Alzheimer's disease (AD) is the major form of age-related dementia worldwide, accounting for more than two-thirds of all dementia cases. The disease is characterized by a progressive loss of cognitive and intellectual functioning (Gilman, 1997). A number of risk factors for AD have been identified. The prevalence of AD increases with age, diabetes, depression, family history of Parkinson's disease and following head injury or exposure to solvents (Jorm et al., 1991; van Duijn et al., 1991; Ott et al., 1995; Yoshitake et al., 1995; Devanand et al., 1996). Published research further suggests that low education levels are associated with increased prevalence of clinical AD (Gatz et al., 2001; Qiu et al., 2001; Ravaglia et al., 2002). Women also have a higher risk for developing the disease than men, with the risk being markedly increased following menopause (Sherwin, 2002; Sherwin 2003). Additionally, slightly more severe cognitive deficits have been reported in AD in women compared to men (Buckwalter et al., 1993, Henderson and Buckwalter, 1994). These epidemiological trends may be a consequence of reproductive hormonal changes. Specifically, menopause results in a marked diminution in gonadal estrogen production in women (see Sherwin, 2003, for a review). Estrogen plays a pivotal role in the maintenance and function of neuronal circuits in the brain and in resistance to neuronal damage (McEwen, 2001). The neuroprotective properties of estrogen are thought to be mediated at least in part by anti-amyloidogenic, anti-oxidative and ant-inflammatory mechanisms (reviewed in Barron et al., 2006a). However, limited and somewhat mixed data exist regarding the association between endogenous levels of estrogen and cognitive decline (Manly et al., 2000; Schupf et al., 2003). Based on some of our own findings, we here consider the factors that may be useful in predicting memory decline as a risk factor for Alzheimer's disease in older post-menopausal women.

It is believed that a better understanding of the methods used by older people for suicide may lead to the development of targeted preventative strategies, which are expected to meet the challenge of sustaining this observed decline in suicide rates among older people over time in England and Wales (Lindesay, 1991; Dennis and Lindesay, 1995).

A curvilinear (U-shaped) relationship between population growth rates and elderly suicide rates fitting the quadratic equation y = a + bx + cx2 (where y is the elderly suicide rate, x is the population growth rate and a, b and c are constants) has been reported (Shah, 2009a). A theoretical model with three sequential stages incorporating population growth, elderly population size, the proportion of elderly in the general population, life expectancy and birth rates has been proposed to explain the findings (Shah, 2009a).

A recent cross-national study demonstrated that the relationship between elderly suicide rates and educational attainment is curvilinear (U-shaped curve) (Shah and Chatterjee, 2008). That study used only one-year data on suicide rates for the latest available year. These findings may have been spurious as suicide rates can randomly fluctuate year on year (Shah and Coupe, 2009). Therefore, this curvilinear relationship was examined using a one-year average of five-year data on elderly suicide rates to establish the accuracy of the earlier findings.