There is also a need

There is also a need for the measurement and modelling of gender inequity at other levels of social aggregation and in other settings. The international level deserves attention. Countries show marked differences in the extent of gender inequity across a range of different dimensions (Social Watch n.d.; UNDP, 2011; WEF, 2014; World Bank, 2011). Few studies at the country level have utilised a multilevel approach to investigate men\'s individual level health (Hopcroft & Bradley, 2007; Van de Velde et al., 2013). Future work should also look at the possible interaction between gender inequity and socioeconomic factors with regard to men\'s health. A number of authors have suggested that factor xa inhibitor marginalised men may compensate for a subordinate social position by appealing to gender hierarchies through risk-taking behaviour (Courtenay, 2000a; Lohan, 2007; Pyke, 1996). These men may be at the greatest risk from gender inequity. Finally, there is a need to utilise metrics that combine multiple individual measures of gender inequity. Such an approach has the potential to identify the health impacts of gender inequity when measured as a broad social factor. However, the construction of gender inequity indexes is challenging (Grown, 2008; Permanyer, 2010), and the validity of some measures has been called into question because of methodological uncertainties (see Dijkstra, 2002; Permanyer, 2010).
Conclusion
Conflict of interest
Acknowledgements
Introduction Multimorbidity (i.e. the presence of multiple chronic conditions) has been associated with numerous adverse outcomes, including greater risk of disability, hospitalization, and death (see review by Marengoni et al., 2011). Most studies of multimorbidity have focused on older adults, despite the high prevalence of chronic illnesses among non-elderly individuals. An analysis of the 2012 National Health Interview Survey (NHIS) showed that approximately 25% of US adults age 18+ have multiple chronic conditions (Ward, Schiller, & Goodman, 2014). Among adults ages 45–64, 18.5% had 2 chronic conditions, and 13.8% had 3 or more chronic conditions (Ward et al., 2014). Trend data indicate that the co-occurrence of chronic conditions among the non-elderly has increased in recent years (Ward & Schiller, 2013). These findings all suggest that studying multimorbidity among non-elderly adults is important for researchers who wish to understand chronic disease burden in populations. Understanding multimorbidity at earlier stages of the life course is important for a number of reasons. Early and middle adulthood are periods often marked by exposure to change and psychological stress in numerous social domains (Lachman & James, 1997; Lowenthal, Thurner, & Chiriboga, 1975). During these developmental periods, individuals typically handle often demanding and conflicting social roles as they manage careers, romantic partnerships, parenthood, and caring for older relatives (Greenhaus & Beutell, 1985; Miller, 1981). Strain from managing these multiple social roles could increase vulnerability to chronic illnesses, or it could make the management of existing chronic conditions more difficult. Studying multimorbidity early in the life course also has implications for health service delivery. Because existing health care delivery models are often specialized and disease-specific, managing multiple chronic conditions is often difficult and inefficient (Valderas, Starfield, Sibbald, Salisbury, & Roland, 2009). Identifying individuals with multimorbidities early in the life course may allow for the development of integrated, comprehensive services to assist individuals in the earliest stages of disease. This could ultimately make disease management less burdensome and more efficient in the long term. Understanding multimorbidity at younger ages also allows for the development of health behavior interventions early in the disease process when they are most likely to be effective. For example, improvements in physical activity can lower blood pressure (Fagard, & Cornelissen, 2007) and improve glucose control (Boulé, Haddad, Kenny, Wells, & Sigal, 2001), which can decrease risk of cardiovascular disease and/or diabetes-related complications (Kelly et al., 2009; Sowers, Epstein, & Frohlich, 2001). Some forms of physical activity, however, may be more difficult for older adults due to greater frailty and/or more functional disability at advanced ages (frailty: Fulop et al., 2010; functional disability: U.S. Census Bureau, 2008) Thus, targeting behavior-related interventions to individuals at younger ages may prove more effective in reducing disease burden and progression.