Guide on Gender Analysis of Census Data Full Draft of 6 December 2012 Contents
Table 43: Mexico (2010) – Unstandardized and standardized prevalence of selected types of disabilities
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- Table 44: Life table for Aruba 2010-2011 (males and females) life with and without disability
- Figure 13: Percentage of life expectancy at age (x) spent with at least one disability, by sex
- 6. Multivariate and further gender analyses
- Table 45: Vanuatu (2009) - Logistic regression of school attendance by children and adolescents aged 5-20, by type of disability and other explanatory variables
- 7. Interpretation, Policy and Advocacy
Table 43: Mexico (2010) – Unstandardized and standardized prevalence of selected types of disabilities
546. Disability-free life expectancy. For addressing the interrelationships of ageing, gender and disability, the disability-free life expectancy measure may be useful. This concept provides an indicator of elderly persons’ health condition in order to help plan adequate services and facilities. The method to calculate this disability-free life expectancy was first presented in a report of the US Department of Health Education and Welfare (Sullivan, 1971) and is often referred to as ‘Sullivan Health Expectancy method’. The Sullivan method makes use of a life table and the age-specific proportions of persons with a disability. The age-specific proportions of persons with a disability are multiplied by the corresponding number of person years (Lx) lived between ages x and x + n in the life table. On the basis of these calculated Lx values, the total after lifetime (Tx) in the disabled state can be calculated. By dividing these age-specific Tx values by the total life table survivors (lx), one obtains the life expectancy in the disabled (and non-disabled) state. Table 44: Life table for Aruba 2010-2011 (males and females) life with and without disability
Source: Population and Housing Census Aruba 2010; Helder (2012) Figure 13: Percentage of life expectancy at age (x) spent with at least one disability, by sex 547. Table 44 shows an example of the Sullivan method for Aruba. For each age-category, the percentage of total remaining life expectancy with a disability was calculated. Differences between males and females in the percentage of remaining life spent with a disability are depicted in Figure 13. Life expectancy for men on Aruba at age 60 is 19.0 years. At this age, an average man can expect to live 14.5 years disability-free and 4.5 with at least one disability. Women live longer. Their life expectancy at age 60 is 23.7 years, which is 4.7 years more than men. Of these years, they spend 16.4 years in the disability-free state and 7.3 years in the disabled state. Compared to men, most of the extra years women live are spent in the disabled state. At age 60, a man can expect to live with a disability for 23.6 per cent of his remaining years, for a woman this is 30.8 per cent. 548. A study from Thailand (Jitapunkul et al., 2003) showed similar results. Although women had a longer life expectancy than men, they spent more years in the disabled state. At age 60, women had a life expectancy of 23.9 years, and on average could expect to spend 18.2 years free from long-term disability, leaving 5.7 years (or 24 per cent of their remaining life expectancy) of years lived a disability. By contrast, men aged 60 had a remaining life expectancy of 20.3 years and a disability-free life expectancy of 16.4 years, resulting in 3.9 years (or 19 per cent of remaining life expectancy) spent in a disabled state. Women, therefore, had more years to live, both in disabled and in disability-free states. Similar conclusions were obtained in a study from the City of São Paulo, Brazil (Camargos, Perpétuo and Machado, 2005). In 2000, 60-year-old men could expect to live, on average, 17.6 years, of which 14.6 years (83%) would be free of functional disability. Women of the same age could expect to live 22.2 years, of which 16.4 years (74%) would be free of functional disability. Men would have a functional disability and be dependent on others for 1.6 years (9%), while the comparable period for women would be 2.5 years (11%). 549. Using the Sullivan method in censuses, one can at least make some predictions about gender differences in the incidence of disability on the basis of prevalence data, provided that one can control for the duration of disability. One potential solution is to compute what percentage of their remaining life men and women are expected to live with disabilities. In the example of Aruba in Table 44, this is 30.8 per cent for 60 year old women and 28.9 per cent for 60 year old men. But this result is still biased by the fact that women, because they live longer, survive to more advanced ages, where disabilities are more common. A better alternative is therefore to standardize using the same life table (i.e. the one that characterizes the mortality experience for both sexes combined) for both men and women. In this way one can see how the results would change if men and women survived equally. Based on the female life table, women aged 20 should expect to spend 8.8 years of their remaining life with a disability, compared to 5.7 for men (see Table 44). But when the life table for both sexes is used, this result changes to 7.5 years for women, compared to 6.5 for men. This implies that, although the prevalence of disabilities beyond age 20 is indeed higher for women than for men, a substantial part of the difference is also accounted for by the fact that women live longer and are therefore more likely to survive to higher ages, where disabilities are very common. 550. The Sullivan method provides an insight into gender differences in levels of disability. However, there are also some shortcomings in using this method. There are at least two methodological problems linked to this approach. The first one is that the results for both sexes may be biased because persons with disabilities may have higher mortality than the general population. This means that the number of years lived with a disability will be over-estimated in an approach that assumes the same life table for those with and without disability. 551. The second shortcoming has to do with the nature of the data used in the Sullivan method. In general, each health condition can be described in terms of its prevalence and incidence. Incidence refers to new cases of a health condition in a given period and prevalence to the number of existing cases at a certain point in time. The Sullivan method uses data on prevalence of disability, but to answer the question whether women are more or less prone to disabilities than men would also require data on the incidence of disability. These data are not available in censuses, as it would require a question on the timing when the respondent became disabled. However, prevalence and incidence are closely linked. One of the basic formulas in epidemiology states that prevalence = incidence x duration(avg). The study of Oman et al. (1999) sheds some light on the relationship between prevalence and incidence of disability. In their cohort study of 2,025 residents 55 years of age and older in Marin County, California, they found that the incidence rates for lower body physical disability were not significantly different between men and women. However, age-specific and age-adjusted prevalence rates were consistently higher among women. They attributed this difference to the longer duration women live with a disability, due to the lower recovery and mortality rates among females vis à vis males. 6. Multivariate and further gender analyses MAYBE SOME OF THE STUFF THAT IS NOW IN INDICATORS COULD ALSO BE PUT HERE. 552. Persons with disabilities generally run a greater risk of being excluded from formal education. The following example indicates how in Vanuatu children and adolescents between the ages of 5 and 20 with a disability are less likely to attend education than others of their age. Table 45 shows the results of a logistic regression in which the dependent variable was whether a child was going to school (full time or part time – value 0) or not (i.e. left school or never attended – value 1). Answers to the four questions on disability used in the population census were included, namely: Does this person have difficulty in: a) Seeing, even wearing glasses ? b) Hearing, even if using a hearing aid ? c) Walking or climbing steps ? d) Remembering or concentrating ? In addition to these four disability conditions, sex, age, rural/urban residence, and citizenship were included as control variables. 553. The results of the logistic regression clearly show that, for each of the four disability variables, children with some difficulties have higher probabilities of not attending than those without difficulties. Children who cannot hear, walk or remember/concentrate at all score much higher than those who have some difficulty. For instance, a child who cannot walk has more than 6 times higher odds of not attending school. A child with some difficulty has about 90 per cent higher chance to remain without schooling. Somewhat unexpectedly, the effect is less pronounced (but present) for children who are visually impaired. Finally, girls have a slightly higher probability of not attending school than boys, but the difference is only 3 per cent. This is more or less the same as the difference in the general population, without controling for intervening factors in a multivariate analysis: among all boys aged 5-19, 72.2 per cent attend school against 71.5 per cent of girls. Note, however, that the education of the mother has a profound effect on the chance of a child to attend school. The odds of children to be in school, with a mother who has more than primary education is 2.5 times as high (1/.406) than among children whose mother has less than primary education. Table 45: Vanuatu (2009) - Logistic regression of school attendance by children and adolescents aged 5-20, by type of disability and other explanatory variables
Source: Population and Housing Census of Vanuatu (2009) 554. Noteworthy studies that utilize census data to profile persons with disabilities within their populations are Zambia and Israel. These studies can be viewed online at the following URLs: Zambia (http://www.statssa.gov.za/census01/html/Disability.pdf), and Israel (http://www.cdc.gov/nchs/washington_group.htm). This latter study was discussed in the 10th Washington Group Meeting in Luxembourg in 2010. 7. Interpretation, Policy and Advocacy 555. Negative stereotypes about persons with disabilities may result in lowered expectations of their abilities and social policies that do not allow them to realize their full potential. Negative images and portrayals of persons with disabilities, especially older women with disabilities, influence society’s view of them, and consequently, their ability to integrate and participate in society. Negative stereotypes also increase their vulnerability to abuse and discrimination. 556. According to Rousso (2003), boys may have the advantage in obtaining assistive devices and other rehabilitation services needed to get to and participate at school. Women receive only one fifth of the rehabilitation in the world and, particularly in developing countries, men have greater access to rehabilitation services and to prosthetic and orthotic devices than women. Gender bias in access to rehabilitative services and devices is in itself a barrier to education for girls with disabilities. He recommends that more reliable data should come from increased research on such basics as the number of girls with disabilities who are of school age, their school enrollment levels, and their educational outcomes. This requires developing a consistent definition of disability as well as disaggregating data on children who are disabled by sex, and disaggregating data on girls by disability status. 557. Country examples for successful advocacy for data collection include an NGO-alliance led by North India Cerebral Palsy Association that campaigned in Punjab, India for women and men not to hide their disabilities during the census. Download 4.06 Mb. Share with your friends:
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