Abstracts

Introduction

In developing countries, in which the epidemiological transition is still in an intermediate phase and mortality rates will tend to drop progressively over the upcoming years, it is important to assess the potential change in life expectancy and disability-free life expectancy resulting from the elimination of chronic diseases¹1. Strong K, Mathers C, Leeder S, Beaglehole R. Preventing chronic diseases: how many lives can we save? Lancet 2005; 366(9496):1578-1582.^,22. Omran AR. The epidemiologic transition: a theory of the epidemiology of population change. Milbank Meml Q 1971; 49(4):509-538..

Non-transmittable chronic diseases account for 50% of the total number of diseases in developing countries, with a large portion of deaths related to cardiovascular disease, chronic pulmonary disease and diabetes¹1. Strong K, Mathers C, Leeder S, Beaglehole R. Preventing chronic diseases: how many lives can we save? Lancet 2005; 366(9496):1578-1582..It is estimated that a 2% reduction in the mortality rate due to chronic diseases would avoid 36 million deaths in these countries between 2005 and 2015¹1. Strong K, Mathers C, Leeder S, Beaglehole R. Preventing chronic diseases: how many lives can we save? Lancet 2005; 366(9496):1578-1582..

Three theories have been formulated to address the effect of changes in morbidity-mortality patterns on the health status of populations²2. Omran AR. The epidemiologic transition: a theory of the epidemiology of population change. Milbank Meml Q 1971; 49(4):509-538.^,33. Omran AR. The epidemiologic transition theory. A preliminary update. J Trop Pediatr 1983; 29(6):305-316.. The first is known as the "compression of morbidity" and suggests that the life expectancy of adults has arrived at biological limits. As a result, if the incidence of debilitating diseases could be delayed, morbidity would then be compressed into a shorter period of life⁴4. Fries JF. Aging, natural death and the compression of morbidity. NEJM 1980; 303(3):130-135.^,55. Fries JF. Strategies for reduction of morbidity. Am J Clin Nutr 1992; 55(6 Supl.):1257S-1262S..

The second theory proposes that the decline in the mortality rate results from a reduction in disease lethality rates and not the reduction in incidence or progression; consequently, the decline in the mortality rate is accompanied by an increase in the number of individuals with chronic diseases and disability⁶6. Kramer M. The rising pandemic of mental disorders and associated chronic diseases and disabilities. Acta Psychiatr Scand 1980; 62(S285):382-397..

The third theory states that the decline in the mortality rate is partially due to the drop in lethality rates, but, at the same time, the incidence and progression of chronic disease must be diminishing, leading to a dynamic balance. According to this hypothesis, the years with severe or debilitating health conditions remain relatively constant, as medical interventions and changes in lifestyle reduce the progression rate of chronic diseases⁷7. Manton KG. Changing concepts of morbidity and mortality in the elderly population. Milbank Meml Q Health Soc 1982; 60(2):183-244..

Summary measures of population health that combine morbidity and mortality data are commonly used to estimate the impact of particular conditions and diseases, enabling the analysis of expected gains with the reduction or elimination of such conditions⁸8. Mathers CD, Ezzati M, Lopez AD, Murray CJL, Rodgers A. Causal decomposition of summary measures of population health. In: Murray CJL, Salomon JA, Mathers CD, Lopez AD, editors. Summary Measures of Population Health: Concepts, Ethics, Measurement and Applications. Geneva: World Health Organization; 2002. p. 274-290.^,99. Nusselder WJ. Compression of Morbidity. In: Robine JM, Jagger C, Mathers CD, Crimmins EM, Suzman RM, editores. Determining Health Expectancies. London: John Wiley & Sons; 2003. p. 35-58.. Thus, life expectancy and disability-free life expectancy may be used to assess the occurrence of the compression of morbidity in a population⁹9. Nusselder WJ. Compression of Morbidity. In: Robine JM, Jagger C, Mathers CD, Crimmins EM, Suzman RM, editores. Determining Health Expectancies. London: John Wiley & Sons; 2003. p. 35-58..

The aim of the present study was to determine whether the elimination of certain chronic diseases is capable of leading to the compression of morbidity among elderly individuals, in the city of Sao Paulo (Brazil), through an analysis of changes in life expectancy and disability-free life expectancy.

Methods

Study design and sampling

An analytical, population-based, cross-sectional study was carried out with data from the Saúde, Bem-Estar e Envelhecimento (SABE [Health, Wellbeing and Ageing]) study, that began in 2000, which was a population-based survey aimed at evaluating the living conditions of elderly individuals in seven cities in Latin American and the Caribbean (Argentina, Barbados, Brazil, Chile, Cuba, Mexico and Uruguay)¹⁰10. Lebrão ML, Laurenti R. Saúde, bem-estar e envelhecimento: o estudo SABE no município de São Paulo. Rev Bras Epidemiol 2005; 8(2):127-141.. In 2010, the SABE study was carried out in the city of Sao Paulo, involving 907 male and female elderly individuals aged 60 years or older. Sampling was probabilistic and representative of the elderly population of the city in 2010¹¹11. Instituto Brasileiro de Geografia e Estatística (IBGE). Censo demográfico: 2010. Rio de Janeiro: IBGE; 2010.. Details on the methodology of the study are described elsewhere¹⁰10. Lebrão ML, Laurenti R. Saúde, bem-estar e envelhecimento: o estudo SABE no município de São Paulo. Rev Bras Epidemiol 2005; 8(2):127-141.^,1212. Andrade FCD, Guevara PE, Lebrão ML, Duarte YAO, Santos JLF. Gender differences in life expectancy and disability-free life expectancy among older adults in São Paulo, Brazil. Womens Health Issues 2011; 21(1):64-70..

Data collection

The data were collected using a questionnaire drafted by a regional committee made up of the main researchers in each participating country or specialists on specific topics of the study. The questionnaire was made up of 11 sections addressing aspects of the lives of elderly individuals: personal data, cognitive assessment, health status, functional state, medications, use of and access to services, family and social support network, labor history, housing characteristics, anthropometry, flexibility and mobility¹³13. Albala C, Lebrão ML, León Díaz EM, Ham-Chande R, Hennis AJ, Palloni A, Peláez M, Pratts O. Encuesta Salud, Bienestar y Envejecimiento (SABE): metodologia de la encuesta y perfil de la población estudiada. Rev Panam Salud Pública 2005; 17(5/6):307-322..

Selected variables

The following socio-demographic characteristics were considered based on the pertinent scientific literature: age, gender, living arrangement, marital status, skin color, labor status and schooling¹⁴14. Gutiérrez-Fisac JL, Gispert R, Sola J. Factors explaining the geographical differences in Disability Free Life Expectancy in Spain. J Epidemiol Community Health 2000; 54(6):451-455.^,1515. athews RJ, Jagger C, Hancock RM. Does socio-economic advantage lead to a longer, healthier old age? Soc Sci Med 2006; 62(10):2489-2499.. Age was categorized in 60 to 74 years and 75 years and older. Living arrangement was dichotomized as living alone or accompanied. Schooling was categorized as no formal education, elementary education, high school education and university education (including postgraduate education). Marital status was categorized as single, married/stable relationship, widowed and divorced/separated. Labor status was dichotomized as currently working or not working.

Chronic diseases were recorded based on affirmative responses (self-reports) to the presence of nine group of chronic diseases: systemic arterial hypertension, diabetes mellitus, heart disease, lung disease, cancer, joint disease, cerebrovascular disease, falls in the previous year and nervous or psychiatric problem. The diseases used in the present study was based on the International Classification of Diseases - 10^th revision (ICD-10). Functional incapacity was defined as difficulty in performing one or more activities of daily living: dressing, eating, bathing, toileting, ambulation, fecal incontinence and urinary incontinence¹⁶16. Tinetti ME. Preventing falls in elderly persons. NEJM 2003; 348(1):42-49.^,1717. Deeg DJH, Verbrugge LM, Jagger C. Disability Measurement. In: Robine JM, Jagger C, Mathers CD, Crimins EM, Suzman RM, editors. Determining Health Expectancies. London: John Wiley & Sons; 2003. p. 203-219..

In addition to self-reports, certain conditions were only considered in the present study when under current treatment (e.g., diabetes) and when a previous medical diagnosis had been established (e.g., cerebrovascular disease).

Data analysis

Summary life tables beginning at 60 years of age for the year 2010 were calculated based on mortality and health condition information¹⁸18. Sullivan DF. A single index of mortality and morbidity. HSMHA Health Reports 1971; 86:347-354. following the steps described in demographics manuals¹⁹19. Jagger C, Cox B, Le Roy S, EHEMU. Health Expectancy Calculation by the Sullivan Method. 3º ed. Montpellier: EHEMU; 2007. EHEMU Technical Report 2006 -3.. The elderly population estimated for the year 2010 was obtained from the 2010 demographic census¹¹11. Instituto Brasileiro de Geografia e Estatística (IBGE). Censo demográfico: 2010. Rio de Janeiro: IBGE; 2010.^,2020. Brasil. Ministério da Saúde (MS). DATASUS. Mortalidade - Brasil [online]. [cited Nov 10, 2012]. http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sim/cnv/obtuf.def
http://tabnet.datasus.gov.br/cgi/tabcgi.... and information on deaths among the elderly population was obtained from the Sao Paulo State Data Analysis Foundation²⁰20. Brasil. Ministério da Saúde (MS). DATASUS. Mortalidade - Brasil [online]. [cited Nov 10, 2012]. http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sim/cnv/obtuf.def
http://tabnet.datasus.gov.br/cgi/tabcgi.... ^,2121. Fundação Sistema Estadual de Análise de Dados (SEADE). Óbitos ocorridos no município de São Paulo, 2010. São Paulo: Fundação SEADE; 2012..

The approach proposed by Nusselder et al.²²22. Nusselder WJ, Van der Velden K, Van Sonsbeek JLA, Lenior M, Van den Bos GAM. The elimination of selected chronic diseases in a population: the compression and expansion of morbidity. Am J Public Health 1996; 86(2):187-194. was used to analyze the effect of the elimination of a chronic disease on LE and DFLE. According to the authors, if a disease were eliminated, individuals would not be affected by disability or death stemming from the disease in question. Assuming independence between the causes of death and disability, the elimination of a disease would lead to a decline in the specific probabilities of death for age and the specific prevalence of disability for age.

Multivariate logistic regression analysis controlled for age was used to estimate the probability of disability with the elimination of a cause (chronic disease). Disability was the dependent variable (1 = present; 0 = absent) and the independent variables were age and disease (1 = present; 0 = absent). The probability of an individual having one or more disabilities was computed substituting the regression coefficients and scores of the respondents in the independent variables of the regression equation:

in which P is the probability of an individual having at least one disability, ε is the basis of the natural logarithm and βx = α +β1x1 + β2x2 + ..., which is a vector of regression coefficients, with x being the variables.

The effect of the elimination of a disease on the prevalence of disability was simulated by deleting the disease from the regression equation. The difference between the prevalence of disability with and without disease could then be attributed to the deleted disease²²22. Nusselder WJ, Van der Velden K, Van Sonsbeek JLA, Lenior M, Van den Bos GAM. The elimination of selected chronic diseases in a population: the compression and expansion of morbidity. Am J Public Health 1996; 86(2):187-194.. With the elimination of a disease, the probabilities of death were estimated using cause-deleted life tables. The estimated probabilities of death with the deletion of a specific cause and the prevalence of disability estimated with the deletion of the cause from the logistic regression model were combined in total LE and DFLE using Sullivan's method¹⁸18. Sullivan DF. A single index of mortality and morbidity. HSMHA Health Reports 1971; 86:347-354.. In analyses involving inferences based on the sample of information from each individual, corrections for stratification and non-responses were weighed by the inverse of the sampling fraction.

Statistical analysis and ethical considerations

Relative frequencies (%) were used for the prevalence analysis and the Rao-Scott test was used to determine associations²³23. Rao JNK, Scott AJ. On Chi - squared tests for multiway contingency tables with cell proportions estimated from survey data. Annals of Statistics 1984; 12:46-60.. The analyses were performed using either Microsoft Excel 2007 or Stata 11.1. With the latter, the weight of the sample was taken into consideration (svy command).

The original SABE study received approval from the respective ethics committees of the countries involved. In Brazil, the study received approval from the Human Research Ethics Committee of the School of Public Health of the Universidade de São Paulo and the National Research Ethics Committee (Comitê Nacional de Ética em Pesquisa).

Interpretation of data

Changes in heath expectancy based on the elimination of diseases were classified into four possible situations⁹9. Nusselder WJ. Compression of Morbidity. In: Robine JM, Jagger C, Mathers CD, Crimmins EM, Suzman RM, editores. Determining Health Expectancies. London: John Wiley & Sons; 2003. p. 35-58.:

1 - Absolute compression and relative compression of morbidity: a reduction in LE-D and increase in the percentage of DFLE in LE.

2 - Absolute compression and relative expansion of morbidity: a reduction in LE-D and a reduction in the percentage of DFLE in LE.

3 - Absolute expansion and relative compression of morbidity: an increase in LE-D and an increase in the percentage of DFLE in LE.

4 - Absolute expansion and relative expansion of morbidity: an increase in LE-D and a reduction in the percentage of DFLE in LE.

Results

A total of 907 elderly individuals were evaluated (Table 1), 640 of whom were women (64.6%). A greater percentage of women (43.52%) were aged 75 years or more in comparison to men (37.0%). No statistically significant differences were found with regard to the distribution of skin color (p = 0.4329). Regarding schooling, 11.5% and 9.4% of the men had a complete high school and university education, respectively; for women, these figures were 6.9% and 5.0%, respectively. A greater percentage of women were widowed (51.4%) in comparison to men (15.6%) and a greater percentage of women were single (4.9%) in comparison to men (3.5%). A greater percentage of men were employed at the time of the study (39.9%) in comparison to women (12.7%). A greater percentage of women lived alone (21.4%) in comparison to men (8.3%).

Table s2 and 3 display the changes in health expectancy values with the elimination of chronic diseases among women and men, respectively. In absolute terms, the gains in LE and DFLE were larger in the younger age group (60 to 74 years) in both genders. In relative terms (%DFLE in LE), the gains were higher among those women aged 75 years or older and among those men aged 60 to 74 years. For women at age 60 to 74 years, the eliminated conditions that would generate the greater proportion of years lived free of disabilities were heart disease, diabetes mellitus and hypertension (Table 2, Column 5). Among women aged 75 years or older, the eliminated conditions that would generate the greater proportion of years lived free of disabilities were diabetes, heart disease, hypertension and falls (Table 2, Column 5). For men at age 60 to 74 years, these conditions were heart disease, chronic lung disease and fall (Table 3, Column 5). Among men aged 75 years or older, the eliminated conditions that would generate the greater proportion of years lived free of disabilities were heart disease, hypertension and chronic lung disease (Table 3, Column 5).

Discussion

The results demonstrate that the elimination of chronic diseases would lead to gains in disability-free life expectancy between the ages of 60 and 74 years and at 75 years of age or older.

Regarding women in both age groups, the elimination of chronic diseases would lead to a reduction in life expectancy with disability (LE-D), signifying an absolute compression of morbidity and consequent gain in years to be lived without disability. The magnitude of this finding was greater in the younger age group (60 to 74 years). The data on DFLE in LE for women exhibited inverse behavior: for both age groups, the elimination of chronic diseases would increase the percentage of DFLE in LE, signifying a relative compression of morbidity, which would be greater in the older age group (Table 2).

Using heart disease as an example (Table 2), its elimination at age 60 to 74 years would imply a gain of 2.22 years in life expectancy among women. Moreover, these years gained would simultaneously correspond to a gain of 11.51 years of DFLE and a reduction of 9.25 years in LE-D. The greater gain in DFLE in relation to the gain in LE signifies a compression of morbidity.

Among men (Table 3), in both age groups, all diseases eliminated would lead to an decrease in LE-D, which signifies an absolute compression of morbidity, although these values are lower than the corresponding values for women. On the other hand, as the elimination of chronic diseases would lead to an increase in the percentage of DFLE in LE, a relative compression of morbidity would be expected (also at percentages far below those found among women). One may therefore say that the gains in LE would be greater among men than women, but at the cost of a smaller absolute compression of morbidity and a smaller conversion of years with disability into years without disability (relative compression of morbidity). However, the relative compression of morbidity among men would be greater in the younger age group.

Analyzing studies carried out in other countries, the elimination of chronic diseases would lead to the compression of morbidity in some situations²⁴24. Perenboom RJM, Van Oyen H, Mutafova M. Health Expectancies in European Countries. In: Robine JM, Jagger C, Mathers CD, Crimins EM, Suzman RM, editors. Determining Health Expectancies. London: John Wiley & Sons; 2003. p. 359-376.. Data from Australia indicate that the elimination of circulatory disease from the elderly population would lead to greater gains in years of healthy living in both men and women, followed by the elimination of neoplasms in men and musculoskeletal disease in women²⁵25. Mathers CD. Gain in health expectancy from the elimination of diseases among older people. Disabil Rehabil 1999; 21(5-6):211-221.. Similar results are found in a study carried out in the United Kingdom, with the exception of gains obtained from the elimination of accidents and poisoning in both genders²⁶26. Mathers CD. Cause-deleted health expectancies. In: Robine JM, Jagger C, Mathers CD, Crimmins EM, Suzman RM, editors. Determining Health Expectancies. London: John Wiley & Sons; 2003. p. 149-174..

In the Netherlands, the elimination of heart disease, arthritis and low back pain would lead to greater gains in DFLE. Arranging the diseases in terms of impact, differences were noted between genders. The elimination of heart disease would have the greatest impact among men, whereas the elimination of arthritis and low back pain would have the greatest impact among women. Similar results were found in this population for individuals aged 65 years or older, with the exception of the finding regarding heart disease, the elimination of which did not imply either a relative expansion or compression of morbidity²²22. Nusselder WJ, Van der Velden K, Van Sonsbeek JLA, Lenior M, Van den Bos GAM. The elimination of selected chronic diseases in a population: the compression and expansion of morbidity. Am J Public Health 1996; 86(2):187-194.. In Denmark, a study involving an elderly population found that the elimination of fatal diseases (such as cardiovascular disease) would lead to a relative compression of morbidity, whereas an absolute compression would be achieved with the elimination of non-fatal diseases, such as osteoarticular diseases²⁷27. Brønnum-Hansen H, Juel K, Davidsen M. The burden of selected disease among older people in Denmark. J Aging Health 2006; 18(4):491-506.. In the USA, the elimination of deaths due to heart disease would result in greater gains in life expectancy (3 years for men and 4 years for women at 70 years of age); most gains in DFLE occurred at 70 years of age, while this trend changed with the advance in years, as the elimination of heart disease among very elderly individuals would lead to the addition of more years with disability than without disability²⁶26. Mathers CD. Cause-deleted health expectancies. In: Robine JM, Jagger C, Mathers CD, Crimmins EM, Suzman RM, editors. Determining Health Expectancies. London: John Wiley & Sons; 2003. p. 149-174.^,2828. Hayward M, Crimmins E, Saito Y. Cause of death and active life expectancy in the older population of the United States. J Aging Health 1998; 10(2):192-213..

One limitation of the present study is the presupposition of independence in causes of death. However, information on multiple causes of death is not widely available and this limitation will continue to be difficult to overcome until greater knowledge is acquired regarding the relationship of dependence among different causes of death in Brazil. Furthermore, the presupposition of independence may have led to an overestimation of the reduction in mortality at more advanced ages, when the coexistence of several diseases becomes more frequent. The fact that some diseases are risk factors for others could lead to the underestimation of the final cause of death. Moreover, as groups of diseases were considered (e.g., joint diseases), no consideration was given to the fact that different diseases have different impacts in terms of disability depending on the age group.

Another limitation regards the fact that the probability of death was not related to disability in the regression analysis. Moreover, the fact that disability can predispose an individual to fatal disease was not taken into account (e.g., disability stemming from cerebrovascular disease can lead to pneumonia, with a consequent increase in the mortality rate).

Self-reported information can lead to biases in the results. However, previous studies on the elimination of diseases have involved self-reported diagnoses²²22. Nusselder WJ, Van der Velden K, Van Sonsbeek JLA, Lenior M, Van den Bos GAM. The elimination of selected chronic diseases in a population: the compression and expansion of morbidity. Am J Public Health 1996; 86(2):187-194.^,2626. Mathers CD. Cause-deleted health expectancies. In: Robine JM, Jagger C, Mathers CD, Crimmins EM, Suzman RM, editors. Determining Health Expectancies. London: John Wiley & Sons; 2003. p. 149-174.^,2929. Jagger C, Matthews R, Melzer D, Matthews F, Brayne C. Educational differences in the dynamics of disability incidence, recovery and mortality: Findings from the MRC Cognitive Function and Ageing Study (MRC CFAS). Int J Epidemiol 2007; 36(2):365-367.. Studies carried out in Brazil demonstrate the validity of self-reported information in detecting health conditions³⁰30. Barreto SM, Figueiredo RC. Chronic diseases, self-perceived health status and health risk behaviors: gender differences. Rev Saude Publica 2009; 43(Supl. 2):1-9.

31. Marcopito LF, Rodrigues SSF, Pacheco MA, Shirassu MM, Goldfeder AJ, Moraes MA. Prevalence of a set of risk factors for chronic diseases in the city of São Paulo, Brazil. Rev Saude Publica 2005; 39(5):1-7.

32. Mendoza-Sassi RA, Béria JU. Gender differences in self-reported morbidity: evidence from a population-based study in southern Brazil. Cad Saude Publica 2007; 23(2):341-346.

33. Virtuoso JF, Mazo GZ, Menezes EC, Cardoso AD, Dias RG, Balbê GP. Perfil de morbidade referida e padrão de acesso a serviços de saúde por idosos praticantes de atividade física. Cien Saude Colet 2012; 17(1):23-31.^-3434. Campolina AG, Dini OS, Ciconelli RM. Impacto da doença crônica na qualidade de vida de idosos da comunidade em São Paulo (SP, Brasil). Cien Saude Colet 2011; 16(6):2919-2925.. Cardiovascular disease and diabetes appear to be adequately reported by individuals due to the universal coverage of the Brazilian public health system³⁵35. Lima-Costa MF, Loyola Filho AI, Matos DL. Tendências nas condições de saúde e uso de serviços de saúde entre idosos brasileiros: um estudo baseado na Pesquisa Nacional por Amostra de Domicílios (1998, 2003). Cad Saude Publica 2007; 23(10):2467-2478..

Another aspect to consider regards the non-inclusion of institutionalized elderly individuals, which may have led to the overestimation of the effect of eliminating chronic disease for this population, as such individuals could be living in institutions for reasons other than chronic diseases and their consequences³⁶36. Sá IPC, Almeida Júnior LR, Corvino MPF, Sá SPC. Condições de saúde bucal de idosos da instituição de longa permanência Lar Samaritano no município de São Gonçalo - RJ. Cien Saude Colet 2012; 17(5):1259-1265..

The lack of longitudinal data and the use of prevalence rates based on Sullivan's method have disadvantages when considering changes in mortality and disability among the elderly population over time³⁷37. Barendregt JJ. Incidence and prevalence-based SMPH: Making the Twain Meet. In: Murray CJL, Salomon JA, Mathers CD, Lopez AD, editors. Summary Measures of Population Health: Concepts, Ethics, Measurement and Applications. Geneva: World Health Organization; 2002. p. 221-231.. However, when disease-related mortality and disability are eliminated simultaneously, the dynamic effects of these transformations in a particular population no longer exist. Moreover, due to their simplicity, life tables in a number of studies have been calculated using Sullivan's method, which is the most widely used technique in different countries, thereby facilitating future comparisons³⁸38. Mathers CD, Robine JM. How good is Sullivan's method for monitoring changes in population health expectancies. J Epidemiol Community Health 1997; 51(1):80-86.^,3939. Imai K, Soneji S. On the estimation of disability-free life expectancy: Sullivan's method and its extension. Journal of the American Statistical Association 2007; 102(480):1199-1211..

Future studies should employ a longitudinal design, which would permit a better understanding of the relationships between different chronic diseases and transitions in health status, especially with regard to functional capacity²⁹29. Jagger C, Matthews R, Melzer D, Matthews F, Brayne C. Educational differences in the dynamics of disability incidence, recovery and mortality: Findings from the MRC Cognitive Function and Ageing Study (MRC CFAS). Int J Epidemiol 2007; 36(2):365-367.^,4040. Cruz GT, Saito Y, Natividad JN. Active life expectancy and functional health transition among Filipino older people. Canadian Studies in Population 2007; 34(1):29-47.. Moreover, studies addressing the multi-causality of deaths and relationships between multi-morbidity and functional capacity could contribute toward the understanding of the compression of morbidity in this population.

Based on the findings of the present study, the elimination of chronic diseases from the elderly population could lead to a compression of morbidity in men and women at both 60 to 74 years of age and 75 years of age or older. Greater gains in disability-free life expectancy would occur in the female gender due to absolute and relative compression of morbidity.

References

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History