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Annex II

DATA AND STATISTICS FOR DISABILITY-SENSITIVE POLICIES AND PROGRAMMES

Policy makers and service providers require information on those served by their programmes. Data on the characteristics of persons and the trends influencing their lives can provide critical information on their needs, as well as on those changes they encounter as they age over time. This knowledge can assist in tailoring programmes to meet the actual needs of people and in anticipating future developments. In recent years, data have been increasingly used for programme evaluation. This report has stressed the use of data as a tool for effective implementation of disability policies and programmes.

Given the impact of that the particular conceptualization chosen for disability can have on policies and programmes, a prerequisite for the effective use of data has to be a clear delineation of the concepts to be implemented in any policy or programme. For example, data used to determine medical procedures to prevent Impairments in children in a developing country will yield a different prevalence than data on persons with activity limitations on elderly people in a developed country even though the concepts measured in both sets of data may be labeled "Disability." In these cases, differences may be attributable to differences in the definition of disability, in the age of the target population and in development stage and the culture of the particular country.

Because the proper operationalization of concepts can enhance understanding of disability policies and programmes, the purpose of this annex is provide a brief overview of relevant technical issues that can influence the interpretation of data on persons with disabilities. The focus here is on analysis of retrospective data; however, these analyses also demonstrate issues related that need to be taken into account in the design of data collection. While the report demonstrated issues related to monitoring and indicators to track data over time, the goal here is to increase understanding of the interplay between particular attributes of the disabled population that can influence data analysis and, thus, should be taken into account in the design of data systems.

Disability data can be obtained from several sources, including censuses, surveys, registries and administrative records.[1] The primary source of data employed here is the 1991 Census of Namibia. Namibia has been chosen for analysis here, because it is a developing country with a recent census that reports a relatively high prevalence of persons with disabilities at 3.1 per cent. Other countries with high reported rates from a census are Mali for 1987 at 2.8 per cent, Mauritius for 1990 at 2.6 per cent and a comparable country in Africa to Namibia, Botswana for 1991 at 2.2 per cent.[2] The high percentage allows for greater variation in disability prevalence rates by demographic characteristics than usual for census results in a developing country and yet this percentage cannot be attributed to a particular disability definition or survey methodology. Canadian trend data are also employed for some comparisons to a developed country.

Four key issues are delineated here. First, some general principles that appear hold relatively constant across cultures and that influence general trends and patterns in disability prevalence are discussed. Then, the influence of the demographic composition of the general population on these trends and patterns is elaborated. Third, the result of the interaction of the general population composition and the selection of certain persons into the disabled population is explored in terms of the demographic composition of the population with disabilities. Finally, the influence of differences in demographic composition between the general and the disabled population on socioeconomic variables related to the equalization of opportunity are discerned. If these four issues are taken into account in the design of data systems, the application of some of the techniques demonstrated here can be anticipated to control for the role of demography in the analysis of progress towards the goals of the World Programme of Action concerning Disabled Persons.

As noted in the body of this report, Canada is a country that has collected disability data over time that can be employed to assess progress towards programme goals. Since the 1983-1984 period, Canada has witnessed a substantial increase in reported disability. As shown in figure B.1, the percentage of all persons reported with disabilities has grown by over four percentage points in eight years - from 11.2 per cent to 15.5 per cent. The prevalence rate has grown by over a third. While Canada's population has aged during that period, increases in the reported rate have occurred in almost all age groups. While some of this growth may be due to population ageing within the age groups, primary source for an increase in the prevalence of reported disability, note that reported increases for persons under the age of 35 years are very high. It is probable that some of this increase over time may be due to increased (or improved) reporting of disability.

Sources: Statistics Canada, Health Division and Department of the Secretary of State, Social trends analysis directorate, 1986; Report of the Canadian health and disability survey, 1983-84 (Ottawa, Author, 1986) and Health and Activity Limitations Survey (Ottawa, Supply and Services Canada, 1986 and 1991).

These results are a function of the definition employed for identifying people with disabilities in the survey. In Canada, the definitions are operationalized in a systematic and reliable way. However, a circular pattern may obtain even in these data. As more policies and programmes to serve disabled people are developed, greater numbers may report themselves or be reported by others as having a disability. These increases may, in turn, lend support for more initiatives related to disability issues.

Such data may offer a preview of future reported data for a country, like Namibia. To obtain information about disabled persons, reported data must divide the population into those with and those without disability. Although the reported disability rate for Namibia is high at 3.1 per cent in 1991, the rate for Canada in the same year is 15.5 per cent. Given that Canada is this a developed country with a comprehensive health system and that Namibia is a developing country, it is unlikely that the health of persons in Canada is worse than in Namibia. Thus, other factors must account for the differences in reported prevalence.

First, in ICIDH terms, Canada's survey employs a Disability definition while Namibia tended to use an Impairment definition. In Namibia, a person is considered disabled if they are reported as blind, deaf, mentally disabled or as having impaired speech, impaired limbs or some other condition. If a Disability definition is employed, experience has shown that a higher prevalence will obtain than if an Impairment definition is used.[3] One reason for this difference may be that people are likely to know more about what their actions and activities than about their medical conditions.

Second, as noted earlier, Canada is a developed country while Namibia is engaged in development. As Namibia develops, its view of disability may change. For example, as more children attend school, reported learning disabilities may increase. The number of disability categories may increase beyond six, as noted in the report occurred for Tunisia and Zambia, and this may result in a higher reported prevalence rate. When Namibia conducts its census, the country must obtain information on its entire population. This places constraints on the number and of the detail of questions that are asked. Canada can employ more resources in its surveys that contact a limited number of respondents.

Despite these vast differences in reported prevalence, one pattern appears to be clear -the reported disability prevalence increases by age. As given in figure B.2, the rate increases with increasing age at each of the broad age groups in Namibia. Rates double between the age groups 55 to 64 years and 65 years and older. Age differentials are much more powerful than gender differences. Rates for males are generally higher than those for females with each succeeding age group, while the female rate is usually higher than that for the males reported for the preceding age group.

Assuming the disability data are comparable over the age groups, there are two broad explanations for such increments of reported disability by age. These are as follows: (1) people can become disabled as they get older and (2) as health improvements are made over time, succeeding generations may be less likely to become disabled than their predecessors. A brief review shows how each occurs.

First, assuming few disabled persons become non-disabled as they become older, people without disabilities will encounter conditions resulting in either death or reported disability. While it should not be assumed that ageing must cause disability, it does appear that exposure to risk factors over time as people get older does contribute to reporting of high disability prevalence rates in the older ages. Note, however, that the prevalence rate declines for Canada between 10 to 14 and 15 to 34 years of age. In this occurs as people age, it would appear likely that some children are viewed as disabled in the school context, but may not reported as disabled as adults. This contradicts somewhat a hard and fast assumption that disability must increase with age. The assumption does work well when analyzing the reported data from Namibia.

Second, as medical advances occur over time, some people never catch the diseases that older generations did, while others are more easily cured. In both cases, disability may not occur when it might have in earlier times. For example, introduction of vaccine against rubella in 1969 has played an important role in reducing some blindness and deafness. In such a case, one cannot observe that age causes disability, but rather younger generations are healthier. Trends cause the reported increase in prevalence by age, not ageing or risk factor exposure. Again, note that Canada points to an exception. The data for that country showed increases, not decreases in reported disability. While the possibilities of increased reporting and ageing with age groups have been noted as possible causes, two other explanations should not be ignored. One is that certain life style changes over time may contribute to disability. The other is that medical advances may actually contribute to disability. In this case, people who in previous generations may have died are now living but are living with a disability. All of these factors are important for a country like Namibia to monitor, as its population ages.

Note that broad generalizations should not be made about prevalence rates for disability by gender. Although rates for Namibia are higher for males than females, this was not true for Canada for persons 65 years of age and over. Other data sources have shown that in some countries, rates are not significantly different by gender, while in others, rates can differ, favouring either men or women.[4]

Given that most people in many developing countries live in rural areas, the overall disability prevalence rate reported is likely to be a function of patterns in rural areas. Roughly two thirds of Namibia's people were reported in the 1991 Census as living in rural areas. Thus, as shown in figure B.3, the overall disability prevalence rate for Namibia follows a pattern more like that of rural than of urban areas. Regardless of age group, rates are higher in rural than in urban areas. Note that in the age group 65 years of age and over, the difference in reported prevalence rates between rural and urban areas is quite large. The differences between these two areas by age group are much larger than those for men and women. In many cases, the rates for particular age groups in rural areas are higher than those for older age groups in urban areas. The age pattern still is very strong, however, with rates increasing by age.

While it appears that the patterns for Namibia are quite apparent, further exploration reveals more information about the situation in that country. When rates by age group, gender and urban and rural location are explored together, the strength of the urban-rural differential becomes quite apparent. As demonstrated in figure B.4, rates are higher for men than women in both urban and rural areas for each age group. However, the rates for rural females are higher than those for urban males in each age group. Hence, one can conclude that for Namibia, the residence difference in reported rates is greater than that by gender.

Although it is reasonable to conclude for Namibia that disability increases with age and that age and urban-rural residence probably have a greater influence on reported disability than gender, it is important to examine the influence on reported disability prevalence rates of the composition of the population. For instance, if the population in rural areas is, on average, older than in rural places, that population composition could contribute to the higher reported prevalence in the rural areas. Hence, each of the variables, age, gender and urban-rural residence can impact the other.

A demographic technique called direct age standardization adjusts for this influence of age structure. It is particularly appropriate for use when analyzing disability data, because of the increasing reported prevalence with age. In using the technique for Namibia, age-specific rates for males, females and persons living in rural and in urban areas are multiplied by the population in each age group for the entire country. The resulting numbers are added together for each gender and for each residence. The resulting sum is then divided by the population of the entire country, yielding a standardized prevalence rate. This puts into play an assumption that each group has the same age structure as the country as a whole. The resulting standardized rates, given in figure B.5 are the rates that would obtain if men had the same age structure as women and if persons living in rural areas had the same age structure as those living in urban areas. This assumption reduces the effect of age on their rates.[5]

The results of the direct standardization demonstrate the influence of Namibia's age structure on its disability rates by gender. The standardized rate for men is higher than the actual rate while the rate for women is lower. The difference in rates between men and women widens indicating that women are more likely to be in age groups with higher reported prevalence for disabilities while men are more likely to be in age groups with lower rates. Thus, the fact that women are, on average, older than men in Namibia depresses the gender difference in reported disability. If both sexes had the same age structure, the differences in rates would be one third greater.

For urban and rural residence, the exact opposite occurs. Because the country is predominantly rural, the age structure of the country reflects the rural age structure. Hence, the rural rate does not change. The urban standardized rate, however, is higher than the actual reported rate for urban areas. This change reduces the difference between urban and rural areas, indicating that persons in rural areas have a greater tendency to be in older age groups reporting higher rates of disability, while those in urban areas are in younger age groups with lower disability rates. Roughly 12 per cent of the urban and rural difference in rates is accounted for by differences in the age structure between these areas in Namibia.

Again, looking at gender, age and urban-rural residence together provide more information. The widening of the rates by gender is a function of age structure in rural areas. In fact, standardization narrows the gender gap for rates in urban areas. This indicates that it is rural females who are disproportionately located in the higher age groups, not the urban females. At the same time, standardization slightly widens the gap between urban and rural men and narrows it between urban and rural women, indicating again that the age structure of rural women pushes up their rate.

Given the high prevalence of disability in the population 65 years of age and over, it is possible that the standardized rates may be primarily a function of the removal of the influence of that particular age group. Another standardized measure called the gross years of disabled life can be employed to adjusts for the influence of age structure on populations under 65 years of age. In employing the measure for Namibia, age-specific rates for males, females and persons living in rural and in urban areas are multiplied by the number of years in each age (i.e., the rate for persons 0 to 4 years of age is multiplied by 5 but the rate for those 15 to 24 is multiplied by 10). The resulting numbers are added together for each gender and for each residence. The resulting sum is the number of years under 65 years of age each person could, on average, expect to spend with a disability.[6] The resulting years, provided in figure B.6 are free of the age effect.

This measure demonstrates that differences by residence and gender exist even free of age influence for those under 65 years of age. Males can be expected to spend a slightly higher number of years with a disability than females, particularly in rural areas. The rural-urban difference is stronger with the number of years for men in rural areas being double that of men residing in urban areas. This points to a different issue than standardizing the rates did. While the age standardization demonstrated that the age structure of rural women pushed their rates up, this measure demonstrates that the age structure of rural men has the opposite influence of suppressing the disability rate. Hence, rural males are present in age groups that overall have relatively low rates, but, in each age group, the rate for rural males is very high. If this is a function of Namibia's war for independence, then these males with war-related disabilities would have a cohort effect as they age.

While standardized rates and the gross years of disabled life demonstrate the impact of population age structure on disability prevalence rates, it is useful to observe measures of the total population age structure to isolate these influences. As shown in figure B.7, the percentage of the entire population aged 65 years and over is much higher for females than males and much higher in rural areas than in urban areas. The percentage for rural females is particularly high. Ironically, the patterns for median ages in Namibia are a little different. Here the median ages are higher in urban areas than in rural areas and, although higher for rural females than males, they are higher for urban males than females. Indeed, median ages for rural males are very low.

While these patterns confirm the disproportionate influence of elderly rural females on the disability prevalence rate for females, they suggest the influence of fertility, mortality and migration patterns on disability rates. Lower mortality rates in urban than in rural areas could mean people live longer and are currently disproportionately in middle age. Lower fertility rates in urban than in rural areas also would mean disproportionately less children in that population and, again, more in middle age. Likewise, rural-urban migration patterns would yield a disproportionate number of young men and women in rural areas. In this sense, the relatively large number of women 65 years of age and over represents the fact that women live longer than men and they may not have migrated to urban areas. This would again confirm that the rate of rural men is suppressed by the age structure of rural men. Given that the rate for rural men was the highest of all groups at 4 per cent, this implies the disability rate for rural men in Namibia is probably relatively very high.

As the demographic structure of populations influences the disability rates, so, too that rate combines with the structure to determine the demographic characteristics of persons with disabilities. This is critical, because, as demonstrated by a replication of demographic measures for the disabled population presented in figure B.8, the reported demographic characteristics of the disabled population are very different from those of the general population in Namibia.

For instance, as people live longer and encounter age-related disabilities, the disabled population will contain a disproportionate number of older persons. Indeed, the proportion 65 years and over in Namibia is over five times as high for persons with disabilities than in the general population. Although this percentage is higher for rural than urban areas, the difference in this percentage between the total and disabled population is greater for urban than for rural areas with the percentage being six times as high for persons with disabilities in urban areas. Of course, this percentage is higher for females than males.

The median age of disabled persons stands in marked contrast to the total population, being double of that for all persons. Where for the entire population, median ages were higher in urban than rural areas, for the disabled population, the reverse is true. In Namibia, median ages are always higher in rural than for urban areas and higher for females than for males. The differences by residence are much greater than those by gender.

As the aging process occurs for persons in a country like Namibia, the gender distribution of persons with disabilities will reflect that of the general population. Thus, the gender distribution of the elderly disabled population is a function of the gender distribution of all people. Since, as a general rule, males have higher rates of mortality, leaving many more females to survive to older ages, females will probably constitute a majority of older disabled people. Because of differences shown earlier between countries like Namibia and those like Canada, this pattern cannot be generalized to other age groups throughout the world.

The analyses up to this point have treated disability as if it were a variable where one is reported with or without a disability. While such a viewpoint has use, it is potentially misleading for a country like Namibia where the reported disabled population is actually a function of those reported with one of six conditions. This can be of particular concern if some of those conditions have different associations with the variables previously discussed. Indeed, the data for Namibia demonstrate that these conditions do have different relations with age, gender and residence.

There are several ways one can look at disability categories. Categories can be examined individually for reported prevalence. This has, historically, been of importance to assess goals related to prevention and rehabilitation. Another important perspective is yielded when the demographic characteristics of the population with disabilities is examined. This reveals important information for use to assess equalization of opportunity.

For example, when the six conditions for Namibia are documented as a percentage of the entire population with disabilities by age group, the categories divide into roughly four groups. As shown in figure B.9, the percentage blind remains relatively constant in the age group through age 34 years and then increases steadily with each succeeding age group. The percentage deaf increases for those 65 years of age and over. The percentage with speech impairments rises through age 5 to 9 and then steadily declines with age while that for persons reported as mentally disabled rises through 15 to 24 and then declines. The proportion with impairments of limbs remains relatively flat. This has important implications for those policies and programmes that are targeted towards age groups. In the case of programmes for the elderly, over half of the disabled population is reported as blind. In younger ages, physical disabilities account for a large share of the population with disabilities. Such information can assist in providing the most appropriate service or accommodation.

It is important to remember that these are not disability prevalence rates. Indeed, the rate for all categories increases with age. The importance here is related to the targeting of services for people with disabilities. Although reported prevalence rates for speech and mental disabilities may increase with age, their shares of the disabled population reveal that those with responsibility for providing services at younger ages need to focus on the needs of persons with these two conditions.

As with age, gender and residential issues are important as well. For instance, a disproportionate number of disabled females are reported as blind or deaf, as shown in figure B.10. However, the disproportionate number of rural females in older ages has been demonstrated and those are the ages where blindness tended to be reported, as well. Hence, it is not surprising that the reported percentages for blindness are higher in rural Namibia than in the urban areas. The differences are so great that the percentages for all other disability categories are lower in urban than in rural areas.

These differences are quite consistent, too. The patterns of differences in percentages between rural and urban areas hold by gender and the differences between males and females hold by residence. This points out that the age differences shown earlier are not merely of academic interest. Rather, they point to the likelihood of service providers in rural areas interacting with an older woman with blindness, while those in urban areas may be more likely to interact with a younger man with a physical, speech or mental disability.

As the disability characteristics of particular populations are of interest, so too is the demographic distribution within each condition. One means of examining distribution is the sex ratio where the number of men is divided by the number of women and subsequently multiplied by 100. When the ratio is below 100, females predominate in that population. As shown in figure B.11, this is the case for Namibia as a whole, but not for the disabled population. Yet, although the disabled population is predominantly male, females predominate to such an extent in the blind and deaf populations that their ratios are lower than for the population as a whole. These patterns hold in both urban and rural areas, although it should be noted that the ratios for blind and deaf in urban areas are lower than for other groups. Note that all ratios are lower in urban than in rural areas.

The discussion thus far has been quite lengthy given that only four variables have been considered - age, gender, residence and disability category. The importance of this has been to set the stage to highlight differences between the disabled and total population and how those differences come to be. Once these differences are understood, an assessment of differences in the equalization of opportunity between populations with and without disabilities can take place in a realistic fashion.

For example, if one compares educational attendance patterns between the disabled and total populations of Namibia, the figures for the country as a whole are virtually misleading. Because of their historical exclusion from the educational system, disabled people are likely to have lower reported lower educational levels, as shown in figure B.12. The percentages of those never attending school 6 years of age and over in Namibia differs by a substantial amount, 30.1 percentage points, as shown in figure B.12. However, for the populations 6 to 19 years and 20 years and over, the differences are 22.5 points and 26.3 points, respectively. It thus appears that the difference for all ages is greater than that for the individual broad age groups. This means a great deal of the difference is explained by the fact that disabled persons are disproportionately 20 years of age and over where percentages never attending school are higher than for younger persons. This is a cohort effect where, as educational levels rise in a developing country, younger populations have higher educational levels than older populations. While differences between the disabled and total populations in their education are very real, the situation is exaggerated, because disabled people are more likely to be in a generation where education did not occur for many people.

However, the percentage never attending school for disabled people 6 to 19 years of age is higher than for the total population 20 years of age and over. This means that not only are the differences substantial but other issues must be explored. This is because of differences caused by the fact that persons 6 to 19 years are likely to be currently attending school, while those 20 and over are more likely to have attended school in the past. Indeed, while the difference in previous school attendance appears to be relatively small at 3.4 percentage points for all ages, the difference is 21.9 in the age group where people have finished their education - 20 years and over. When goals related to education are assessed, data for younger people should consider current school attendance, while those for older people can consider cumulative educational attainment.

When current school attendance is examined for children 6 to 19 years of age in Namibia, a greater consistency of patterns develop. Attendance patterns for the country are influenced by the situation in rural areas where girls have slightly higher rates of attendance than boys. Regardless of gender, however, differences between total and disabled children in attendance are roughly 25 percentage points in rural areas, as given in figure B.13. These differences are somewhat less in urban areas, where overall rates of attendance are likely to be higher. Somewhat interesting to note is that attendance levels in urban areas are the same for all boys and girls, but disabled girls are more likely to be attending school than disabled boys. As a result, the difference for urban girls between the total and disabled population is narrowed to slightly less than 17 points.

Where the present school attendance situation in Namibia shows consistent patterns, the past situation is more complex. The overall difference in past school attendance between the total and disabled population at 22.2 percentage points is actually greater than in either urban or rural areas by themselves, as demonstrated by figure B.14. As with the data for all age groups, this points to some of the difference overall being attributable to differences between disabled and non-disabled people in residential patterns. The difference is partially a function of the actual difference in past school attendance between disabled and non-disabled people and partially a function of the fact that disabled people live in rural areas, where educational levels have been lower.

Ironically, where disabled girls have higher rates of school attendance than disabled boys in Namibia, the past situation appears to have favoured the disabled boys. Past school attendance does not differ greatly for the total population by gender. The rates are, however, higher for disabled males than disabled females, which means differences are greater for women than men. It is important to remember the higher percentage of rural females in older ages and that, even within these populations, disabled rural women may be disproportionately older.

Moreover, the history of past school attendance information may even more complex. The data do not reveal the extent to which the current situation is a function of (a) disabled children in the past not receiving education and (b) persons who did not receive education in the past having acquired disability to a greater extent than those who had education. Inclusion of an age at onset question can assist in revealing this information.

Further explorations can reveal more information. These breakdowns can be presented by disability category. These data can be standardized by demographic factors either using the simple techniques shown earlier or by multivariate analyses. Other outcomes besides education can be examined, as well. The clear lesson here is that differences in demographic distributions between the total and disabled populations must be taken into account, particularly when the target area, such as education and employment, is also correlated with those factors. Otherwise, the role of demographic trends play in assessing progress towards programme goals may confuse the data analyses.

Disabled populations can also be compared to the total populations or their situation can be evaluated across countries. However, as noted earlier in comparing Namibia and Canada, data collections differ on the components of the disabled population depending on the specific questions that are asked. Indeed, even particular disabilities will vary depending on the particular question that is asked. It is thus difficult to generalize about the disability distribution across countries.

There are some patterns, however, that probably can be reasonably expected to hold across countries. The majority of the world's disabled population probably lives in developing countries, in rural areas and in poverty, because that is where most of the people without disabilities live. Disability can be expected to increase with age, because of improving health conditions and increased exposure by individuals to risk over time. Differences between males and females are more difficult to predict, except that the elderly population is somewhat more likely to be female, because life expectancies for women on generally longer than for men.[7] Persons with disability appear likely to have lower education levels and may be more likely to live in rural areas than those without disabilities, although these two patterns may change in the future.

However, the disabled population is quite heterogeneous and the environment can contribute to that diversity when disability is viewed as a social construct. Unfortunately, there are not several data sources to document environmental improvements in developing countries. Plans for monitoring programme results may contribute to the establishment of more data sources on the environment.

This report has stressed the importance of disability concepts and has discussed how those constructs can influence results. This annex has demonstrated how, even if constructs are held relatively constant in country measurement, demographic factors can influence data on outcomes in ways unrelated to the goals of a disability programme. These factors influence data both on prevalence rates for disability and on the characteristics of the population with disabilities. They also can impact on differences found by particular variables.

These issues are not raised to minimize the fact that differences in equalization of opportunities exist between disabled and non-disabled people. In fact, they are raised to promote useful measures of this gap. Certain techniques exist to take the role of demographic factors into account and these influences and to measure their impact. If these issues are addressed properly, at worst they can be resolved as a technical matter and at best they can assist in understanding the characteristics of people who need accommodations.

Notes:

[1] An extensive review and appraisal of sources of disability data appears in United Nations Secretariat, Department for Economic and Social Information and Policy Analysis Statistics Division, Manual for the Development of Statistical Information for Disability Programs and Policies (United Nations publication, Sales No. E.96.XVII.4), pp. 20-48.

[2] Manual for the Development …, p. 22.

[3] Ibid., p 21.

[4] Ibid., p 22-23.

[5] The standardized rate for the country as a whole is the same as the actual rate, because this rate is standardized on its own age structure. Hence, age structure has no influence on the rate.

[6] In some sense, the gross years of disabled life measure yields a result similar to an age standardized rate. However, instead of assuming a particular population age structure as the standard, it makes the equivalent of assumption that the populations in all ages are equal. The sum of the results of the multiplication of age-specific rates by the number of years in that age group yields the same result if there were 65 persons in the population with one in each age (i.e., one at age 0, one at age 1, etc.). If the gross years of disabled life measure is divided by the total number of years (i.e., 65), the resulting ratio is, in effect, a standardized rate based on a population of 65 persons, with one in each age group. This is a measure similar to the total fertility rate employed in demography or the gross years of active life employed in labour force analyses. For a discussion of the gross years of active life, see Methods of Analysing Census Data on Economic Activities of the Population (United Nations publication, Sales No. E.69.XIII.2), pp 27-29.

[7] Historically, life expectancies for men are greater than for women in the South Asian countries of India, Pakistan and Bangladesh.

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