult to identify what the pertinent individual differences are. Accident research has established, however, that the risk of accidents is higher among workers who have less knowledge or only a few months' or years' experience (after which time their risk plateaus) and when tasks are more complex, novel, or confusing (Boyle, 1980, p. 54; Hale & Hale, 1972). Recall that the same pattern was found for overall job performance.
Buffardi, Fleishman, Morath, and McCarthy (2000) recently confirmed that errors increase when tasks demand higher cognitive abilities. They found that error rates- human error probabilities (HEPs)- on work tasks in Air Force and nuclear power plant jobs generally correlated .50 to .60 with the number and level of cognitive abilities that the tasks required. A large study by AT&T estimated that it could reduce employee accidents by 17% and absences due to illness by 14% if it hired from the top 40% of applicants on an aptitude test (McCormick, 2001). And, as noted earlier, the Australian Veterans Health Studies found that IQ was the best predictor of motor vehicle deaths among veterans by age 40. Compared with men who were somewhat above average in IQ (IQ 100 -115), the MVA death rate for men of IQ 85-100 was twice as high (92.2 vs. 51.5 per 10,000), and for men of IQ 80 - 85 it was three times as high (146.7; O'Toole, 1990). (Like the United States, Australia does not induct anyone below about IQ 80 owing to low trainability.)
Of the 29 categories of unintentional death listed in Table 7, only one shows higher mortality at higher income levels (aircraft accidents, most of which involve personal planes), and the difference is only slight. Another four or five exhibit no clear social class gradient (falls, motorcycle and bicycle deaths, unintentional poisoning with solids or liquids, pedestrian-train accidents, and perhaps suffocation). For all the remainder-from choking on food, drowning, and dying in car crashes to accidental death by firearms, explosions, falling objects, natural disasters, and neglect-residence in lower income areas is associated with higher risk, and risk usually rises in a fairly regular manner down the income gradient. This breadth and monotonicity of the SES-accident relation replicates the generality of the SES- health relation for chronic disease ...Fires are seldom just accidental, however. Cigarettes are the most common cause (28%), and children playing with matches account for another 10% (S. P. Baker et al., 1992, pp. 162-163). Half of adult fatalities in house fires show evidence of high levels of blood alcohol (S. P. Baker et al., 1992, p. 164).
Advances in sanitation, medicine, ergonomics, health science, and much more have greatly reduced morbidity and mortality as well as increased options for how we live our life in developed nations. Even the poorest stratum of Americans has access to material goods and medical care that far exceeds what most people in the world today could ever hope for. But with each technological advance, some sectors of society benefit more than others. Triumph over the scourges of infectious disease and dire poverty has not equalized physical well-being. Technological and social advance greatly increase both the complexity of our life and the choices we have. Although we welcome more choice, both choice and complexity put a big premium on g. If we knew more about life's daily demands for continual learning, spotting of problems, and reasoning, especially in health self-care, we might know better how to structure environments, deliver services, and provide instruction. This might ease the burdens of complexity and promote wiser choices for everyone, but especially persons lower on the IQ continuum."