Abstracts

Introduction

Population aging is one of the most important social topics of the 2000s. Many changes in global demographic patterns follow historical processes of social and economic development and improvements in access to health care ¹1. GBD 2019 Demographics Collaborators. Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396:1160-203.. In Brazil, the number of people aged 60 years or over increases at a fast pace (3% annual growth) and is estimated to increase by more than 50% in the next 15 years ²2. Instituto Brasileiro de Geografia e Estatística. Síntese de indicadores sociais: uma análise das condições de vida da população brasileira: 2015. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2015., especially due to the intense socioeconomic developmental, urbanization, and health care transformations over the past 30 years ³3. Victora CG, Aquino EM, Leal MC, Monteiro CA, Barros FC, Szwarcwald CL. Maternal and child health in Brazil: progress and challenges. Lancet 2011; 377:1863-76..

Regarding health care, Brazil has evolved from a multiple system to a unified health system, with profound changes in public health policies ⁴4. Paim J, Travassos C, Almeida C, Bahia L, Macinko J. The Brazilian health system: history, advances, and challenges. Lancet 2011; 377:1778-97.. The expansion of the Family Health Strategy (FHS) ⁵5. Aquino R, Oliveira NF, Barreto ML. Impact of the family health program on infant mortality in Brazilian municipalities. Am J Public Health 2009; 99:87-93., the universalization of children immunization ⁶6. Leal MC, Szwarcwald CL, Almeida PVB, Aquino EML, Barreto ML, Barros F, et al. Reproductive, maternal, neonatal and child health in the 30 years since the creation of the Unified Health System (SUS). Ciênc Saúde Colet 2018; 23:1915-28., along with the income transfer programs since the 2000s have contributed to dramatic decreases in infant and child mortality ⁷7. Rasella D, Aquino R, Santos CA, Paes-Sousa R, Barreto ML. Effect of a conditional cash transfer programme on childhood mortality: a nationwide analysis of Brazilian municipalities. Lancet 2013; 382:57-64.. Recently, most public health efforts have focused on noncommunicable chronic diseases by promoting health behaviors and reducing risk factors ⁸8. Malta DC, Reis AACD, Jaime PC, Morais Neto OL, Silva MMAD, Akerman M. Brazil's Unified Health System and the National Health Promotion Policy: prospects, results, progress and challenges in times of crisis. Ciênc Saúde Colet 2018; 23:1799-809., a strategy that has greatly improved the overall life expectancy.

However, with the increased population aging and a smaller proportion of the active population, the demographic shift is considered a social and health challenge, associated with an increasing demand on health and social benefit needs ⁹9. Lamnisos D, Giannakou K, Jakovljevic MM. Demographic forecasting of population aging in Greece and Cyprus: one big challenge for the Mediterranean health and social system long-term sustainability. Health Res Policy Syst 2021; 19:21.. Furthermore, the older population tends to have multiple chronic health conditions that can overwhelm health budgets ¹⁰10. Paim JS. Thirty years of the Unified Health System (SUS). Ciênc Saúde Colet 2018; 23:1723-8.. The increase in life expectancy in Brazil has come with the continuous growth of the noncommunicable chronic diseases (NCDs), generating more health care needs and limitations in daily living activities ¹¹11. Veloso MV, Sousa NFDS, Medina LPB, Barros MBA. Income inequality and functional capacity of the elderly in a city in Southeastern Brazil. Rev Bras Epidemiol 2020; 23:e200093.. Currently, the NCDs are responsible for more than 70% of premature deaths and loss of quality of life, representing a substantial part of the total burden of diseases in older adults ¹²12. Cardoso LSM, Teixeira RA, Ribeiro ALP, Malta DC. Premature mortality due to non-communicable diseases in Brazilian municipalities estimated for the three-year periods of 2010 to 2012 and 2015 to 2017. Rev Bras Epidemiol 2021; 24 Suppl 1:e210005..

Questioning if rising longevity has led to additional life years spent in good or poor health is essential. The most common hypotheses on the relationship between longevity and healthy life have been proposed in the 1980s ¹³13. Fries JF. The compression of morbidity. Gerontologist 1984; 24:354-9.^,1414. Manton KG. Changing concepts of morbidity and mortality in the elderly population. Milbank Mem Fund Q Health Soc 1982; 60:183-244.^,1515. Verbrugge LM. Longer life but worsening health? Trends in health and mortality of middle-aged and older persons. Milbank Mem Fund Q Health Soc 1984; 62:475-519.. The compression hypothesis states that medical progress and healthy lifestyles are expected to result in poor health in fewer years. On the other hand, the expansion hypothesis states that medical progress is expected to lead to an increasing survival of people in poor health, resulting in the expansion of the number of years spent in poor health. The dynamic balance hypothesis, in turn, states that the increase in prevalence is balanced by the decrease in the severity of chronic diseases, resulting in a constant proportion of life spent in poor health.

The growth in longevity has generated the need for more useful measures of the aging process and different health indicators have been proposed to complement mortality by additionally accounting for morbidity, functional capacity, and disabilities ¹⁶16. Leite IC, Valente JG, Schramm JMA, Daumas RP, Rodrigues RN, Santos MF, et al. Carga de doença no Brasil e suas regiões, 2008. Cad Saúde Pública 2015; 31:1551-64.. In national health surveys, self-rated health and self-reported diagnosis of noncommunicable chronic diseases have been broadly used to establish differences in morbidity among population groups ¹⁷17. Liu Z, Zheng H, Wu Y, Wang S, Liu Y, Hu S. Self-rated healthy life expectancy changes in Jiangxi Province of China by gender and urban-rural differences, 2013-2018. Front Public Health 2021; 8:596249.^,1818. Malta DC, Bernal RT, de Souza MF, Szwarcwald CL, Lima MG, Barros MB. Social inequalities in the prevalence of self-reported chronic non-communicable diseases in Brazil: National Health Survey 2013. Int J Equity Health 2016; 15:153.^,1919. Thomson KH, Renneberg AC, McNamara CL, Akhter N, Reibling N, Bambra C. Regional inequalities in self-reported conditions and non-communicable diseases in European countries: findings from the European Social Survey (2014) special module on the social determinants of health. Eur J Public Health 2017; 27 Suppl 1:14-21.^,2020. Theme Filha MM, Souza Junior PR, Damacena GN, Szwarcwald CL. Prevalence of chronic non-communicable diseases and association with self-rated health: National Health Survey, 2013. Rev Bras Epidemiol 2015; 18(2 Suppl):83-96.. Health indicators that combine mortality data with morbidity or health status data have also been proposed for evaluating health care and prevention programs because they emphasize the quality of life in later years ²¹21. Campolina AG, Adami F, Santos JLF, Lebrão ML. A transição de saúde e as mudanças na expectativa de vida saudável da população idosa: possíveis impactos da prevenção de doenças crônicas. Cad Saúde Pública 2013; 29:1217-29.^,2222. Camargos MCS, Gonzaga MR. Viver mais e melhor? Estimativas de expectativa de vida saudável para a população brasileira. Cad Saúde Pública 2015; 31:1460-72..

The disability adjusted life year (DALY) and the healthy life expectancy are composite indicators of disease burden in a population that combine healthy life lost from mortality and morbidity. Both indicators provide summary measures of health across geographic areas and time that can inform changes in epidemiological patterns and can contribute in setting health priorities, but the two measures have different formulations and meanings ²³23. GBD 2015 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016; 388:1603-58.. The DALY quantifies the burden of disease by combining years of life lost with years lived with disability due to a specific disease and is the most important indicator of the Global Burden of Disease group ²⁴24. Azeredo Passos VM, Champs APS, Teixeira R, Lima-Costa MFF, Kirkwood R, Veras R, et al. The burden of disease among Brazilian older adults and the challenge for health policies: results of the Global Burden of Disease Study 2017. Popul Health Metr 2020; 18 Suppl 1:14.. The healthy life expectancy quantifies the number of years that a person can expect to live in good health at a certain age and is useful for the empirical analysis of the morbidity compression hypothesis ²⁵25. Moreno X, Lera L, Albala C. Disability-free life expectancy and life expectancy in good self-rated health in Chile: gender differences and compression of morbidity between 2009 and 2016. PLoS One 2020; 15:e0232445.. The most common approach to separate the total number of life years into those spent in good and poor health is the Sullivan method, which incorporates the health dimension to the classic life table ²⁶26. Sullivan DF. A single index of mortality and morbidity. HSMHA Health Rep 1971; 86:347-54.. Definitions of “healthy” are generally based on perceived health status, presence of chronic disease or disability, and functional or cognitive limitations ²⁷27. Mathers CD, Iburg KM, Salomon JA, Tandon A, Chatterji S, Ustün B, et al. Global patterns of healthy life expectancy in the year 2002. BMC Public Health 2004; 4:66.^,2828. Romero DE, Leite IC, Szwarcwald CL. Healthy life expectancy in Brazil: applying the Sullivan method. Cad Saúde Pública 2005; 21 Suppl 1:S7-18.^,2929. Hyder AA, Rotllant G, Morrow RH. Measuring the burden of disease: healthy life-years. Am J Public Health 1998; 88:196-202..

In Brazil, the healthy life expectancy has been estimated before for the total adult population according to sex and age group ²²22. Camargos MCS, Gonzaga MR. Viver mais e melhor? Estimativas de expectativa de vida saudável para a população brasileira. Cad Saúde Pública 2015; 31:1460-72.^,2828. Romero DE, Leite IC, Szwarcwald CL. Healthy life expectancy in Brazil: applying the Sullivan method. Cad Saúde Pública 2005; 21 Suppl 1:S7-18. and major regions ³⁰30. Camargos MCS, Gonzaga MR, Costa JV, Bomfim WC. Disability-free life expectancy estimates for Brazil and Major Regions, 1998 and 2013. Ciênc Saúde Colet 2019; 24:737-47.^,3131. Szwarcwald CL, Souza Júnior PR, Marques AP, Almeida WS, Montilla DE. Inequalities in healthy life expectancy by Brazilian geographic regions: findings from the National Health Survey, 2013. Int J Equity Health 2016; 15:141.. Other studies have analyzed the healthy life expectancy geographic inequalities at subnational levels ³²32. Szwarcwald CL, Montilla DER, Marques AP, Damacena GN, Almeida WS, Malta DC. Inequalities in healthy life expectancy by Federated States. Rev Saúde Pública 2017; 51 Suppl 1:7s.^,3333. Guimarães RM, Andrade FCD. Healthy life-expectancy and multimorbidity among older adults: Do inequality and poverty matter? Arch Gerontol Geriatr 2020; 90:104157.. This study investigates changes in healthy life expectancy in older adults from 2013 to 2019 and how population socioeconomic level and use of health care services are associated with this summary population health outcome using a cross-sectional ecological design with Brazilian Federated Units (UF - States) as the units of analysis.

Methods

Study design

In this article, the main outcome was the healthy life expectancy. The indicator was estimated by Sullivan’s method ²⁶26. Sullivan DF. A single index of mortality and morbidity. HSMHA Health Rep 1971; 86:347-54. according to sex, age (60, 65 and 70 years old) and UFs, in 2013 and 2019. The approach is an adaptation of the traditional life table method using two independent measures of health: the rate of being healthy by age group and the mortality component given by age-specific life expectancy provided by the Brazilian Institute of Geography and Statistics ³⁴34. Instituto Brasileiro de Geografia e Estatística. Tábuas de Mortalidade 2010/2060. Projeções da população do Brasil e Unidades da Federação por sexo e idade: 2010-2060. https://www.ibge.gov.br/estatisticas/sociais/populacao/9109-projecao-da-populacao.html?=&t=resultados (accessed on 08/Apr/2021).
https://www.ibge.gov.br/estatisticas/soc... . The method consists of removing the proportion of time lived in poor health from the total expected lifespan of a given cohort ²⁸28. Romero DE, Leite IC, Szwarcwald CL. Healthy life expectancy in Brazil: applying the Sullivan method. Cad Saúde Pública 2005; 21 Suppl 1:S7-18.. To establish the “healthy/unhealthy state” for the Brazilian older population in 2013 and 2019, we used survey data from the two editions of the Brazilian National Health Survey (PNS) as the sources of information ³⁵35. Stopa SR, Szwarcwald CL, Oliveira MM, Gouvea ECDP, Vieira MLFP, Freitas MPS, et al. National Health Survey 2019: history, methods and perspectives. Epidemiol Serv Saúde 2020; 29:e2020315..

The PNS is a nationwide household-based survey carried out by the Brazilian Ministry of Health in partnership with the Brazilian Institute of Geography and Statistics (IBGE) in 2013 and 2019 aimed at assessing health conditions and health system performance of Brazilian states and regions. The 2013 PNS was approved by the Brazilian National Ethics Research Committe (CONEP) in June 2013 (n. 328,159) and the 2019 PNS in August 2019 (n. 3,529,376).

Sampling

In the two PNS editions, the surveyed population includes Brazilian residents of private households, except those located in special census tracts. A three-stage cluster sampling (census tracts, households, and individuals) was used with stratification of the primary sampling units and random selection in each stage. Details of the sampling process are available in another publication ³⁶36. Souza-Jr PRB, Freitas MPS, Antonaci GA, Szwarcwald CL. Desenho da amostra da Pesquisa Nacional de Saúde 2013. Epidemiol Serv Saúde 2015; 24:207-16.. In 2013, 60,202 individual interviews were held and, in 2019, the number increased to 94,114 ³⁵35. Stopa SR, Szwarcwald CL, Oliveira MM, Gouvea ECDP, Vieira MLFP, Freitas MPS, et al. National Health Survey 2019: history, methods and perspectives. Epidemiol Serv Saúde 2020; 29:e2020315..

The expansion factors were estimated by the inverse of the selection probability product at each stage. IBGE recalibrated the expansion factors of the 2013 PNS to allow the comparison between the two editions of the PNS ³⁷37. Instituto Brasileiro de Geografia e Estatística. Nota Técnica - Informações referentes à revisão do plano tabular da PNS 2013. https://biblioteca.ibge.gov.br/visualizacao/livros/liv101838.pdf (accessed on 14/May/2021).
https://biblioteca.ibge.gov.br/visualiza... .

Data analysis

In this study, we used self-reported information from the household resident randomly selected to answer the individual questionnaire.

The self-reported diagnoses of the following chronic diseases were considered: hypertension, diabetes, heart disease, stroke, asthma, arthritis, chronic backpain problem, work-related musculoskeletal disorder (WMSD), depression, other mental illness, lung disease, cancer, and chronic kidney disease. For each NCD, the analysis of the degree of limitations in usual activities due to the disease was based on the following PNS question: “In general, to what degree does the disease or any complication of the disease limit your usual activities?” with five possible response options (no limitation; little; moderate; severe; very severe). The two last options were aggregated to define the presence of severe limitation due to each NCD.

For each one of the NCDs, we estimated the NCD prevalence and the proportion of people with severe limitations due to the disease. Estimates were compared between 2013 and 2019, using prevalence ratios to test the significance of the differences at the 5% level. Since the PNS design used stratification of census tracts and multiple stage cluster selection, the complex sample design was considered in the statistical analysis.

To establish the “unhealthy state”, two measures were used: self-rated health and severe limitations in usual activities due to noncommunicable chronic diseases. The analysis of the self-rated health was based on the following PNS question from the individual questionnaire: “In general, how would you rate your health?” with five possible answers (very good, good, moderate, bad, very bad). The first three options were aggregated to define “good health” and the two last categories to define “poor self-rated health”. Severe limitation to at least one NCD or poor self-rated health were used for defining the unhealthy state.

The age and sex specific rates of being unhealthy were estimated by the proportion of people reporting poor self-rated health or having severe limitations due to at least one NCD in each of the age-groups (60 or over, 65 or over, 70 or over) by sex. Proportions of unhealthy state and the corresponding 95% confidence intervals (95%CI) were estimated by sex and age for 2013 and 2019. Prevalence ratios were used to test the significance of the differences at the 5% level.

The Sullivan’s method consists of removing the proportion of time lived in unhealthy state from the total number years of life expectancy at each age 60, 65, and 70, thus transforming the life expectancy indicator into the healthy life expectancy indicator by subtracting the number of years lived in an unhealthy state ²⁶26. Sullivan DF. A single index of mortality and morbidity. HSMHA Health Rep 1971; 86:347-54..

To analyze the socio-spatial health inequalities in Brazil, we estimated the healthy life expectancy by UF. In the analysis of the subnational data, the summary population outcomes were life expectancy at 60 years old and healthy life expectancy at age 60, estimated in 2013 and 2019. The measures of geographical inequalities were the range and the inequality ratio, given by the difference and the ratio between the UF maximum and the minimum estimates, respectively.

To investigate the life expectancy and healthy life expectancy inequalities, we used the indicator of poverty “proportion of people with monthly per capita income ≤ 1 minimum wage”. As inequality measures, we used the quintile inequality ratio between the average life expectancy and healthy life expectancy estimates in the wealthiest and poorest quintiles. Under the supposition that part of the overall outcome variability is explained by the socioeconomic variable, we also used the slope index of inequalities, corresponding to the regression slope of each outcome with the poverty indicator ³⁸38. Schneider MC, Castillo-Salgado C, Bacallao J, Loyola E, Mujica OJ, Vidaurre M, et al. Methods for measuring inequalities in health. Rev Panam Salud Pública 2002; 12:398-414..

Finally, using PNS 2019 data, we conducted a principal component analysis using the UFs as the units of analysis and considering the following indicators: healthy life expectancy; proportion of people aged 18 or over with incomplete high school; proportion of people aged 18 or over with per capita income smaller or equal to one minimum wage; proportion of people living in urban areas; proportion of people with at least one medical consultation in the last 12 months prior to the survey; proportion of people who had at least one dental appointment in the last 12 months prior to the survey; proportion of people who sought health care due to illness or health problem in the last two weeks prior to the survey; and proportion of people who sought preventive care in the last two weeks prior to the survey. We used the two principal components that maximize the variance of the projected data with varimax rotation and analyzed the UF location points on the scatterplot composed by the two orthogonal dimensions representing socioeconomic status and the use of health care.

Results

Table 1 shows the NCD prevalence estimates and the limitations of the usual activities resulting from NCDs in older adults, in 2013 and 2019. In general, an increase in the NCD prevalence from 2013 to 2019 was observed. The highest prevalence estimates corresponded to hypertension increasing from 50.7 to 55% (PR = 1.08, p < 0.001); chronic backpain, from 28.2 to 31.1% (PR = 1.10, p = 0.002); diabetes, from 18.1 to 20.2% (PR = 1.11, p = 0.008); arthritis, from 16.5 to 18.2% (PR = 1.10, p = 0.030); heart disease, from 11.3 to 13.1% (PR = 1.16, p = 0.008); and depression, from 9.5 to 11.8% (PR = 1.25, p < 0.001). Prevalence of having one or more NCDs was 75.3% in 2013, significantly increasing to 79.6% in 2019 (PR = 1.06, p < 0.001).

Analysis of the limitations in usual activities due to NCDs in 2013 and 2019 shows high NCD limitation estimates for stroke, heart disease, lung disease, arthritis, chronic backpain, work-related musculoskeletal disorder, and mental illness other than depression. A significant increase from 2013 to 2019 was found only for arthritis, from 14.9 to 20.9% (PR = 1.41, p = 0.008) while significant decreases were found for hypertension, asthma, and other mental illness. No significant difference was found for the proportion of older adults with severe limitations due to the presence of one or more NCDs, remaining around 16%. The proportion of people with poor/very poor self-rated health was 12.1% in 2013 and 11.2% in 2019, with no significant variation as well (Table 1).

Table 2 shows the life expectancy and healthy life expectancy indicators at ages of 60, 65 and 70 years old by sex, in 2013 and 2019. In 2013, the life expectancy at 60 was 21.7 years and the healthy life expectancy was 17.6 years (95%CI: 17.3-17.9), corresponding to the unhealthy life proportion of 18.9% (95%CI: 17.7-20.2). In 2019, the life expectancy at 60 years old increased to 22.6 years and the healthy life expectancy to 18.4 years (95%CI: 18.2-18.6). The number of years of unhealthy living was 4.2, and the unhealthy live proportion of 18.7% (95%CI: 17.9-19.6) remains statistically unchanged. Regarding the healthy life expectancy variation by age, the number of years of healthy life decreases as people get older. At age 65, the proportion of unhealthy life was 20.3% (95%CI: 18.8-21.9) in 2013, decreasing to 19.7% (95%CI: 18.6-20.7) in 2019. At age 70, the proportion of unhealthy life decreased from 22.3% (95%CI: 20.3-24.5) to 20.9% (95%CI: 19.6-22.3), between 2013 and 2019. However, no statistically significant variation was found in the proportion of unhealthy state across all ages from 2013 to 2019.

Regarding gender differences, both life expectancy and healthy life expectancy are higher for females at all ages. However, the proportions of unhealthy living are always higher for females. In 2013, among women, the life expectancy at age 60 was 23.4, and among men, 19.9. In 2019, a similar increase of approximately 3.6 years for both sexes was identified. Healthy life expectancy at 60 years old increased 0.6 years from 2013 to 2019, among females, and 0.9 years among males. From 2013 to 2019, among women, the proportions of unhealthy life increased from 21.2% (95%CI: 19.5-23.0) to 21.8% (95%CI: 20.5-23.1), and among men, a reduction from 16% (95%CI: 14.3-17.9) to 14.7% (95%CI: 13.7-15.8), was observed, although not statistically significant. With aging, the proportion of unhealthy life for both males and females reduced. From 2013 to 2019, increases in healthy life expectancy at ages 65 and 70 of about 10 months were found, but not statistically significant (Table 2).

In Table 3, the life expectancy and healthy life expectancy at age 60 and the poverty indicator are presented by UF in 2013 and 2019. Regarding geographic inequalities, the life expectancy at 60 years ranged from 19.1 to 23.5 years, in 2013, and from 19.7 to 24.4 years, in 2019 and increasing trends were found in all UFs. The healthy life expectancy ranged from 13.6 to 19.9, in 2013, and from 14.1 to 20.1, in 2019, with an increase in 22 UFs. The poverty indicator varied from 27.8% to 77.1%, in 2013, and from 18.2% to 48.3% in 2019, showing clear improvements in socioeconomic inequalities by UF.

Table 3 shows the indicators of geographic and socioeconomic inequalities. The differences between the highest and lowest life expectancy value by UF were 4.4 years, in 2013, and 4.7 years, in 2019. The inequality ratio was 1.23 in 2013 and 1.24 in 2019. The differences in healthy life expectancy were more pronounced, 6.4 years, in 2013, and 5.3 years, in 2019 as well as the inequality ratio of 1.46, in 2013, and 1.35, in 2019. Regarding inequalities by quintile of the socioeconomic indicator, in 2013, the average life expectancy ranged from 20.2 to 22.5 years, while the average healthy life expectancy ranged from 15.0 to 18.6 years, from the poorest to the wealthiest quintile. In 2019, average life expectancy estimates ranged from 21.0 to 22.8 years, and the average healthy life expectancy, from 15.9 to 19.1 years, indicating larger socioeconomic inequalities in healthy life expectancy than in life expectancy in 2013 and 2019. The life expectancy income inequality ratio varied from 1.11 to 1.08, from 2013 to 2019, and the healthy life expectancy ratio from 1.24 to 1.20, showing a decreasing trend in both life expectancy and healthy life expectancy socioeconomic inequalities.

Figure 1 shows the regression results of life expectancy and healthy life expectancy with the poverty indicator, in 2013 and 2019, using the UFs as the units of analysis. Both summary health indicators showed a downward trend as the percentage of poverty increases, but the healthy life expectancy presents a markedly greater inequality than the life expectancy. The slope life expectancy inequality coefficients were -0,054, in 2013, and -0.080 in 2019. Slope inequality coefficients of higher magnitude were found for the healthy life expectancy, of -0.083 in 2013, and -0.146 in 2019, meaning that for each 10% increase in the poverty ratio, the life expectancy decreases by 0.5 and the healthy life expectancy by 0.8 years, in 2013, and by 0.83 years and 1.46 years, in 2019, respectively.

The principal component analysis resulted in two main axes, labeled poverty concentration and health care use, which explained 86% of the total variance. The healthy life expectancy correlated negatively with the axis of poverty concentration (-0.73) and positively with the axis of health care use (0.56). After the varimax rotation procedure, the UFs were displayed on the graph composed by the two orthogonal axes, poverty concentration (horizontal axis) and health care use (vertical axis). In quadrant I, representing better socioeconomic status and greater use of health services, are the UFs located in the South, Southeast and Central-West regions. All UFs located in the Northeast Region are in quadrants II or III, indicating the high concentration of poverty in these UFs. The Northern Region UFs are distributed in quadrants III and IV, indicating the inadequate use of health services. The Federal District is at the far left of quadrant III, with the lowest concentration of poverty among all UFs (Figure 2).

Discussion

The results of this study showed an increase in prevalence of noncommunicable chronic diseases in the older population in Brazil from 2013 to 2019, but the proportions of older people with limitations in their usual activities due to NCDs decreased or did not change significantly, with exception of limitations due to arthritis. Between 2013 and 2019, an increase in both life expectancy and healthy life expectancy at 60, 65 and 70 years old was observed, and as people get older, the greater the proportion of life in an unhealthy state. Differences by sex are in accordance with national and international literature ³⁹39. Belon AP, Lima MG, Barros MB. Gender differences in healthy life expectancy among Brazilian elderly. Health Qual Life Outcomes 2014; 12:88.^,4040. Crimmins EM, Kim JK, Solé-Auró A. Gender differences in health: results from SHARE, ELSA and HRS. Eur J Public Health 2011; 21:81-91.^,4141. Yokota RTC, Nusselder WJ, Robine JM, Tafforeau J, Renard F, Deboosere P, et al. Contribution of chronic conditions to gender disparities in health expectancies in Belgium, 2001, 2004 and 2008. Eur J Public Health 2019; 29:82-7., with higher life expectancy among women, but higher proportions of unhealthy lives.

In this study, we included the self-perception of health in the definition of unhealthy state firstly because a broader definition of health transcends the absence of death, disease and disability and incorporates concepts of well-being and quality of life ⁴²42. Buss PM, Carvalho AI. Development of health promotion in Brazil in the last twenty years (1988-2008). Ciênc Saúde Colet 2009; 14:2305-16.^,4343. Blank N, Diderichsen F. The prediction of different experiences of longterm illness: a longitudinal approach in Sweden. J Epidemiol Community Health 1996; 50:156-61.. Secondly, unhealthy state definitions based only on diagnosed morbidity could be underestimated, since they depend on access to diagnosis, admittedly uneven by region, area of residence (urban/rural), and socioeconomic status ⁴⁴44. Viacava F, Porto SM, Carvalho CC, Bellido JG. Health inequalities by region and social group based on data from household surveys (Brazil, 1998-2013). Ciênc Saúde Colet 2019; 24:2745-60.^,4545. Oliveira RAD, Duarte CMR, Pavão ALB, Viacava F. Barreiras de acesso aos serviços em cinco Regiões de Saúde do Brasil: percepção de gestores e profissionais do Sistema Único de Saúde. Cad Saúde Pública 2019; 35:e00120718.. Besides, not all NCDs are included in the PNS questionnaire.

The increase in the prevalence of several NCDs from 2013 to 2019 reflects the epidemiologic transition in Brazil ²⁴24. Azeredo Passos VM, Champs APS, Teixeira R, Lima-Costa MFF, Kirkwood R, Veras R, et al. The burden of disease among Brazilian older adults and the challenge for health policies: results of the Global Burden of Disease Study 2017. Popul Health Metr 2020; 18 Suppl 1:14.^,4646. Schmidt MI, Duncan BB, Azevedo e Silva G, Menezes AM, Monteiro CA, Barreto SM, et al. Chronic non-communicable diseases in Brazil: burden and current challenges. Lancet 2011; 377:1949-61.. Additionally, the decline in undernutrition in children and adults occurred with an increasing obesity trend across the 2000s ⁴⁷47. Conde WL, Monteiro CA. Nutrition transition and double burden of undernutrition and excess of weight in Brazil. Am J Clin Nutr 2014; 100:1617S-22S., influencing premature mortality and disabilities ⁴⁸48. Leocádio PCL, Lopes SC, Dias RP, Alvarez-Leite JI, Guerrant RL, Malva JO, et al. The transition from undernutrition to overnutrition under adverse environments and poverty: the risk for chronic diseases. Front Nutr 2021; 8:676044.^,4949. Lartey S, Si L, Lung T, Magnussen CG, Boateng GO, Minicuci N, et al. Impact of overweight and obesity on life expectancy, quality-adjusted life years and lifetime costs in the adult population of Ghana. BMJ Glob Health 2020; 5:e003332.. Findings of this study indicate increasing trends in obesity-related diseases such as diabetes and cardiovascular diseases from 2013 to 2019, but no significant increase in daily activity limitations due to those diseases were found. Also, a high prevalence of musculoskeletal conditions in the Brazilian older population was found, with growing trends in limitations due to arthritis from 2013 to 2019. As has been evidenced before, this is a group of diseases that greatly affects functional disabilities ⁵⁰50. Bezerra MAM, Hellwig N, Pinheiro GRC, Lopes CS. Prevalence of chronic musculoskeletal conditions and associated factors in Brazilian adults - National Health Survey. BMC Public Health 2018; 18:287..

According to the criterion that combines limitations due to NCDs with poor self-rated health, both healthy life expectancy and the ratio of healthy years to life expectancy increased between 2013 and 2019, except for females at age 60. With an average decrease of 10 months of unhealthy life in the period 2013-2019, ill health seems to be more compressed into the later years of life, despite the non-statistically significant relative reductions in unhealthy state. Regarding differences by gender, our results show that life expectancy is always higher among females but the proportions of people living in an unhealthy state are smaller among males, resulting in greater improvements in healthy life expectancy among men and a decrease in the healthy life expectancy gender gap. One possible reason is that women are in general more willing to admit health problems and to seek medical care than men ⁴⁰40. Crimmins EM, Kim JK, Solé-Auró A. Gender differences in health: results from SHARE, ELSA and HRS. Eur J Public Health 2011; 21:81-91.. Another explanation is based on gender differences in morbidity. While women are more likely to have non-lethal conditions and functioning problems, men are more likely to have acute severe conditions ⁵¹51. Oksuzyan A, Juel K, Vaupel JW, Christensen K. Men: good health and high mortality. Sex differences in health and aging. Aging Clin Exp Res 2008; 20:91-102..

In the analysis by UFs, both geographic inequality indicators (the difference and the ratio) show larger disparities in healthy life expectancy than in life expectancy. The inequality ratio reached 1.46 in 2013 and decreased to 1.35 in 2019, meaning that the expected number of years lived in good health by the older population of a given state is up to 35% higher than that of another state in Brazil. Despite the narrowing of healthy life expectancy inequalities among Brazilian states, the geographic inequality remains high. These findings are in accordance with previous national ¹¹11. Veloso MV, Sousa NFDS, Medina LPB, Barros MBA. Income inequality and functional capacity of the elderly in a city in Southeastern Brazil. Rev Bras Epidemiol 2020; 23:e200093.^,3232. Szwarcwald CL, Montilla DER, Marques AP, Damacena GN, Almeida WS, Malta DC. Inequalities in healthy life expectancy by Federated States. Rev Saúde Pública 2017; 51 Suppl 1:7s. and international studies ⁵²52. Zheng XY, Xu XJ, Liu YY, Xu YJ, Pan SX, Zeng XY, et al. Age-standardized mortality, disability-adjusted life-years and healthy life expectancy in different cultural regions of Guangdong, China: a population-based study of 2005-2015. BMC Public Health 2020; 20:858.^,5353. Zang E, Lynch SM, West J. Regional differences in the impact of diabetes on population health in the USA. J Epidemiol Community Health 2021; 75:56-61., which show large healthy life expectancy heterogeneity at subnational levels.

Regarding socioeconomic inequality, the results suggest that healthy life expectancy is a more sensitive indicator than life expectancy. The healthy life expectancy slope index of inequality in 2019 indicates a 10% decrease in poverty concentration means an increase of nearly one year and six months of healthy life at age 60 ³⁸38. Schneider MC, Castillo-Salgado C, Bacallao J, Loyola E, Mujica OJ, Vidaurre M, et al. Methods for measuring inequalities in health. Rev Panam Salud Pública 2002; 12:398-414.. Evidence of the effects of socioeconomic inequalities on healthy longevity are increasingly available, with results invariably unfavorable to the disadvantaged groups ⁵⁴54. Mäki N, Martikainen P, Eikemo T, Menvielle G, Lundberg O, Ostergren O, et al. Educational differences in disability-free life expectancy: a comparative study of long-standing activity limitation in eight European countries. Soc Sci Med 2013; 94:1-8.^,5555. Love-Koh J, Asaria M, Cookson R, Griffin S. The social distribution of health: estimating quality-adjusted life expectancy in England. Value Health 2015; 18:655-62.^,5656. Kim JI, Kim G. Factors affecting the survival probability of becoming a centenarian for those aged 70, based on the human mortality database: income, health expenditure, telephone, and sanitation. BMC Geriatr 2014; 14:113.. In Brazil, a recent study showed the influence of poverty on the years of life with multimorbidity ³³33. Guimarães RM, Andrade FCD. Healthy life-expectancy and multimorbidity among older adults: Do inequality and poverty matter? Arch Gerontol Geriatr 2020; 90:104157.. A study in the city of Rio de Janeiro showed huge healthy life expectancy differences in the older population and showed the importance of considering community-level socioeconomic conditions as key correlates of survival ⁵⁷57. Szwarcwald CL, Mota JC, Damacena GN, Pereira TG. Health inequalities in Rio de Janeiro, Brazil: lower healthy life expectancy in socioeconomically disadvantaged areas. Am J Public Health 2011; 101:517-23.. A study in European countries showed large and increasing inequalities in healthy life expectancy at age 50 from 2005 to 2010, partly explained by worsening of material deprivation and long-term unemployment ⁵⁸58. Fouweather T, Gillies C, Wohland P, Oyen HV, Nusselder W, Robine JM, et al. Comparison of socio-economic indicators explaining inequalities in Healthy Life Years at age 50 in Europe: 2005 and 2010. Eur J Public Health 2015; 25:978-83. while a study conducted in Spain emphasizes the importance of the education level on extending the proportion of years spent in good health ⁵⁹59. Solé-Auró A, Martín U, Rodríguez AD. Educational inequalities in life and healthy life expectancies among the 50-plus in Spain. Int J Environ Res Public Health 2020; 17:3558..

The regional and the socioeconomic inequality in the access and utilization of health services is another issue of concern ⁶⁰60. Macinko J, Dourado I, Aquino R, Bonolo PF, Lima-Costa MF, Medina MG, et al. Major expansion of primary care in Brazil linked to decline in unnecessary hospitalization. Health Aff 2010; 29:2149-60.. The principal component analysis shows that all UFs located in the less developed regions are represented in the worst quadrants, while the states located in the Southeast and South regions are in the best quadrant. Results from a study in Japan indicated that health examination results, including attitude toward improving health habits, were positively associated with healthy life expectancy ⁶¹61. Nagakura Y, Kato H, Asano S, Jinno Y, Tanei S. The significant association between health examination results and population health: a cross-sectional ecological study using a nation-wide health checkup database in Japan. Int J Environ Res Public Health 2021; 18:836..

By showing that not only mortality indicators are associated with living conditions, but also that inequalities are even more pronounced when morbidity is considered, this study draws attention to the demand for more useful population health measures to complement mortality. The socio-spatial inequalities demonstrated the excess burden of severe NCD limitations and poor health experienced by the older population living in the less developed Brazilian regions. To mitigate the effects of social exclusion, the development of strategies at subnational levels is essential not only to provide equal access to health care, but also to reduce risk exposures, prioritizing the disadvantaged population groups that will have the greater impact of interventions.

One limitation of the Sullivan’s method stems from the that it combines the flow variable “mortality” to estimate the life expectancy, and the stock variable “prevalence” to estimate the unhealthy number of years ⁶²62. Barendregt JJ, Bonneux L, Van der Maas PJ. Health expectancy: an indicator for change? Technology assessment methods project team. J Epidemiol Community Health 1994; 48:482-7.. To correct this inconsistency, two other procedures have been proposed: the double decrement method, in which the birth cohort is subjected to both disease incidence probabilities and disease specific mortality; the multi-state method, in which one or more disease states (for example, recovery or cure) are allowed. Nevertheless, previous discussions support the conclusion the Sullivan’s method is adequate and useful for monitoring population health in which transition rates and mortality rates evolve without sudden and substantial change ⁶³63. Mathers CD, Robine JM. How good is Sullivan's method for monitoring changes in population health expectancies? J Epidemiol Community Health 1997; 51:80-6..

A constraint of this study is that life expectancy uncertainties derived from the IBGE mortality projections could not be considered in the statistical analysis. Also, data on functional limitations of daily living activities in the elderly are not yet available for the PNS 2019. Finally, disabilities and severe limitations due to other diseases not considered in PNS have not been included.

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