Research on Determinants of Women’s Health
Improvements in women’s health require an understanding of the determinants of disease, functioning, and well-being and the capacity to intervene in connection with the determinants. Intervention can occur at any level from cells to communities. Some determinants are linked to specific disorders; others have broad effects. Addressing common determinants of multiple diseases increases the potential for a greater overall influence on women’s health.
Several models have been developed to illustrate determinants of population health. Although they are not specific to women, they are useful for describing the variety of factors in women’s health. Models of determinants of health have generally distinguished individual-level characteristics (such as biologic and physiologic factors and health-related behaviors) from the broader determinants (such as environmental and social determinants) in which the individual-level characteristics develop and are expressed. There is some variability among models with regard to labeling determinants of health and the organizing frameworks for determinants (Dahlgren and Whitehead, 1991; Evans and Stoddart, 1990; IOM, 2000a), but in general, biologic and physiologic, or “downstream,” determinants of health are identified as modifiable through complex pathways by proximal determinants (such as drugs, surgical interventions, and health behaviors) and “upstream” determinants (such as social and economic policies).
To organize its review of research on the determinants of women’s health, the committee adopted a model of health determinants similar to that described in The Future of the Public’s Health in the 21st Century (IOM, 2002a). Adapted from Dahlgren and Whitehead (1991), the model (see Figure 2-1) is consistent with this committee’s approach that determinants of health encompass biologic, behavioral, and social factors. In addition this model acknowledges the interac
social, family, and community relationships and networks;
living and working conditions; and
societal, economic, cultural, and environmental policies and conditions.
Some determinants may operate at more than one level, and most health outcomes will be related to determinants from more than one level of the model. The model is consistent with the committee’s belief that quality of life is a particularly important component of women’s health. The determinant-based framework has advantages over a strictly disease-based framework in that it more readily allows consideration of functioning, wellness, and quality of life in addition to the understanding, detecting, and treating of diseases. It also allows discussion of interventions that can occur at the individual, community, and population levels, and how determinants are related to health across a woman’s life span.
This chapter presents evidence of the impact of a number of behavioral factors (smoking, eating habits and physical activity, sexual risk behavior, and alcohol use), social and community factors (violence against girls and women, social connections and stress, and social disadvantage), and societal factors (cultural factors and health care) that affect women’s health. Those factors are discussed as examples of factors that affect women’s health and should not be considered a comprehensive list of determinants of women’s health. Biologic determinants are discussed in Chapter 3 in the context of their roles in specific diseases.
In the last 20 years, there has been substantial progress in understanding how behavior affects people’s health, including the health of women and girls. Research has identified modifiable risk factors for a variety of health outcomes and has led to a better understanding of the level at which behavior leads to improvements in or deterioration of health. Human behavior is one of the biggest contributors to death and disease (McGinnis and Foege, 1993; McGinnis et al., 2002). With respect to US women, for example, substantial numbers of deaths have been attributed to smoking, physical inactivity, and dietary factors, which are preventable (Figure 2-2). As discussed in the Institute of Medicine (IOM) report Health and Behavior: The Interplay of Biological, Behavioral, and Societal Influences, the extent to which a given behavior affects health varies during the course of life (IOM, 2001a), and life span and stage of development are important to consider. Several IOM reports have also discussed strategies for modifying behavioral norms to improve the health of specific populations (IOM, 2000b, 2005a,b, 2010). This section discusses research on behavioral factors that are major contributors to morbidity and mortality among women: smoking, eating habits and physical activity, sexual risk behavior, and alcohol use.
The US surgeon general issued reports outlining the state of the evidence on the health consequences of smoking and progress in understanding the factors that influence smoking by women and girls in 1980 (HHS, 1980) and 2001 (HHS, 2001). Hundreds of observational studies conducted since the 2001 surgeon general’s report have been published and have added substantially to the knowledge base on smoking by women and girls and have helped to improve women’s health. In 2007, about one-fifth of US women 18 years old or older were current smokers1 (CDC, 2008a). Smoking has been declining in both women and men in recent decades, but the rate of decline has been slower in women. Similarly, declines in lung-cancer deaths have been slower in women than in men.
Smoking is strongly associated with socioeconomic status (Dube et al., 2009). The prevalence of smoking is over 3 times as high in women who have 9–11 years of education (33.6%) as in women who have an undergraduate degree
(9.7%). The difference by education is even greater in pregnant women: 25.5% in pregnant women who have 9–11 years of education vs 2.2% in college graduates. Similarly, smoking is higher in women who live below the poverty level (31.7%) than in those living at or above the poverty level (17.0%). American Indian or Alaskan native women are more likely to smoke (22.4%) than women who are white (20.6%), black (17.8%), or Hispanic (10.7%); Asian American or Pacific Islanders (4.7%) are the least likely to do so (Dube et al., 2009).
Health Consequences of Smoking
Research has painted an increasingly bleak picture of the health consequences of smoking in women and girls. As is true for the US population as a whole, tobacco use is the leading cause of preventable death in women. Smoking substantially increases women’s risk of a number of cardiovascular outcomes, including coronary heart disease and stroke (Bermudez et al., 2002; Kawachi et al., 1993; Stampfer et al., 2000). In women who smoke and use oral contraceptives, the risk of heart attack is even greater; the risk of heart attack is increased by as much as a factor of 30 and the risk of stroke by a factor of 3 compared with the risk in nonsmokers who use oral contraceptives (Burkman et al., 2004). Smoking is the major cause of lung cancer in women, with about 80% of lung cancers in women attributable to smoking. In 1987 lung cancer surpassed breast cancer to become the leading cause of cancer deaths in women (HHS, 2004; RWJF, 2009). Observational studies have established that smoking also increases the risk of cancers of the larynx, oral cavity and esophagus, stomach, bladder, kidneys, and pancreas (HHS, 1980, 2001, 2004). For women specifically, smoking results in increased risk of cancers of the cervix and vulva and of such gynecologic and reproductive complications as menstrual problems, reduced fertility, and premature menopause (Gold et al., 2001; Laurent et al., 1992; Luborsky et al., 2003). Postmenopausal women who smoke experience accelerated loss of bone mass, which may put them at increased risk for osteoporosis and hip fracture (HHS, 2001, 2004; Law and Hackshaw, 1997).2 Smoking during pregnancy can result in placental abruption and previa (CDC, 2007a; HHS, 2001; RWJF, 2009).
Research on Smoking Initiation, Prevention, and Interventions
Since the late 1960s the tobacco industry has targeted women and girls by using specific cigarette brands and marketing techniques (RWJF, 2009).3 The US surgeon general’s reports on women and smoking and more recent reports by the
National Cancer Institute, the Robert Wood Johnson Foundation, and others have described tobacco companies’ use of themes of associations between smoking and social desirability, independence, and weight control to target adolescent girls, who are at an impressionable age (HHS, 1980, 2001; NCI, 2008; RWJF, 2009). Such targeted marketing has been identified as a reason that lung cancer–death rates initially increased and then have been slower to decline in women than in men in recent years (Jemal et al., 2008; RWJF, 2009).
The large majority of women who smoke, as with men, began doing so during adolescence; this is cause for concern because risks of many of the health consequences of smoking are a function of the duration (years smoked), in addition to intensity (cigarettes per day) of use (Flanders et al., 2003; Hegmann et al., 1993; Terry and Rohan, 2002). Most interventions to prevent smoking initiation, therefore, have targeted adolescents. Interventions have included school-based or health-care–based educational and informational programs, environmental and policy change interventions that restrict tobacco advertising and youth access to tobacco products, smoking bans, and taxation of tobacco products (IOM, 2009a). Several interventions have been found to reduce youth smoking but, as discussed in a report of the surgeon general in 2001, little systematic effort has been focused on developing and evaluating prevention interventions specifically in girls (HHS, 2001), and more research on differences in smoking cessation for girls and boys is needed (Thorner et al., 2007).
The Department of Health and Human Services report Treating Tobacco Use and Dependence: 2008 Update concluded that women benefit from the same interventions as men but that the data are mixed as to whether they benefit by the same magnitude. The smoking quit ratio—proportion of ever smokers who are now former smokers—has increased in both men and women, but women have consistently had lower quit ratios (Gritz et al., 1996). Several factors are associated with poorer cessation outcomes in women and girls: being less ready to stop smoking; being more addicted to cigarettes, as indicated by the smoking of more cigarettes per day; having less confidence in resisting temptation; having less social support; and socioeconomic disadvantages (being unemployed and having less education and lower employment) (HHS, 2001). Psychosocial interventions—including telephone counseling, individually tailored followup, and advice to quit geared toward children’s health—are effective in women smokers (HHS, 2008a). Weight gain is associated with smoking cessation (Caan et al., 1996; Flegal et al., 1995), and is often of concern to women (Copeland et al., 2006). There is some evidence that exercise is effective in reducing weight gain after smoking cessation in women, but the findings are not consistent (HHS, 2008a).
Several pharmacologic aids have been developed in the last 25 years to help smokers quit and to help prevent relapse by reducing cigarette cravings and withdrawal symptoms. A few trials have compared the benefit of the aids in women and men. In the overall population, nicotine-replacement therapies (NRTs), such
as the nicotine patch and chewing gum, double the odds of quitting smoking relative to placebo (Silagy et al., 2004). Although NRTs appear to lead to higher cessation rates in women than placebo especially when combined with cessation counseling (Reynoso et al., 2005), there is some evidence that NRTs are more efficacious in men than in women. In part because of concerns about exposure of the fetus to NRTs, few studies have tested NRTs in pregnant women (see for example, HHS, 2008a; Schnoll et al., 2007).
Pregnancy appears to be a time of high motivation for many women to quit smoking, but relapse often occurs after birth (Reichert et al., 2004). In the health-care setting, multipronged psychosocial interventions (for example, a combination of pregnancy-specific self-help materials and counseling with a health educator) have been found to be significantly effective in getting women to quit smoking during pregnancy. Psychosocial interventions for postpartum abstinence from smoking had positive but nonsignificant effects (HHS, 2008a). Spousal support for quitting, including the spouse’s own change in smoking, is particularly helpful (HHS, 2004). Partner smoking is associated with continued smoking by women during pregnancy; this suggests the need for partner-focused interventions along with interventions for pregnant women themselves (DiClemente et al., 2000).
Eating Habits and Physical Activity
The prevalence of obesity, defined as a body-mass index (BMI) of 30 or more, in the United States has more than doubled in the last 3 decades, with increases seen in women, men, and children (Flegal et al., 2002; Mokdad et al., 1999; Ogden et al., 2006; Sturm, 2003). More than one-third of US adults were obese and more than two-thirds of adults were either obese or overweight (BMI 25–29.9) in 2007–2008 (Flegal et al., 2010). Class 3 or extreme obesity, which has been defined as a BMI of 40 or more, is associated with an increased risk of all-cause mortality and comorbidities. The prevalence of Class 3 obesity more than doubled in women between the early 1990s and 2000, and in 2000 was 2.8%, which was about twice as high as in men. The prevalence was highest in black women (6%) and those without a high-school education (Freedman, 2002). Recent data from the National Health and Nutrition Examination Survey (NHANES) provide some evidence that the rate of increase in obesity is slowing, particularly in women. Unlike the increases seen between 1976–1980 and 1988–1994, and between 1988–1994 and 1999–2000, “the prevalence of obesity showed no statistically significant changes over the 10-year period from 1999 through 2008” (Flegal et al., 2010).
Eating habits and physical activity are the primary drivers of weight; over-consumption of calories and insufficient physical activity are fueling high rates of people who are overweight or obese (Patrick et al., 2004; Weinsier et al., 1998). Calorie consumption has increased over the past 4 decades, in part from larger
portion sizes, increased consumption of high-sugar and high-fat foods, increased consumption of high-calorie and low-nutrient food and beverages (for example, sodas), and increased eating out (Levi et al., 2009). Over the past 5 decades, physical activity has decreased with Americans walking less, having less time to exercise in part from longer working hours and longer commutes, and reduced physical demands of work, household management, and travel (Levi et al., 2009). In addition, the built environment4 can “facilitate or constrain physical activity” (NRC, 2005), and many people live in areas that facilitate driving rather than walking, or in areas where parks and recreational facilities are not considered safe (Levi et al., 2009).
Substantial headway has been made over the last 20 years in understanding how eating habits and physical activity affect the health of women. Whereas prior evidence came largely from studies of men, several large US cohort studies of women—such as the Nurses’ Health Study, the Women’s Health Initiative (see Box 2-1 for a brief description), the Women’s Health Study, and the Black Women’s Health Study—have resulted in a vast literature on the roles of eating habits and physical activity in women’s health (Hu et al., 2001; Martinez et al., 1997). That research has informed the design of interventions to increase physical activity and improve eating habits in women and girls.
Much more is known about determinants of being overweight and obese, but there is still a lack of effective interventions. Although the rate of increase in obesity has decreased, the prevalence of obesity doubled in the last 2 decades (Flegal et al., 2002; Mokdad et al., 1999; Sturm, 2003); in 2001–2004, more than one-third of US adults were obese (BMI, over 30), and two-thirds were overweight or obese (BMI, 25–29.9) (Ogden et al., 2006).
According to a self-report survey of adults conducted in 2007, 40% of US women did not meet the 2008 Physical Activity Guidelines for Americans for adequate physical activity (CDC, 2008b),5 despite the documented health benefits of physical activity. The eating habits of women in the United States are also far from optimal. For example, less than one-third of women in 2005 met recommendations to eat five or more servings of fruits and vegetables per day (CDC, 2007b). It is a reflection of the poor diets and lack of physical activity of most
General Description of Women’s Health Initiative
The Women’s Health Initiative (WHI) was a multi-million-dollar, large prospective clinical study coordinated by the National Heart Lung and Blood Institute. Enrollment of 68,132 postmenopausal women ages 50 to 79 ran from 1993 to 1998. To address some research questions, women were randomized to various studies: (1) a dietary modification arm, which assigned 48,835 women to follow a 20% low-fat eating plan or self-selected diet; (2) a hormone therapy arm consisting of conjugated equine estrogen-plus-progestin or placebo for 16,608 women with uterus; or (3) another hormone therapy arm consisting of conjugated equine estrogen-only or placebo for 10,739 women with a hysterectomy. Other WHI studies included 8,050 women who followed both the dietary modification and a hormone therapy, and a calcium and vitamin D study that started 1 year later and included 36,282 of the women. In addition, 93,676 women from the same population agreed to be in an observational study (Prentice and Anderson, 2008).
women that over 60% of women 20 years old and older are overweight or obese (Ogden et al., 2006).6 This can have serious effects on subsequent generations as studies have shown that a child with an obese parent is 60% as likely to become an obese adult (IOM, 2005b; Whitaker et al., 1997).
A greater proportion of women than men are obese; the difference is greatest among some racial and ethnic populations (see Figure 2-3) (CDC, 2009a). Non-Hispanic black women (about 53%) and Mexican-American women (about 42%) are more likely to be obese than non-Hispanic white women (about 32%). The percent of obese non-Hispanic Black women is higher (about 61%) in women 60 years old and older (Pan et al., 2009).
With some consistency, studies have shown that women engage in less physical activity than do men and that activity declines with age and is lower in nonwhite women (Biddle and Mutrie, 2008). Both girls and women have been found to engage in leisure-time physical activity,7 such as sports and recreational
activities, less often than their male counterparts, and there is a substantial decrease in activity in girls during adolescence (Kimm et al., 2002; Sallis et al., 2000; Trost et al., 2002).8 There is some evidence that girls become less active during adolescence because of shifting self-perceptions associated with pubertal development, heightened awareness of peers, and changes in self-esteem (Kimm et al., 2001; Murdey et al., 2004).
Findings depend on the type of physical activity assessed. For instance, although older women engage in fewer sports and less planned exercise than men,
reported activity levels may increase when household and caregiving activities are considered (Sternfeld et al., 2000). White women and those with more education and income, who generally have more resources and flexibility, are more likely to engage in leisure activities, whereas other groups of women are more likely to be classified as physically active when occupational activity, household activity, and walking for transportation are considered as physical activities (Brownson et al., 2000; Eyler et al., 2002; Sternfeld et al., 1999; Young and Cochrane, 2004).
Childbearing and motherhood, especially the early years of raising children, have been identified as common barriers to regular physical activity in women 25–35 years old (Cramp and Brawley, 2006). Whereas in the past it was recommended that pregnant women limit strenuous exercise or stop altogether (ACOG, 1985), today, moderate exercise has been established as safe for healthy women and has been shown to reduce gestational diabetes and pre-eclampsia and to help in preventing excess maternal weight gain (Clapp and Little, 1995; Dempsey et al., 2004a; Saftlas et al., 2004; Sorensen et al., 2003). Because of a lack of dissemination of that evidence, however, some women may remain uncertain about the safety of exercise during pregnancy (Mudd et al., 2009). Indeed, evidence from both retrospective and prospective studies shows that intensity and duration of leisure-time physical activity are lower during pregnancy than before it and lower in the third trimester than in the first (Poudevigne and O’Connor, 2006). There appear to be smaller decreases in household and caregiving activities during pregnancy (Mottola and Campbell, 2003; Schmidt et al., 2006; Taber-Chasan et al., 2007). One of the strongest determinants of whether a woman will be active during pregnancy is her level of activity during the year before pregnancy; inactivity during pregnancy is more common among multiparous women, especially those who are economically disadvantaged or who have less education (Ning et al., 2003; Poudevigne and O’Connor, 2006).
In recent years, there has been wide use of ecologic models that emphasize social and environmental influences on physical activity in addition to individual-level influences (Biddle and Mutrie, 2008; King et al., 2002; Spence and Lee, 2003). Evidence from mostly cross-sectional studies shows that attributes of the neighborhood environment—such as availability of recreational facilities and parks, low crime rates, seeing others exercise, less traffic, sidewalks, and street lighting—are associated with more physical activity (Duncan et al., 2005; Humpel et al., 2004; Owen et al., 2004). A recent dissertation on the effect of elementary school policies on physical activity and obesity in children found that the presence of a gymnasium in a school is associated with more time in physical education class and that children from disadvantaged backgrounds are less likely to have a gymnasium, but it did not find a significant correlation between presence of a school gymnasium and rates of being overweight or obese (Fernandes, 2010). Having the recommended time for recess and physical education was associated with a decrease in BMI for boys, but not for girls (Fernandes, 2010).
Having a high quality diet means taking in adequate amounts of healthy foods, including fruits and vegetables,9 and limiting intake of unhealthy foods, such as fats and sugar (HHS and USDA, 2005). Ingestion of energy (or calories) via consumption of carbohydrates, proteins, and fats in the diet is necessary to maintain body functions such as respiration, circulation, physical activity, and protein synthesis (IOM, 2005c). Women eat more fruits and vegetables than men10 and are more aware of recommendations for fruit and vegetable intake and of the links between fruit and vegetable consumption and disease prevention (Baker and Wardle, 2003; Wardle et al., 2004). Sex differences in fruit and vegetable consumption in adolescents have been less clear, although where differences have been noted, consumption has been higher in girls (Cooke and Wardle, 2005; Rasmussen et al., 2006). Other determinants positively associated with fruit and vegetable consumption in adults include higher socioeconomic status (SES), being married, and having good local availability (Kamphuis et al., 2006). Among the determinants consistently found to influence fruit and vegetable intake in children and adolescents are SES, preferences, parental eating habits, and home availability and accessibility (Rasmussen et al., 2006). Availability of fruits and vegetables in schools has the potential to increase consumption in youth, but only about one-fifth of US middle and high schools offer fruit and vegetables as competitive foods (CDC, 2009b).
Minorities, including minority-group women and girls, on the average have diets that are lower in quality and nutrient intake than non-minorities (Neumark-Sztainer et al., 1998; Xie et al., 2003). Neighborhood SES may play a role, as indicated by findings that residents of lower-SES neighborhoods have poorer fruit and vegetable availability and an overabundance of fast and convenience food (Bodor et al., 2008; Morland et al., 2002).11 Relative costs of healthy and unhealthy foods and cultural differences in food choices and methods of food preparation may also play some role in differences in diet quality in populations of women. Research involving Latina women has found that acculturation into mainstream culture in the United States is associated with a degradation of diet
quality, including lower intake of fruits and vegetables and higher consumption of fats and drinks that contain refined sugar, an effect often modified by SES (Perez-Escamilla and Putnik, 2007).
Family-level factors are among the strongest influences on food choices and eating behavior in children, including girls. Much research has documented similarities between parents and their children in food acceptance and preferences for foods, such as fruits and vegetables (Patrick and Nicklas, 2005), as well as in physical activity and other factors that decrease the risk of weight-related problems (Neumark-Sztainer, 2005). During adolescence, peers can be especially influential in adolescent eating behavior, particularly in girls, who, in general, have more body-image and weight concerns than their male peers. Lieberman et al. (2001) found that adolescent girls’ eating behavior was strongly predicted by peer pressure even after controlling for other interpersonal variables. There is also evidence that public policies, such as taxing unhealthful food and beverages, such as pizza and soda, could help improve eating habits (Duffey et al., 2010).
Health Consequences Related to Eating Habits and Physical Activity
Findings of hundreds of studies on the protective and detrimental effects of dietary exposures have added to the knowledge base on how eating habits affect women’s health, including the increasingly common problem of obesity. Trans fatty acids and saturated fats, high-glycemic-index foods, red meat, and a Western dietary pattern, to name a few examples, have been found to increase the incidence of coronary heart disease in studies involving women, whereas such foods as fruits and vegetables, nuts, and omega-3 fatty acids have been shown to reduce the risk of coronary heart disease (Mente et al., 2009). Excess body fat is probably the most important determinant of type 2 diabetes in women and men (Hu et al., 2001); however, there is some evidence from large observational studies of women that, even after controlling for BMI, women who consume a high-fiber, low-glycemic-index diet are less likely to develop type 2 diabetes (Hu et al., 2001; Liu et al., 2000; Salmeron et al., 1997).
Physical activity has been shown to reduce the incidence of cardiovascular conditions in women, including coronary heart disease and stroke (Ellekjaer et al., 2000; Hu et al., 2000, 2004; Li et al., 2006; Manson et al., 1999; Oguma and Shinoda-Tagawa, 2004; Stevens et al., 2002; Weinstein et al., 2008; Wessel et al., 2004). For example, the Nurses Health Study showed the health benefits of brisk walking, including an association between walking briskly for 3 or more hours per week and a reduced risk of coronary heart disease (Manson et al., 1999). Several publications on physical activity and type 2 diabetes in women have reported significant inverse associations between physical activity and incident diabetes (Folsom et al., 2000; Hsia et al., 2005; Hu et al., 1999, 2003; Jeon et al., 2007; Weinstein et al., 2004). Moreover, evidence is mounting that physical activity before and during pregnancy can reduce the chances that a woman will
develop gestational diabetes mellitus during pregnancy (Dempsey et al., 2004b; Oken et al., 2006; Zhang et al., 2006). In addition to reducing colon cancer (Chao et al., 2004; Martinez et al., 1997; Wolin et al., 2007), physical activity may help to reduce risk of premenopausal and postmenopausal breast cancer and endometrial cancer in women (AICR, 2007; Maruti et al., 2008). Studies have also demonstrated less cognitive decline in women with higher physical activity (Weuve et al., 2004; Yaffe et al., 2001). Inactivity or a sedentary lifestyle increases women’s risks of several chronic conditions and is linked with weight gain, overweight, and obesity, which themselves are linked to detrimental health outcomes independent of physical activity.12 An inverse dose–response relationship of physical activity with health outcomes is frequently observed: benefits are conferred by moderate activities, such as walking, and even more by vigorous activity. Reduction in the risk of cardiovascular disease and type 2 diabetes in women has been found with as little as 15 or 30 minutes of physical activity per day (Brown et al., 2007).
Besides cardiovascular outcomes, cancers, and type 2 diabetes, research has looked at other health outcomes in women as well. For instance, physical activity and dietary exposures, such as to calcium and vitamin D, have been studied in relation to women’s bone health as possible risk modifiers for conditions, such as osteoporosis and hip fracture, that are more likely to occur in older women (Feskanich et al., 2003; Jackson et al., 2006). The findings on calcium and vitamin D remain less certain, but it appears that girls and young women who are regularly active achieve a greater peak bone mass—a factor that decreases risk of osteoporosis—than those who do not and that older women can prevent bone loss with regular activity (NIAMS, 2009).
A relatively small but growing field of research is focused on how physical activity and eating habits may influence how well women who are living with chronic conditions, or who have experienced a health-related event, fare and recover. For women who have early-stage breast cancer, for instance, there is some evidence that physical activity is favorably associated with quality of life and improved survival (Bicego et al., 2009; Holmes et al., 2005; McNeely et al., 2006; Pierce et al., 2007).
In girls and women, increments in adipose tissue (fat) tend to be distributed in both upper (abdominal) and lower (hips) body compartments (Kissebah et al., 1983). Accumulation of fat in either may reduce the image and esteem of a girl or woman in her own mind as well as among her peers: this situation may occur in girls and women more than boys and men (Crocker, 1999; Wing et al.,
1991). Trends in fat accumulation that may begin in childhood and accelerate during adolescence are increasingly consequential during young adulthood and middle age. The sex differences in patterns of fat buildup become clearer: men add weight (especially fat) in a central, upper-body, waist-expanding fashion (sometimes termed the android pattern), and women classically selectively increase fat deposits in a more peripheral, lower-body, hip- and thigh-expanding distribution (the gynoid pattern) (Ley et al., 1992). The health implications of that gender difference are profound and evident in the waist:hip ratio, which is lower in many women than in men (Ley et al., 1992). A high waist:hip ratio correlates with measures of insulin insensitivity and the metabolic syndrome (Björntorp, 1997; Goodpaster et al., 2005), high blood pressure (Kalkhoff et al., 1983), dyslipidemia (increased low-density lipoprotein and decreased high-density lipoprotein) (Björntorp, 1997), thrombosis (Walker et al., 1996), type 2 diabetes (Barker et al., 1993; Stern and Haffner, 1986), and cardiovascular disease (Stern and Haffner, 1986). High waist circumference, an indicator of visceral fat, also correlates with adverse health outcomes (Li et al., 2007), including an increased risk of myocardial infarction (Yusuf et al., 2005), type 2 diabetes (Wang et al., 2005), and all-cause mortality (Bigaard et al., 2005).
Research on Interventions
Evidence of the effectiveness of interventions to increase physical activity and improve eating habits in women and girls comes largely from studies that have targeted these behaviors, often in concert, as a means of reducing or managing weight or reducing the risk of diabetes, cardiovascular disease, and other health outcomes (Bayne-Smith et al., 2004; Pate et al., 2005). The antecedents of changes in behavior (such as changes in knowledge about risks and self-efficacy, that is, their belief in their ability to change their behavior) and physical activity and dietary change themselves have been used as measures of intervention effectiveness (Edmundson et al., 1996; Emmons et al., 1999; Saksvig et al., 2005). Some studies have used biomarkers—such as weight or BMI change, serum cholesterol, and blood pressure—as measures of effectiveness (Andersen et al., 1999; Elmer et al., 2006; Moreau et al., 2001; Stefanick et al., 1998).
Many interventions that have targeted children and adolescents have been school-based and have targeted boys and girls together. The Task Force on Community Preventive Services found insufficient evidence to determine the effectiveness of school-based interventions in increasing fruit and vegetable intake or in decreasing fat and saturated-fat intake. Although generally positive, the effects of individual interventions are modest and based on self-reports (Guide to Community Preventive Services, 2004).
With respect to physical activity, it appears that modified curricula that increase the length or intensity of activity in schools’ physical-education classes are effective in increasing physical activity and improving fitness in girls and boys
among diverse racial, ethnic, and socioeconomic groups (Guide to Community Preventive Services, 2004). A more recent review, however, concluded that the evidence does not support the effectiveness of school-based physical-activity interventions in general in increasing the percentage of children and adolescents who are physically active during leisure time or in reducing BMI (Dobbins et al., 2009). Where increases in activity have been documented, they have tended to be modest and of short duration, and have not generalized to nonschool settings. Moreover, maintenance of physical-activity increases has been poor or, because of short followup, not assessed (Marcus et al., 2006).
Low income is associated with low fruit and vegetable consumption in part because of poorer access (Steptoe et al., 2003). On the basis of the socioecologic model, some efforts to improve access to fruits and vegetables, such as food vouchers for farmers’ markets and supermarkets, have been successful in increasing fruit and vegetable intake in low-income women. In a study of Women, Infants, and Children participants, women who received food vouchers increased their consumption of fruits and vegetables significantly and sustained the increase 6 months after the intervention was terminated (model-adjusted R2 0.13; P < 0.001) (Herman et al., 2008).
Clinical settings offer a way to reach women to increase physical activity and improve eating habits. A review of 32 studies of nutrition-counseling and physical-activity interventions to reduce the risk of cardiovascular disease in women found that, overall, interventions administered in health-care settings tended to produce modest but statistically significant effects on physical activity or exercise, dietary fat, weight loss, blood pressure, and serum cholesterol (Wilcox et al., 2001). Diet-only and combined interventions were equally effective in reducing dietary fat, and physical-activity-only and combined interventions were about equally effective in increasing physical activity. Effects were observed even after modest interventions, such as brief behavioral counseling by a health-care provider and printed educational materials. The length of followup varied among studies. With respect to physical activity in particular, interventions with less than 6 months of followup had greater effects, indicating a problem with sustaining the activity. Studies that were tailored to ethnic group and stage or readiness for change had larger effects, but the authors indicated that more research was needed to confirm their findings (Wilcox et al., 2001).
Little research has been carried out on strategies for encouraging physical activity during or after pregnancy (Cramp and Brawley, 2006). In a review of weight-management interventions for pregnant and postpartum women, Kuhlmann et al. (2008) concluded that interventions that addressed modifications in eating habits and exercise and included individual or group counseling sessions, combined with written and telephone correspondence or food and exercise diaries, resulted in significantly better outcomes on weight measures than other interventions, although refusal and attrition rates were high.
The Centers for Disease Control and Prevention’s (CDC’s) VERB campaign—
which encouraged daily physical activity in children aged 9–13 years old through paid advertisements, school and community promotions, and Internet activities—had some success. After 1 year of the program, a significant positive relationship was detected between the level of awareness of VERB and weekly median sessions of free-time physical activity in the total population. Significant overall effects were observed in girls exposed to the campaign compared with girls unaware of the campaign (Huhman et al., 2005). Studies of mass-media campaigns that deliver messages on a local or regional level via television, newspaper, and radio, however, can be successful in producing recall of campaign messages but show mixed results as to attitude and behavioral change in targeted populations (Marcus et al., 2006).
Although interventions that promote healthy eating have the potential to improve health, short-term “diets” may be more harmful than helpful. A careful review of research on dieting found in long-term followup (4–7 years) that one-third to two-thirds of dieters gain more weight than they lost on their diets (Mann et al., 2007).
Sexual Risk Behaviors
Sexual risk behaviors increase the chances of adverse outcomes associated with sexual contact, including sexually transmitted infections (STIs) and unintended pregnancy. In the research literature, sexual risk behaviors have commonly included nonuse and incorrect and inconsistent use of condoms and other birth control methods, sex with multiple partners, sex with high-risk or casual partners, and use of drugs or alcohol (which can impair judgment and decision making) before or during sex (Blake et al., 2003; LaBrie et al., 2007; Pulerwitz et al., 2002; Santelli et al., 1998). Although those behaviors put women at risk for both unintended pregnancy and STIs, the research has tended to look at these separately in a reflection of differences in funding streams and service delivery. Unintended pregnancy and its health consequences and prevention are discussed further in Chapter 3. The rates of many STIs have decreased in recent years, but in 2008 it was estimated that one-fourth of females 14–19 years old had one of the most common STIs—human papillomavirus (HPV) infection, chlamydiosis, herpes simplex, or trichomoniasis (Forhan et al., 2008). Chlamydiosis has not decreased, but this may be because there have been more screening and more sensitive tests. About half of pregnancies are unintended (Finer and Henshaw, 2006).
In 2008, the overall rate of chlamydial infection in the United States in women (584 cases per 100,000 women) was almost 3 times the rate for men (CDC, 2009c). Of females, those 15–19 and 20–24 years old have the highest rates of chlamydial infection (CDC, 2009c). Poorer women and girls, and those
who are members of racial and ethnic minorities are disproportionately affected by STIs and their sequelae, and by unintended pregnancy (Finer and Henshaw, 2006; Guttmacher Institute, 2006). The prevalence of chlamydial infection is greater in economically disadvantaged women than among those who are more advantaged (Harrison et al., 1983; Jolly et al., 1995). Gonorrhea rates in men and women are generally similar and have remained fairly stable over the last 12 years (CDC, 2009d). In 2008, 15- to 19-year-old black women had the highest gonorrhea rates of any group, followed closely by 20- to 24-year-old black women (CDC, 2009d).
Syphilis declined from 1992 to 2003 but increased from 0.8 case per 100,000 women in 2004 to 1.5 cases per 100,000 in 2008 (CDC, 2009e). Syphilis is a greater problem in the South and in urban areas in other regions of the country and is more prevalent in some minority groups. Similar patterns emerge across STIs: rates are highest in blacks, followed by Hispanics and American Indians and Alaskan Natives; they are lowest in Asian Americans and Pacific Islanders; and they are intermediate in whites (CDC, 2009e).
Women are at higher risk than men for most STIs and at higher risk of adverse complications. For instance, bacterial STIs in women that are left untreated can lead to pelvic inflammatory disease (PID), which can cause infertility, ectopic pregnancy when pregnant, and chronic pelvic pain. About 1 million US women experience an episode of acute PID each year.13 Symptoms of bacterial STIs (such as chlamydiosis and gonorrhea) are often subtle or “silent,” especially in females, and this can delay testing and treatment. HPV increases men’s risks of some cancers, but it contributes to twice as many cancers in women as in men, primarily HPV-associated cervical cancers. There were 104,097 cases of HPV-associated invasive carcinoma in women during l998–2003 compared with 45,410 in men (Watson et al., 2008).
The consequences, biology, and diagnosis of and treatment for those conditions are discussed in Chapter 3.
Research on Sexual Risk Behavior and Protective Factors
Rates of STIs and unintended pregnancy in those who are sexually active are highest in adolescence and young adults in their early 20s (Finer and Henshaw, 2006). In addition to social and psychologic factors, biologic vulnerabilities may play a role. For example, the cervix of adolescent girls is more susceptible to STIs than those in older women (IOM, 1997; Lee et al., 2006). A review of US studies
published in 1990–2007 (Kirby, 2007) found that multiple factors act to increase or decrease the chances that adolescent girls will engage in sexual risk behavior. They include individual biology (such as pubertal timing); factors in adolescents’ lives and environments (such as physical and emotional abuse and exposure to drugs and alcohol); sexual values, attitudes, norms, and modeled behavior (of teens themselves and their parents, friends, and romantic partners); and connections with parents, other adults, and organizations (such as school and places of worship) that discourage sex, unprotected sex, and early childbearing.14
Several gender-related differences and findings specific to girls have been recognized. Having an older romantic partner is a stronger factor in risky sex in girls than in boys. When teen girls have sex at an early age with much older partners, the chances are greater that their first sexual experiences will be involuntary or unwanted and that they will become pregnant (Kirby, 2007; Manlove et al., 2006). The different decision-making roles and power of male and female partners in a relationship affect decisions about contraceptive use, particularly coitus-dependent methods, such as condoms (Pulerwitz et al., 2000, 2002). Greater communication with parents about sex appears to be protective in both sexes, but the effects on behavior are greater in girls. For teen girls, but not teen boys, participation in sports results in delayed initiation of sex, less frequent sex, greater use of contraception, and lower pregnancy rates (Kirby et al., 2007).15 Multiple studies, including prospective studies that followed victims of abuse over time, have shown that adolescent girls who have a history of sexual abuse engage in more risky sexual behavior and are more likely to become pregnant (Logan et al., 2007).
Research on Interventions
Many curriculum-based interventions to affect adolescent sexual behavior have been developed and evaluated. Most have been implemented in schools, and most have used a comprehensive approach that addresses avoidance of pregnancy and STIs through abstinence and through the use of condoms and other forms of contraception. Although some interventions are ineffective, studies have indicated strongly that well-designed comprehensive interventions generally reduce sexual risk behaviors and do not increase sexual activity (Kirby, 2007). According to self-reporting, interventions appear about equally effective in girls and boys in reducing the number of sexual partners, increasing the use of condoms, increasing the use of other contraceptives, and reducing the overall frequency of unprotected sex (Coyle et al., 2001; Kirby, 2007; Shrier et al., 2001). Even though girls have
less control over condom use than boys do, after some interventions girls are more likely to report that male sex partners used condoms (Coyle et al., 2001; Kirby et al., 2007; Shrier et al., 2001). At their most effective, the curriculum-based interventions appear to reduce adolescent sexual risk-taking (Kirby et al., 2007). Virtually all studies use risk-behavior change as the outcome rather than rates of pregnancies and STIs because obtaining an adequate number of end points to test for the effect of an intervention requires large samples and long followup.
A number of abstinence-only interventions for adolescents have been developed and evaluated in recent years because of requirements for funding introduced in 1996. Systematic reviews of the programs have suggested that their effects on behavior vary, but that overall they had a minimal impact on risky behavior (Bennett and Assefi, 2005; Kirby, 2007; Kohler et al., 2008; Trenholm et al., 2007).
Non–curriculum-based interventions have also been developed and tested, but there is not yet enough evidence to support conclusions about their effectiveness. Examples include human immunodeficiency virus (HIV) and STI educational programs that facilitate discussion between adolescent girls and their parents (Dancy et al., 2006; DiIorio et al., 2006; Nicholson and Postrado, 1991) and video- and computer-based programs for girls (Downs et al., 2004). Many communitywide STI–HIV and pregnancy-prevention initiatives to improve teenage girls’ performance in school, plans for the future life, and connections to family, school, and faith institutions have been found to result in reduced community-level pregnancy and birth rates (Kirby et al., 2007).
Research to evaluate school-based clinics and condom-availability programs finds that teens use those sources to obtain condoms and other contraceptives. School policies that accept condom dissemination have been shown not to increase sexual activity in youth who are not sexually active while increasing condom use by those who are sexually active (Blake et al., 2003). Publicly funded family-planning services overall increase use of contraceptives by women and girls and avert an estimated 1.4 million unintended pregnancies and 600,000 abortions each year (Frost et al., 2008).
Cultural values that promote and emphasize the role of women as mothers and the value of children and that accept adolescent pregnancy can contribute to increased rates of unplanned pregnancy. Promotion of traditional gender roles wherein girls and women are expected to be more naïve and less active in sexual decision making may also contribute to unplanned sexual behavior and to higher rates of STIs and pregnancy in subpopulations of women and girls (Gomez and Van Oss Marin, 1996; Shearer et al., 2005; Weiss et al., 2000).
Most interventions for STIs and unplanned pregnancy have focused on girls or women, and very few have targeted boys and men, who are the ones who use condoms (Amaro, 1995). Intervention approaches studied include involving partners and family members in the intervention; implementation of interventions in churches, community centers, and other locations where people may congregate
along cultural lines; communitywide interventions that aim to change broad perspectives about roles of women and girls; and skill-based interventions to help women and girls in negotiating condom use with their partners (Orr et al., 1996; Roye et al., 2007; Scholes et al., 2003). On the basis of risk-factor research, such organizations as the National Campaign to Prevent Teen and Unplanned Pregnancy have produced evidence-based materials outlining how programs to reduce sexual risk behaviors can better reach high-risk populations, such as black and Hispanic individuals and communities (The National Campaign to Prevent Teen Pregnancy, 2010).16
Alcohol use is more complicated than other risk factors; studies have shown both beneficial and harmful effects. The use of alcohol and the use of other substances, if excessive, constitute serious health conditions of their own; and moderate use can affect the risk of other diseases. Chapter 3 deals specifically with alcohol and drug addiction as a disease, including sex- and gender-differences in its biology and treatment; here, the committee considers the use of alcohol as a more general determinant of women’s health.
Although men are more likely than women to drink alcohol and to drink in large amounts, differences in body structure and chemistry cause women to take longer to break alcohol down and remove it from their bodies. In addition, women, particularly elderly women, typically have lower body weights than men. On drinking equal amounts, women have higher blood alcohol concentrations than men, and the immediate effects occur more quickly and last longer (CDC, 2008c). Furthermore, although women generally begin using alcohol later than men do, they move more quickly (telescoping) to dependence and manifestation of associated adverse health effects (Diehl et al., 2007; Greenfield, 2002). Alcohol use in women appears to have some unique features, including more concurrent psychological and medical problems, which may be more pronounced than in men (Ashley et al., 1977; CDC, 2008c; Chatham et al., 1999; Conner et al., 2007).
Alcohol is a widely used substance with complex relationships to health. Moderate consumption of alcohol is associated with decreased risk of heart disease in women (Hvidtfeldt et al., 2010; Mente et al., 2009). However, as little as one alcoholic beverage per day can increase the chances that a woman will develop breast cancer, particularly if she is postmenopausal or has a family history of breast cancer (AICR, 2007; NIAAA, 2008). Women appear to have greater susceptibility than men to alcohol-related hepatic and cardiac disease, even though on the average women drink less (NIAAA, 2008). Heavy alcohol use has been linked with an increased risk of colorectal cancer in both women and
See http://www.thenationalcampaign.org/resources/reports.aspx#hisp (accessed May 2, 2010)
men (AICR, 2007). Because of the acute toxic effects of alcohol, including impairment of psychomotor function and judgment, alcohol intoxication increases the risk of injury (for example, in motor-vehicle crashes). Especially for women, heavy drinking increases the chances of being a victim of violence or sexual assault (NIAAA, 2008).
SOCIAL AND COMMUNITY FACTORS
In addition to the individual biologic determinants (discussed in Chapter 3) and behavioral determinants that affect women’s health, social and community factors affect health as well, and they modify responses to other determinants. This section discusses some of those factors—exposure to violence, stress and social connections, social disadvantage, and environmental factors.
Exposure to Violence
Violence against women and children, including sexual assault and rape, has attracted national and international attention as a serious public-health concern (WHO, 2005).17 About one-fourth of US women (26.4%) report a lifetime occurrence of intimate-partner violence18 victimization vs 15.9% of men (HHS, 2008b). Women are more likely than men to be injured during an assault, and the risk is highest when they are victimized by current or former partners (Tjaden and Thoennes, 2000). It is estimated that women and men suffer 2 million and 600,000 injuries, respectively, and 1,200 deaths a year of women from intimate-partner violence (HHS, 2008b).
Women who are “multiracial, non-Hispanic” and “American Indian or Alaska Native” report higher rates of exposure to violence than do white women (HHS, 2008b). Those groups are overrepresented in communities characterized by poverty, in which community violence is more common. Urban vs rural residence, however, has not been found to be related to violence exposure (Breiding et al., 2009). Violence against women is not limited to those in heterosexual relationships. A national survey of lesbian women found that 32% and 11.4% of lesbian women reported experiencing rape and intimate-partner violence, respectively;
black lesbians are more likely than their Latina or white counterparts to report being raped (Descamps et al., 2000).
Violence against women within intimate relationships and within the community and violence against girls have direct health effects through injury and trauma and contribute to the development of later health problems (Tjaden and Thoennes, 1998). Women who experience violence have higher rates of later arthritis, asthma, heart disease, gynecological problems, and risk factors for HIV or sexually transmitted diseases and have lower self-rated health than those who do not experience violence (Campbell et al., 2002; HHS, 2008b). Women who experience psychologic intimate-partner violence, even in the absence of physical violence, are at greater risk for a number of adverse health outcomes, including a disability that prevents work, arthritis, chronic pain, migraine and other frequent headaches, STIs, chronic pelvic pain, gastric ulcers, spastic colon, and frequent indigestion, diarrhea, or constipation (Coker et al., 2000). Women of reproductive age who experience violence are at increased risk for unintended pregnancy (O’Donnell et al., 2009; Pallitto et al., 2005; Silverman et al., 2007), miscarriage (Silverman et al., 2007) and low–birth-weight babies (Campbell et al., 2002; Coker et al., 2002; Ellsberg et al., 2008). Intimate-partner violence is associated with psychologic disorders, including eating disorders, drug and alcohol abuse, and depression (McCauley et al., 1995). In a study of self-identified Latina or Hispanic urban women, those reporting intimate-partner violence were 3 times more likely to meet the criteria of posttraumatic stress disorder (PTSD) than those not reporting it (Fedovskiy et al., 2008).
Intimate-partner violence is also linked to STIs, including HIV infection. In a clinic-based sample of black and Latina women, those who experienced partner violence were more likely to report having multiple partners, a history of STIs, and inconsistent condom use (Wu et al., 2003), which are risk factors for HIV transmission (Silverman et al., 2004). Negotiating condom use in abusive relationships is particularly difficult (Campbell et al., 2002; Wingood and DiClemente, 1997).
Exposure to community violence has been associated with greater anxiety, depression, and PTSD and with adjustment or behavioral problems in school, particularly in urban populations (Gorman-Smith and Tolan, 1998; Ozer and Weinstein, 2004; Schwab-Stone and Ayers, 1995). Young men are more likely than their female counterparts to be exposed to community violence (Cooley-Quille et al., 2001; Malik et al., 1997; Ozer and Weinstein, 2004; Schwab-Stone and Ayers, 1995). One study indicated that girls are more likely than boys to demonstrate internalizing behavior (such as withdrawal and anxiety) after community-violence victimization (Cooley-Quille et al., 2001). However, another study that
looked at depression and PTSD in urban adolescents did not demonstrate gender differences (Ozer and Weinstein, 2004).
Research on Prevention
Research on community violence has identified social conditions that foster violence victimization and perpetration. Previously conflated in the literature, violence victimization and the witnessing of community violence are now considered separate phenomena (O’Donnell et al., 2002). Less obvious forms of abuse at the hands of intimate partners have been identified; qualitative studies have described coercive tactics used by male partners to influence women’s reproductive lives (for example, intentionally breaking condoms, preventing a partner from accessing birth control, and using threats to influence decisions about pregnancy and abortion) (Hathaway et al., 2005; Miller et al., 2007a). Those forms of abuse are not currently captured in standard clinical assessments of intimate-partner violence. Such assessments might also miss women and girls who are the victims of trafficking associated with being brought to the United States for domestic labor, sex work, or adoption (Miller et al., 2007b; US Department of State, 2009). There has been little research on differences in mechanisms of exposure to violence between racial and ethnic groups, social classes, sexual orientations, and cultures.
Research is starting to identify factors that may buffer girls and women from some of the sequelae of exposure to violence. A study of young adolescents found that support from parents and teachers was a protective factor against adverse outcomes after community-violence victimization, but that peer support was not related (Ozer and Weinstein, 2004). Although gang involvement can increase risky behavior, such as early sexual initiation and substance use (Cepeda and Valdez, 2003), good mother–daughter relationships were protective against alcohol and substance use regardless of gang involvement (Valdez et al., 2006).
Research on Interventions
The prevention of violence involves educating the general population (primary prevention) and identifying at-risk populations in which targeted prevention (secondary prevention) and intervention programs may be effective. Primary prevention programs are often school-based (Mytton et al., 2002) and focus on violence related to dating. For example, “Safe Dates” is a program tested in rural public schools to prevent adolescent dating violence and to stop victimization from continuing in abusive relationships (Foshee et al., 1998). It uses peer education to change gender norms that supported violence in dating relationships (Foshee et al., 1998, 2005) and significantly reduced a number of measures of victimization in boys and girls (Foshee et al., 2005).
In the clinical arena, effective HIV-prevention programs that include attention to how intimate-partner violence and dating violence affect HIV risk are
emerging. An intervention conducted among black adolescent girls who were seeking care at community-based clinics used health and peer educators to discuss gender norms and resulted in a significant increase in consistency of condom use and a decrease in partner-related barriers to safe sex among those who had experienced dating violence (Wingood et al., 2006). Another promising intervention, conducted in abused adult women who had co-occurring mental-health and substance-use disorders, incorporated group trauma treatment focused on knowledge about personal safety, empowerment, coping skills, HIV-prevention education, and risk-reduction skills (Amaro et al., 2007). Women who did not receive the intervention were more likely than those who did to report unprotected sex 6 months and 12 months after the intervention.
Stress and Social Connections
A substantial body of research links stress and disease. Stress is the state that occurs when people are faced with a threat that they feel unable to counteract (McEwen and Stellar, 1993). Much of the research has examined short-term effects of acute stress on biologic processes—such as blood pressure, heart rate, cortisol, and proinflammatory cytokines—whereas more recent research is examining the health effects of chronic exposure to stress (Burkman, 1988; Dreher, 2004; Fleury et al., 2000; McEwen, 2004; Vitaliano et al., 2002). The occurrence of a threatening situation in itself does not engender stress; it occurs only when a person does not have adequate resources to deal with it. Facing threats that can be controlled or eliminated is generally experienced as a challenge rather than as a threat and has a different physiologic response. Challenge responses are characterized by increased cardiac performance, greater excretion of epinephrine, and greater vasodilation (Blascovich and Mendes, 2000; Blascovich et al., 1996). In contrast, threat responses engage the hypothalamic–pituitary–adrenal (HPA) axis and are accompanied by greater excretion of cortisol and norepinepherine and greater vasoconstriction. Accumulating data suggest that chronic stress accelerates the aging process and shortens life expectancy (Hawkley and Cacioppo, 2004).
In the last 20 years, more has been learned about the effects of repeated exposure to stress and specifically to the chronic stress associated with social disadvantage (McEwen and Gianaros, 2010; Seeman et al., 2009). There have also been advances in the understanding of differences in how men and women respond to stress, which have implications for health. For example, one hypothesis is that women are more likely than men to respond to threats not with the “flight or fight” response and but with a “tend and befriend” response that involves oxytocin, sex hormones, and endogenous opiods, and that increases the parasympathetic response while reducing sympathoadrenal and HPA activity. The
“tend and befriend” response may buffer women from some of the adverse effects of stress (Taylor et al., 2000). Because the predominant model of stress response has been the male model, women’s counterregulatory processes have had less attention, and there are fewer data on the extent to which they operate.
Women’s roles subject them to different types and degrees of stressors than men generally experience. Women often experience stress associated with caregiving, which can take a toll on the body. For example, caregivers showed a significantly weaker antibody response in the months after receipt of a vaccination for pneumonia than did those who had never been caregivers and those who had been caregivers but no longer were (Glaser et al., 2000). Stress associated with caregiving can be seen even at the cellular level as reflected in telomere length. Telomeres cap the end of chromosomes and generally shorten with age. Telomere length is predictive of development of cardiovascular and other diseases and of death (Blackburn, 1991). Epel and colleagues (2004) compared telomere length in cells drawn from blood samples of middle-aged women who were caring for a child with a serious health problem and those who were caring for healthy children. The mothers of ill children who showed psychologic distress had significantly shorter telomeres than the other women; the difference was equivalent to 9 years of aging. The length of the women’s telomeres was significantly related to the number of years in which they had been in a caretaking role. The finding was replicated in a sample of male and female caregivers for patients who had dementia of the Alzheimer’s type. Caregivers had shorter telomeres (the equivalent of 4–8 years of aging), greater depression, higher concentrations of cytokines, and fewer lymphocytes than age- and sex-matched non-caregiving controls (Damjanovic et al., 2007; Epel et al., 2004).
Social connections are an important resource for buffering stress. Humans are social animals, and our links to one another can be a source of comfort as well as of conflict (as described in the section “Exposure to Violence”). A substantial literature shows that socially isolated people are at increased risk for death from multiple causes (Berkman and Syme, 1979; Friedmann et al., 2006) and that those who are more engaged in social relationships are generally healthier (Berkman and Glass, 2000).
Researchers have made much progress in delineating social processes that can affect health. For example, they have differentiated social support, social integration, and negative interactions, each of which shows associations with health (Cohen, 2004). Social integration reflects the variety of social roles in which a person is engaged. In general, occupying more social roles is linked to better health. For example, Cohen and colleagues (1997) used an experimental protocol in which healthy volunteers were exposed to a standard rhinovirus. Those who occupied more social roles were less likely to develop a clinical cold
than were those with fewer; the effect was especially strong in those who were most isolated.
Marriage is an important social role that can be a source of both support and conflict. Being married is consistently linked to better health in men but less so in women. For example, in analyses of the Framingham Offspring Study, Eaker and colleagues (2007) found that married men (mean age at study onset, 48.8 years) are half as likely to die during the 10 years of followup compared with unmarried men, but found no benefit of marriage for women. They also demonstrated that marital quality and the ways in which women responded to marital conflicts predicted that mortality.
As more women have moved into the workplace, there has been increasing research on possible conflicts of work and home roles. Working women may experience work–home conflicts, but they also appear to benefit from the additional social roles afforded them by their work status (Barnett, 2004; Barnett and Hyde, 2001; Ruderman et al., 2002). The benefits of working depend on the characteristics of the work environment, including exposure to physical or chemical hazards and to psychosocial factors, such as the balance of effort and reward and decision latitude. Although a great deal is known about the health effects of work environments, relatively little work has compared the impact of work conditions on women’s health with that on men’s health (Theorell, 2000).
Social networks also affect health through their influence on health-promoting or -damaging behaviors. They provide health-relevant information and influence behavior through social norms. Data from Alameda County, California, showed a link between social integration and health risks, including smoking, sedentary lifestyle, and obesity. The probability of engaging in high-risk behaviors decreased monotonically as social connections increased (Berkman and Glass, 2000). Recent studies of the Framingham study data found that obesity spread through social networks—one’s likelihood of obesity increased if others in one’s network were obese (Christakis and Fowler, 2007).
Important gaps in the understanding of the effects of social connections on women’s health remain. There has been relatively little work on sex differences in the impact of social ties (other than marriage) on health.
Research on Interventions
The adverse effects of stress on health can be reduced by reducing exposure to stressors and by modifying the people’s psychologic and physiologic responses to it. Interventions to address the former include programs to help people to anticipate and manage stress (Taylor, 1990); those addressing the latter include training in coping skills that enable people to reframe situations that they encounter so that they do not elicit a stress response. Exercise and such techniques as meditation have been shown to buffer the impact of stress on the body (Carlson et al., 2003; McComb et al., 2004; Rejeski et al., 1992; Sandlund and Norlander,
2000; Speca et al., 2000) and form the basis of new interventions. Although there is substantial documentation of the association between greater social connection and health, no successful interventions have been developed to improve health by strengthening social ties.
There are multiple reasons why groups are relegated to more disadvantaged positions in society, and those positions have health consequences. In addition to social limitations imposed by gender, some of the most common bases of social disadvantage are race, ethnicity, and SES. Social epidemiology studies over the past 20 years demonstrate that health inequities exist and change with societal conditions and policies (Beckfield and Krieger, 2009). Social conditions linked with low SES have been termed the fundamental determinants of health (Link and Phelan, 1995) because they affect almost all the more proximal determinants of health and illness. SES encompasses a person’s income and wealth, educational attainment, and occupational position (Braveman et al., 2005; Kaplan and Keil, 1993; Link and Phelan, 1995). Each of those domains provides both material and social resources and shapes the environments in which people live and work.
The meaning and measurement of SES in women is not always equivalent to those in men. For many years, women were classified according to their husbands’ socioeconomic characteristics (Zaher, 2002). Data on women’s occupational status has been harder to capture because more women than men are voluntarily out of the workforce for long periods. Income can be measured in terms of a person’s individual or household income. If the latter is used, one needs to take into account household size. Some women lack control over funds, and household income may have different implications for their expenditures and experiences (Phipps and Burton, 1998).
Differences in health associated with SES can be seen from birth and occur throughout the course of life. Premature birth, low birth weight, and birth defects are all more common in babies whose mothers have less money or education. In childhood, rates of asthma, ear diseases, injuries, and limiting chronic conditions decrease as SES increases (Chen et al., 2002). Moreover, childhood disadvantage has residual effects on adult health; a recent study, for example, showed independent associations of socioeconomic disadvantage, abuse, and social isolation experienced in childhood with levels of depression and with biomarkers of metabolic risk and inflammation in adulthood (Danese et al., 2009).
In adulthood, rates of many diseases—including cardiovascular disease, diabetes, such infectious diseases as HIV/acquired immunodeficiency syndrom (AIDS), respiratory illnesses, and mental illness—are higher in low-SES populations. The relative risk of dying before the age of 65 years is 3 times as great as those whose family annual incomes are under $10,000 (based on l999 dollars) than in those whose incomes are above $100,000 (Adler et al., 2007). The gaps
between high- and low-SES groups in morbidity and mortality diminish after the age of 65 years, although they can still be seen (Robert and Li, 2001).
In recent years, research on the joint effects of gender, race and ethnicity, and SES has provided increased evidence that many of the health effects of disadvantage are mediated by SES. Differences in life expectancy between the poorest and the wealthiest groups are twice as great as differences between whites and blacks (Lin et al., 2003); within every racial or ethnic group, the affluent have substantially better health than the poorest. For many diseases, racial and ethnic differences are substantially reduced or even disappear when SES is controlled for. The exception is birth outcomes, in which there is a substantial difference between black and white women at every SES level; in contrast with other outcomes, in which differences between the groups are smaller at higher levels of SES (Williams, 1999), the gap in birth outcomes is greater among higher-SES women than among those with less education or lower income (Cramer, 1995; Kallan, 1993). SES, however, does not explain all effects of race and ethnicity; at every level of SES blacks have higher mortality than whites (Lin et al., 2003).
At the same time, cultural factors may moderate the effects of SES. For example, although Latinos as a group have less education and are more likely to be poor, they have better outcomes on some health measures than similarly disadvantaged groups and better outcomes than some more advantaged groups, including non-Hispanic whites (Hayes-Bautista et al., 1994). Cultural factors may affect men and women differently; for example, US-born Mexican Americans have much higher rates of substance abuse than Mexican-born Mexican Americans and the differences are greater in women than in men (Lara et al., 2005; Vega et al., 1998).
In the last 2 decades, there have been substantial advances in research on SES and health. Initial studies established that the association was not due simply to the poorer health of the most disadvantaged but that health is monotonically related to SES; each step up the SES ladder is associated with better health (Adams et al., 2003; Adler and Stewart, 2010; Smith, 2004). That is the case with income in both men and women; those at the bottom have by far the greatest burden of disease and show the greatest gain in health with higher income, and those in the middle suffer poorer health than the most affluent. About one-fourth of deaths before the age of 65 years occur in the poorest 8% of the population (Santiago et al., 2009; Smith, 2004).
The picture is more complicated for education. White men get health returns for each additional educational milestone passed, from high-school graduation to earning a postcollege degree. The data on white women, however, are mixed. Women’s health improves with high-school and college graduation, but postcollege education does not seem to provide health benefits, and some studies even find slightly poorer health associated with graduate degrees (Krishnan et al., 2010; Lantz et al., 1998). The pattern is similarly complicated for black men and women (Krishnan et al., 2010; Williams and Collins, 2001).
After studies established the strong graded association between SES and health, researchers turned to studies that could uncover the mechanisms by which socioeconomic factors operated to influence health. It is clear that there is no single pathway; the major mechanisms are discussed below.
SES is linked to exposure to health hazards including carcinogens and pathogens that directly affect disease risk. For example, people who live in poorer neighborhoods are subjected to deteriorated housing and have greater exposure to lead (Pirkle et al., 1998). Rates of high-blood-lead concentrations are substantially greater in residents of low-income neighborhoods, and the risk is especially high in poor blacks (Brody et al., 1994; Lanphear et al., 1998). SES-related features of communities can encourage or constrain health behaviors. For example, poorer neighborhoods have less access to supermarkets that provide more choice and lower prices of healthy foods, such as fruits and vegetables (Moore and Diez Roux, 2006). Such neighborhoods also have fewer recreational facilities and may not be conducive to walking or jogging (Macintyre, 2000; Sallis and Glanz, 2009). Most behavioral risk factors for disease are more common in low-SES populations. For example, rates of smoking differ dramatically with educational level: less than 10% of college graduates smoke, but over 30% of those who never graduated from high school are smokers (CDC, 2004).
People’s social environments also differ by SES. Low-SES neighborhoods and work settings are generally more unpredictable, allowing less control, and have more conflict and threat. As a result, at work and at home, those who have less education, lower income, and jobs with less prestige and power encounter more stressors and have fewer resources for dealing with them (Fleury et al., 2000; Hallman et al., 2001).
Neighborhood effects appear to be greater in women than in men (Diez Roux et al., 1997; Winkleby et al., 2007). For example, women’s engagement in exercise was more affected by the proximity of recreational facilities than men’s (Diez Roux et al., 2007), and women’s but not men’s BMI was associated with proximity of supermarkets and convenience stores (Wang et al., 2007). Those findings are consistent with research that showed a stronger association of SES with cardiovascular disease in women than in men (Diez Roux et al., 2007).
SES has a major influence on health-care access and quality of care. People who have fewer economic resources and are employed in lower-SES occupations are less likely to have health insurance or access to care. Lower-SES people are likely to encounter problems as a result of poorer health literacy (IOM, 2004a). Issues of health care are discussed in more detail later in this chapter and in Chapter 5.
Interventions that modify income, education, or occupation are not within the purview of the health-care sector but fall into other domains. A few programs have been evaluated and found to have health effects. Most promising are new programs, largely in Latin American countries, in which conditional cash trans-
fers are linked to performance of behaviors that have health consequences. The model program is Progresa/Oportunidades, in Mexico, which was tested using a randomized design with rigorous evaluation. Results are still emerging, but show that the program has favorable effects, such as increased contraceptive use by 20-to 24-year-olds and beneficial effects on children’s growth (Fernald et al., 2009; Molyneux, 2006). In the United States, the Moving to Opportunity is a program of the Department of Housing and Urban Development in which families living in high-poverty public housing receive housing vouchers to move to a privately owned rental property in a low-poverty area. Among those who moved, the women showed significant gains in mental-health outcomes (Katz et al., 2001; Leventhal and Brooks-Gunn, 2003).
Many more interventions are addressing the pathways by which SES affects health, including community-level interventions to address the physical or social environment. Increased knowledge of the effects of SES on health behaviors may also allow for the development of more effective interventions that address the context in which these behaviors are established and maintained in women of different socioeconomic backgrounds.
Environmental factors can play a role in health, including women’s health. Environmental exposures, such as exposure to chemicals, combine with genetic and other factors to determine health. As is the case with genetic contributions, very few diseases are determined solely by environmental factors (Davey Smith and Ebrahim, 2003; Jirtle and Skinner, 2007). Even in the face of relatively dominant hereditary input (such as BRCA1), it appears that enabling environmental and hereditary (non-BRCA1) contributions are at play (Chia, 2008; Laden and Hunter, 1998; Wolff et al., 1996). Similar gene–environment interactions determine a woman’s health and risk-seeking behavior, not to mention the proclivity to smoking, drug abuse, and obesity (Gammon et al., 1998; Hamajima et al., 2002; Russo, 2002; Wolff et al., 1996). Although some environmental exposures, such as to drugs of abuse and smoking, are due to individual behaviors, others are the result of community factors.
Societal factors, such as cultural norms and health care, can have a large impact on women’s health. Those factors are discussed in this section.
Transmitted from generation to generation, culture is defined in many ways, but is generally considered a distinguishing set of characteristics of a population
group. It consists of a group’s shared values, norms, practices, systems of meaning, ways of life, and other social regularities (Kreuter et al., 2003). Culture can be a powerful influence on health, health behavior, and experiences in the health care system. Influencing perceptions and interpretations of symptoms, culture shapes help seeking, expectations of the sick role, and the level and nature of communication between patient and provider. In particular, cultural differences in language and SES between patient and provider may present barriers to appropriate diagnosis and treatment (Perez-Stable et al., 1997).
Lifestyle behaviors are influenced by culture as evidenced in food preferences and dietary practices. Cultural traditions associated with the consumption of high-fat, high-salt, and high-sugar foods among African Americans contribute to an increased risk for hypertension, diabetes, and heart disease (Airhihenbuwa et al., 1996). On the other hand, several studies of Hispanic diets show a greater consumption of carbohydrates, protein, and fiber and less saturated fats than whites, although the use of traditional foods among Mexican American women appear to decrease with increased acculturation to mainstream society (Guendelman and Abrams, 1995). For both African American and Latino women, cultural factors influence a greater acceptance of being overweight and body image satisfaction than among white women (Fitzgibbon and Beech, 2009; Phelan, 2009).
While cultural factors in some instances may contribute to risks for poor health, aspects of culture may also bring positive protective influences. A study by Nasim and colleagues (2007) found that cultural factors associated with religious beliefs and family values served as protective factors against tobacco and marijuana smoking among African American female college students at a predominantly white university. In another example, Wasserman and colleagues (2006) report how promotoras (lay health advisors) facilitated cervical cancer screening among Latina immigrant women.
Providing parameters for women’s gender roles, culture prescribes acceptable norms in the family and community for behavior associated with being male or female. Cultural norms may determine whether women engage in employment outside the home, the circumstances under which caregiving is undertaken, how women respond to domestic violence, and the nature and type of social support given and received. Cultural norms also affect health decisions from prevention to treatments and may influence choices regarding sexual risk behaviors, contraception, and the acceptability of the HPV vaccine.
Culture appears to mediate the effect of poverty on depression rates in minorities. The recent Collaborative Psychiatric Epidemiology Survey program, which used common core questions and unified sampling weights, found that Hispanic Americans (except those from Puerto Rico), Asian Americans, and black Americans have fewer common mental disorders, such as depression, than do white Americans, even though many of these cultural groups are more likely to be impoverished than their white counterparts (Alegría et al., 2008; Breslau et al.,
2007; Himle et al., 2009; Jackson et al., 2007; Suarez et al., 2009; Takeuchi et al., 2007a,b; Williams et al., 2007). Similarly, American Indians have lower risk of depression than a representative sample of the US population. For Mexican, African, and Caribbean immigrants, the risk of depression is particularly low but increases with time spent in the United States (Alegría et al., 2007; Cutrona et al., 2000; Escobar et al., 2000; Laden and Hunter, 1998; Morales et al., 2002).
However, Page (2005) points out that culture is not static but is viewed as a process. This is seen in the changing gender roles of women, as well as the acculturation of immigrant groups. In addition, data from NHANES shows that foreign-born Hispanics obtained more energy from food groups such as legumes, fruits, and low-fat/high-fiber breads than US-born Hispanics (Duffey et al., 2008). While considerable research has focused on the health of women of various ethnic groups, cultural factors are not consistently examined. Given the increasing ethnic diversity in the US population, understanding cultural factors and their relationship to specific health outcomes remains an ongoing challenge.
Prior IOM reports have summarized the adverse health consequences of poor health care, and these are not reiterated here (IOM, 2001b, 2002b,c, 2003, 2004b, 2009b). The present committee accepted the premise that a lack of health care or high-quality health care adversely affects a woman’s health. A large body of research has examined the factors that affect people’s use of health care. Women use more health services than men (HRSA, 2008; Salganicoff et al., 2005), but there is inadequate understanding of the factors that influence health-care use by men and women and of the extent to which these factors reflect issues related to gender or differential treatment by the health-care system.
Health-Care Access and Quality for Women
Health policies, both private and public, shape our delivery system and influence access and quality of care, especially insofar as they affect health insurance coverage and affordability.
Women and Health-Insurance Coverage In the United States, women are covered by patchwork of private insurance and public coverage programs and by direct financing programs that provide services primarily to low-income or uninsured women. Eligibility for those programs and benefits is determined primarily by a person’s work status, age, household income, and state of residence (DeNavas-Walt et al., 2009). The Patient Protection and Affordability Act of 2010 (Public Law 111-148) will make major changes in health coverage, but most of those changes will not be implemented until 2014.
Most US adults under age 65 years old are covered by an employment-based health insurance plan (DeNavas-Walt et al., 2009). Because women are less likely than men to work full-time, access to the system can be tenuous. Women with employer-based insurance are almost twice as likely as men to be covered as dependents, and this makes them vulnerable to losing their insurance if they become widowed or divorced or if their husbands or partners lose their jobs (Kaiser Family Foundation, 2009). Women are increasingly obtaining coverage in their own names as the share of women in the full-time workforce grows (Glied et al., 2008).
Nationally, about 6% of women purchase coverage through the individual insurance market in which insurers can deny coverage if an applicant has a “preexisting condition,” such as pregnancy, mental illness, or a chronic health condition. In 38 states, insurers can charge women who purchase individual insurance more than men for the same coverage even if they do not cover maternity care (Codispoti et al., 2008; Pollitz et al., 2007). That practice has been banned under the new health reform law, but will not be fully implemented until 2014.
One-tenth of women are covered by Medicaid (Kaiser Family Foundation, 2007). Women are more likely than men to qualify for Medicaid because women on the average have lower incomes and are more likely to fall into one of the program’s eligibility categories (that is, she is pregnant, the parent of a dependent child, over 65 years old, or disabled). Women make up over two-thirds of adult beneficiaries of Medicaid (Kaiser Family Foundation, 2007). Medicaid covers many services that are important to women, paying for two-fifths of births in the United States, nearly two-thirds of all publicly funded family-planning services, a wide array of preventive screening services without copayments, and long-term care (Kaiser Family Foundation, 2007). On a number of primary and preventive indicators, women on Medicaid have access that is on a par with that of women with private insurance (Almeida et al., 2001; Salganicoff et al., 2005).
Women who are 65 years old and older or who are permanently disabled are covered by Medicare, but it has sizable gaps in coverage, notably for vision, dental, and hearing care and, most important for women, long-term care. Sizable out-of-pocket costs associated with the program can pose a major problem for older women, who have lower average Social Security and pension benefits than men (Congressional Research Service, 2008; Salganicoff et al., 2009).
Finally, almost 17 million women—17% of nonelderly women—are uninsured (Kaiser Family Foundation, 2009). Many working women lack access to employer-based coverage because they work part-time or in firms or industries that do not offer insurance. Poor and low-income women, young women, and
Latina and American Indian women are uninsured or underinsured at higher rates than other groups (Kaiser Family Foundation, 2009).19
Coverage, Out-of-Pocket Costs, and the Use of Health-Care Services The literature on women’s access to and use of care has been comprehensively summarized by Brittle and Bird (2007). Women who have health insurance have higher rates of use of a broad array of services than those who are uninsured (Almeida et al., 2001; Taylor et al., 2006). Uninsured women obtain fewer recommended preventive services, such as mammography and Papanicolaou (Pap) tests (Salganicoff et al., 2005); are less likely to have a usual source of care (RWJF, 2002); are less likely to get timely prenatal care (Braveman et al., 2003); and have lower rates of use of prescription drugs (Ranji et al., 2007).
Costs of care are a major concern for women, and reproductive and gender-specific conditions place a heavy burden on women’s health-care out-of-pocket spending throughout their lives (Bertakis et al., 2000; Kjerulff et al., 2007; Salganicoff et al., 2005). The impact of such costs may be particularly burdensome for women, who are disproportionately in a low-income bracket (Glied et al., 2008; Rustgi et al., 2009; Salganicoff et al., 2005). For example, a larger share of women than of men have a usual source of care, which is linked to timely receipt of preventive services (DeVoe et al., 2003), but among those without a usual source of care, women were more likely than men to report that the cost was the major reason that they did not have one (AHRQ, 2009a). Higher levels of cost sharing result in lower use of services, particularly in low-income populations, including fewer visits to doctors, lower use of prescription medicines and mental-health treatment, and fewer dental visits (Hudman and O’Malley, 2003). Medicaid historically has either prohibited cost sharing or kept it to nominal levels. States that have raised cost-sharing levels have sometimes seen declines in service use thereafter (Artiga and O’Malley, 2005).20
Unlike Medicaid, Medicare has relatively high cost-sharing requirements, which affects level of use by beneficiaries, particularly those who have low incomes (Fitzpatrick et al., 2004). Increased cost sharing tends to reduce the appropriate use of prescription drugs through a number of means, including skipping doses, halting medication use, and not filling prescriptions (Rice and Matsuoka, 2004). Cost sharing was also found to be associated with substantial decreases in mammography rates among female Medicare beneficiaries who were enrolled in managed-care plans that instituted even small copayments (Trivedi et al., 2008).
Quality of Care
The quality of care received in the US health-care system is problematic (IOM, 2001b, 2002c, 2003) and, although quality has improved, the gains have been modest (AHRQ, 2009b). Substantial research efforts have focused on the development and application of quality measures. In the case of women’s health, measures have historically focused on a narrow band of process indicators, typically limited to receipt of early prenatal care, mammography, and Pap tests. Although there is increasing attention to expanding the measures that started in the l990s, cost challenges and concerns about administrative burdens have slowed progress. Furthermore, despite clear evidence that there are differences in how women are treated for such conditions as stroke, cardiovascular disease, and diabetes (Brittle and Bird, 2007), most of the quality measures collected by hospitals, plans, and government agencies are not disaggregated by sex. Failure to report by sex hinders evaluation of the quality of care received by men and women and slows progress in improving quality (McGlynn et al., 1999; McKinley et al., 2002; Weisman, 2000).
The National Healthcare Quality Report and the National Healthcare Disparities Report, both issued annually by the Agency for Healthcare Research and Quality (AHRQ, 2009a,b), provide national- and state-level data on a wide array of process, intermediate, and outcome quality measures in different health settings, including outpatient, inpatient, and nursing-home care. Although women are designated as a high-priority population in the reports, there are few indicators for women’s health-care quality. Data are presented for women-specific conditions, including two dimensions of treatment for breast cancer21 and receipt of first-trimester prenatal care. In addition, a subset of sex-specific data is presented for a few indicators that are relevant to both sexes. Such indicators include exercise counseling for obese patients (obese women are more likely to be counseled than obese men), mortality from myocardial infarction (women have significantly higher mortality rate than men), new AIDS cases, and having a usual source of care. Results on those measures are stratified by age and race or ethnicity. They are the only measures, however, that receive such analysis in either report.
The Health Plan Employer Data and Information Set (HEDIS) provides health-quality indicators that are used by commercial managed-care plans and by Medicaid and Medicare managed-care organizations (NCQA, 2010). It contains indicators that measure plan performance on conditions specific to women (for example, screening for breast and cervical cancer and chlamydiosis, and testing for and management of osteoporosis in women who have had a fracture) and on prenatal care (NCQA, 2010). HEDIS also collects other indicators that are
important to women, such as weight, colorectal-cancer screening, persistence of beta-blocker treatment after a heart attack, comprehensive diabetes care, antidepressant-medication management, fall-risk management, aspirin use and discussion, and medical assistance with smoking and tobacco use. Those measures, however, are not reported by patient sex; this limits information on whether plans differ in their quality of care on indicators that are not sex specific. The same is true for differences by race and ethnicity.
Recent efforts to broaden the number of HEDIS measures to focus on women’s health conditions have been met with limited success. Some important indicators have been added to the HEDIS data set, including osteoporosis screening and treatment and chlamydiosis testing in women. However, barriers to the collection of data on other relevant conditions, such as unintended pregnancy, remain; the barriers include difficulty in collecting information, cost and administrative burdens, and low frequency (Bird et al., 2003; McKinley et al., 2002; Weisman, 2000).
There are substantial gaps in knowledge of how quality of care affects women’s health in relation to conditions that affect both men and women and conditions that affect women exclusively. Those issues are similar to challenges that have plagued clinical research, including a lack of sex-stratified analysis and measures that capture the broad array of sex-specific conditions (Correade-Araujo and Clancy, 2006; Kosiak et al., 2006). Improving quality requires improving data and analysis and is salient in relation to prevention of unintended pregnancy and to elements of maternity and older women’s health care (Kelleher et al., 1997; Rehle et al., 2004; Sakala and Corry, 2007; Wilcox, 1999).
There is substantial evidence of the role of individual behavior (for example, smoking, eating habits and physical activity, sexual risk behaviors, and alcohol use) in the prevention of disease and improvement of health. Many behavioral determinants act as common pathways to multiple conditions.
Recent years have seen increased attention to factors beyond the individual that affect health and health behavior—such as social, cultural, family, and community determinants; living and working conditions (for example, environmental exposures); and societal factors—but more work is needed on these broader determinants of women’s health.
Although research has led to a greater understanding of the determinants of women’s health, more is needed to develop effective interventions and prevention strategies to improve women’s health by influencing the determinants.
Although research has documented disparities in determinants across racial and ethnic groups, the efficacy of interventions has not been adequately evaluated in minority-group women, and this limits the generalizability of findings to subgroups of women.
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