Sociodemographic factors associated with the place of death of the Brazilian population and regional variations

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Limited information is available for middle-income countries, like Brazil. This study identified and compared the association of sociodemographic factors (age, sex, ethnic/skin color identification, educational level and marital status) with the place of death in Brazil and its regions, in a population level research. In Brazil, death at hospital was more frequently associated with socioeconomic privilege groups (white people, higher educational level, more developed regions). Older age groups, male, unmarried groups and lower education level were related with higher odd to death at home, which raises concerns about unassisted or limited support in these occurrences. The debate on social demands, public policies and bioethical challenges associated with the assistance offered on death occurrences is need. Mortality Sociodemographic Factors Social Determinants in Health Vulnerable Populations Cause of Deaths Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The way in which society experiences the dying process, and the multiple aspects associated with this moment, is influenced by social, cultural, economic and political aspects. From the perspectives of healthcare system and public policies, understanding the factors associated with the death and dying process, such as the place of death, the determined causes, the sociodemographic influences and the differences between regions of the country can support the definition of planned arrangement and assistance that meet the multiple needs associated with the end of life[ 1 , 2 ]. Although death in the hospital environment is a paradigm in the current health system, in some countries, particularly those with higher income and wider access to palliative care provision for the population, there is an increasing trend of deaths at home or in non-hospital institutions[ 3 , 4 ]. On the other hand, studies carried out in low or middle-income countries show great variation in the distribution of places of death, limitation of long-term care institutions and death at home may indicate lack of access to structured health services[ 1 , 4 , 5 ]. Previous studies have identified that the distribution of place of death has great variability between countries, but Brazil stands out for the large proportion of deaths in hospitals compared to other nations, and, less frequently, at homes, with this distribution varying among its states[ 1 , 5 ]. However, in recent years, several social, epidemiological and public policy changes may affect the way people die and how healthcare support is offered at the end of life, such as population aging, the COVID-19 pandemic period, economic crises, creation or changes in health programs and public policies, among other factors [ 4 , 6 , 7 ]. Thus, this study aims to identify and compare the sociodemographic factors associated with the distribution of the place of death of the Brazilian population, and to verify the differences among its regions. METHODS The retrospective ecologic study was carried out at population level by searching the public database of the Mortality Information System (SIM)[ 8 ] of the Brazilian Ministry of Health, to collect data relating to all deaths registered in the country and classified in its five regions (north, northeast, central-west, southeast and south), between 2002 and 2022. Figure 1 . Brazilian regions map, classified by HDI, with total population and percentage (2022). Source: IBGE, 2022. PNUD, 2022. The proportions of deaths were analyzed according to the place of occurrence (hospital, home, other healthcare facilities and other places [public places, other places and unknown]), age group (less than 1 year old, between 1 and 19 years old, 20 to 59 years old, 60 to 79 years old and over 80 years old), sex (male and female), ethnic/skin color reported classification (white, black, brown/mixed color, yellow/asian and indigenous people), educational level by years of studies (none, between 1 to 7 years and 8 or more years of study) and marital status (married, single, widowed/divorced), according to the standardized classifications available in the database. A descriptive time-series analysis of the places of death distribution and sociodemographic characteristics data was applied for the last 20 years period, presenting frequencies and percentages. A population deaths rates analysis for COVID-19 pandemic period was done. To measure the relative association of sociodemographic factors with the place of death, for national and each region, a non-adjusted odd-ratio (OR) was applied and presented with 95% confidence interval (CI). The Z test was used and p-value less than 0.05 was considered for statistical significance, in comparison between the reference category for each sociodemographic factor, performed by the MedCalc software (MedCalc Software Ltd.). RESULTS Over the period there was an average increase in the number of deaths of 1.9% per year between 2002 (982,807 deaths) and 2019 (1,349,801), due to the COVID-19 pandemic there was an increase of 15.3% in 2020, and a further 17.7% in 2021, reaching a peak of 1,832,649 deaths, followed by a reduction of -15.7% in 2022. Place of death distribution and region comparison The proportional average of the distribution of deaths in hospitals was 66.8%, from 2002 to 2019 (minimum of 65.2, in 2003) and reached 69.1% during the pandemic period, in 2021. In 2022, there was a difference of 9,7% between southt (68,4%) and northeast (58,7%). Deaths at home progressively reduced from 23. 5%, in 2002, to 18.4% in 2021, and there was a slight increase to 21,1%, in 2022. Despite having a small proportional distribution, there was a significant increase in deaths in other healthcare facilities, from 1.5% in 2002 to 6.9% in 2022 (Fig. 1 ). As a result, in the northeast there was a higher proportion of deaths at home, with 27.2%, and 17.8% in the southeast (Table 1 ). The occurrence of deaths in other healthcare facilities, which covers emergency care settings, primary health units, long-term care institutions, among other health services, was 6.9% in Brazil, and varied from 4.2% in south region to 8.9% in the southeast region. Deaths in other places, such as public roads, different or ignored locations, were higher in the north region (10.7%), and 4.9% in the southeast (Table 1 ). Sociodemographic factors association Mortality under 20 years of age is particularly high in the north region of Brazil, representing around 8.5% of total deaths, while in other regions it is less than 5%. For occurrences up to 1 year old, there is a greater prevalence of deaths in hospital (91.9% in Brazil). Deaths over 60 years old represent 71.1% of occurrences in Brazil, and are highest in the south (74.8%) and southeast regions (74.1%), and lowest in the north region (59.2%). The group between 60 and 79 years old has a higher proportion of deaths in hospital (69.5%), compared to the group over 80 years old (64.7%), which presents a deviation towards home deaths (19.9% ​and 26.3%, respectively), as found in previous studies[ 4 ]. This shift was more significant in the northeast (25.7–37.9%) and north (from 22–34.6%) regions (Fig. 2 ). The chance of dying in a hospital compared to at home in the population under 60 years of age was 1.2 times greater compared to those aged 60 years or over, and was even greater in the north and northeast regions (1.6 times greater). Only in the central-west region this difference was not significant (Table 2 ). In relation to the classification by sex, evaluated for those over 20 years of age, it is observed that men have a lower proportion of deaths in hospitals, with a deviation towards occurrences in other places, probably due external causes of deaths in public places or traffic roads, in which 87, 4% are men. In Brazil, men have a lower chance of dying in hospitals than women (OR = 0.87, 95% CI = 0.86–0.87), with this difference being smaller in the central-west region (OR = 0. 78, 95% CI = 0.75–0.80). Regarding differences related to skin color, the population classified as white has a 1.2 times higher chance of dying in hospitals than at home, when compared to other non-whites groups, and this difference was not significant only in the southeast region. The proportion of deaths of white and yellow individuals are similar, and higher in hospitals than brown and black individuals. For the population classified as indigenous people, the proportion of deaths in hospitals was even lower. These deaths outside the hospital are displaced to occurrences in other healthcare facilities for black people, on public roads for brown/mixed color and indigenous people, and at home and other places for indigenous people (Fig. 2 ). When analyzing educational level, it appears that the population identified with no education had a lower proportion of deaths in hospital (58.4%), compared to those with 1 to 7 years of study (65.9%) and those with more than 8 years of study (68.2%). These differences were greater in the north and northeast regions, with an increase in the proportion of incidents at home. When comparing occurrences odds in hospital and home deaths with the time of study in the population over 20 years old, it appears that the population with less time in school had a 30% lower chance of dying in hospital, compared to the population with 8 or more years of education. This difference was greater in the north and northeast regions, where the difference was 41% and 45% respectively. The difference in the chance of dying in hospital was smaller in the southeast, with a 12% lower chance in the population with less education. In general, in Brazil those identified as single had a proportion of death in hospital of 57.9%, and lower compared to the group of widowed/divorced (66.6%) and married (70.2%), with those difference particularly allocated to occurrences on public roads. The population over 20 years old registered as married had a 1.34 times greater chance of dying in hospital, than compared to at home death. Table 1 Distribution of deaths in Brazilian population (2022), classified by place of death and sociodemographic factors. BRAZIL (%) North (%) Northeast (%) Central-West (%) Southeast (%) South (%) Pop. (2022) 214.828.540 100,0% 19.133.894 8,9% 57.951.331 27,9% 16.905.776 7,9% 90.231.492 42,0% 30.606.047 14,2% Total deaths 1.544.266 100,0% 96.552 6,3% 403.132 26,1% 103.892 6,7% 695.469 45,0% 245.221 15,9% PLACE OF DEATH Hospital 1.008.532 65,3% 59.532 61,7% 236.831 58,7% 69.913 67,3% 475.791 68,4% 166.465 67,9% Home 326.175 21,1% 21.784 22,6% 109.822 27,2% 19.476 18,7% 123.801 17,8% 51.292 20,9% Other healthcare facilities 106.505 6,9% 4.864 5,0% 23.010 5,7% 6.370 6,1% 61.949 8,9% 10.312 4,2% Other places 103.054 6,7% 10.372 10,7% 33.469 8,3% 8.133 7,8% 33.928 4,9% 17.152 7,0% AGE GROUP Less than 1 y/o 32.257 2,1% 4.347 4,5% 9.985 2,5% 2.813 2,7% 11.387 1,6% 3.679 1,5% 01–19 y/o 28.041 1,8% 3.830 4,0% 9.555 2,4% 2.404 2,3% 9.067 1,3% 3.185 1,3% 20–59 y/o 383.983 24,9% 30.913 32,0% 109.841 27,3% 30.010 28,9% 158.445 22,8% 54.774 22,4% 60–79 y/o 602.365 39,0% 33.626 34,8% 143.444 35,6% 39.986 38,5% 282.723 40,7% 102.586 41,9% 80 y/o and over 495.283 32,1% 23.522 24,4% 129.552 32,1% 28.505 27,4% 232.464 33,4% 80.563 32,9% SEX* Male 806.722 54,4% 52.824 60,0% 211.669 55,3% 56.807 57,7% 356.848 53,0% 128.574 54,0% Female 674.789 45,5% 35.246 40,0% 171.301 44,7% 41.701 42,3% 316.960 47,0% 109.581 46,0% ETHNIC/SKIN COLOR* White 765.135 51,6% 18.152 20,6% 98.976 25,8% 42.412 43,0% 406.907 60,4% 198.688 83,4% Brown/Mixed 549.793 37,1% 61.193 69,5% 236.603 61,8% 45.787 46,5% 181.734 27,0% 24.476 10,3% Black 128.000 8,6% 5.558 6,3% 35.172 9,2% 7.465 7,6% 68.752 10,2% 11.053 4,6% Yellow/Asian 9.136 0,6% 390 0,4% 1.081 0,3% 641 0,7% 5.913 0,9% 1.111 0,5% Indigenous people 4.020 0,3% 1.499 1,7% 962 0,3% 724 0,7% 467 0,1% 368 0,2% EDUCATION LEVEL (years of study) * None 243.533 16,4% 20.480 23,3% 108.189 28,2% 17.616 17,9% 73.028 10,8% 24.220 10,2% 1 to 7 years 648.529 43,8% 35.476 40,3% 145.501 38,0% 40.405 41,0% 311.782 46,3% 115.365 48,4% 8 years or over 371.454 25,1% 21.793 24,7% 68.944 18,0% 28.399 28,8% 192.307 28,5% 60.011 25,2% MARITAL STATUS* Married 469.984 31,7% 24.716 28,1% 114.829 30,0% 30.293 30,7% 219.002 32,5% 81.144 34,1% Widowed/divorced 491.692 33,2% 20.856 23,7% 104.758 27,4% 31.328 31,8% 248.816 36,9% 85.934 36,1% Single 375.448 25,3% 29.311 33,3% 118.840 31,0% 25.911 26,3% 154.789 23,0% 46.597 19,6% Data classified as Unknown was excluded. *Only in adult population (20 years old or over). Table 2. Association analysis (odd-ratio/ confidence interval) of sociodemographic factors with place of death, in adults (20 years old and over) in Brazil (2022). BRAZIL North Northeast Central-West Southeast South OR CI (95%) p value OR CI (95%) p value OR CI (95%) p value OR CI (95%) p value OR CI (95%) p value OR CI (95%) p value Age group 1,21 1.19-1.21 1,64 1.58- 1.70 1,60 1.57-1.62 1,03 0.99-1.06 1,04 1.02-1.05 1,05 1.02-1.07 (Ref.: 60 y/o or over / Under 60 y/o) P < 0.01 P < 0.01 P < 0.01 * P = 0.08 P < 0.01 P = 0.01 Sex 0,87 0.86-0.87 0,87 0.83-0.89 0,91 0,89-0,92 0,78 0.75-0.80 0,86 0.84-0.87 0,86 0.84-0.88 (Ref.: Male/ Female) P < 0.01 P < 0.01 P < 0.01 P < 0.01 P < 0.01 P < 0.01 Ethnic/ Skin color 1,22 1.21-1.23 1,20 1.15-1.25 1,03 1.01-1.04 1,14 1.10-1.18 1,01 0.99-1.02 1,11 1.07-1.13 (Ref.: White/ Non white) P < 0.01 P < 0.01 P < 0.01 P < 0.01 * P = 0.12 P < 0.01 Educational level 0,71 0.70-0.71 0,59 0.56-0.61 0,56 0.54-0.56 0,78 0.75-0.80 0,88 0.86-0.89 0,84 0.81-0.85 (Ref.: Under 7 year of study/ 8 yers or over) P < 0.01 P < 0.01 P < 0.01 P < 0.01 P < 0.01 P < 0.01 Marital status 1,34 1.33-1.35 1,34 1.29-1.39 1,27 1.24-1.29 1,46 1.40-1.51 1,37 1.34-1.38 1,36 1.32-1.38 (Ref.: Married/ Unmarried) P < 0.01 P < 0.01 P < 0.01 P < 0.01 P < 0.01 P 0,05). DISCUTION In this study, there was a progressive increase in the number of deaths of around 1.9% per year, with the exception of the years 2020 and 2021, which were greatly influenced by the COVID-19 pandemic, with a partial return to the trend in the values ​​analyzed in 2022[ 7 , 9 ]. The distribution of deaths maintained the predominance of occurrences in hospitals, a slight reduction in those recorded at home, and with a significant increase in deaths in other healthcare facilities, although to overall distribution it have a small population proportion, as found in previous studies before COVID-19 pandemic period[ 1 , 5 ]. It can be seen that during the pandemic peak of death, there was an increase of 35,8% of yearly deaths in comparison from 2019 and 2021 [excess death of 480,848 in 2021). Considering population proportion of deaths, while in 2019 was 0,64% (with mean 0,59% from 2002 and 2019), it reached 0,86% in 2021, and return to 0,72 in 2022. The impact on place of death distribution occurred mainly in hospitals settings, with increase of 2,1%, and 0,5% for other health care facilities[ 9 ]. While occurrences at home decreased 1,2%, as seem for other places (less 0,8%, comparison between 2019 and 2021). Among Brazilian regions, the north and northeast have lower proportions of deaths in hospital. While in the north these may be explained to the increased frequency on public roads and other public places, in the northeast these deaths were allocated at homes and public roads. The southeast is the only region in which deaths in other healthcare facilities have a higher proportion than the national average, however, more data is needed to verify in which kind of establishments these occurrences are distributed. For example, deaths in emergency care settings may indicate an unexpected or unplanned event, while occurrences in long-term care institutions (ex. nursing homes and hospices) are often associated with aging and chronic diseases, and may have an expected and planned end of life. However, this information is not classified in the analyzed database, and more detailed study is necessary. Thus, there is a disparity in the distribution of places of death among the regions of Brazil, with the southeast region presenting higher rates of institutionalization of deaths in health services (hospital and other health facilities), while the northeast and north regions have lower frequency of these occurrences, with a higher proportion of deaths at home. This difference may be associated with Human Development Index (HDI) differences among Brazilian regions, since states form the north and northeast have lower HDI ranking (mean of 0,70), while Southeast and South have the highest HDI (mean of 0,79)[ 5 , 6 , 10 ]. Population aging, associated with an increased frequency of chronic diseases as main cause of mortality, implies an overload on healthcare services. In Brazil, over 70% of all deaths occurs in the population over 60 years old, as found by previous studies[ 1 , 5 , 6 ]. There is a shift in the occurrence of death from the hospital to the home in the older age groups, from 20–26.3%, for the population aged 60 to 79 years and over 80 years, respectively. This shift is more significant in the northeast (25.7–37.9%) and north (from 22–34.6%) regions. In the north and northeast regions, the chance of dying in hospital under the age of 60 was 1.6 times greater than in the older age groups. As identified by previous studies[ 1 , 5 ], when comparing the distribution of deaths between the sexes in Brazil, women had a higher proportion of deaths in hospitals (69%) compared to men (62%). The biggest difference was observed in the northeast, followed by the central-west. These difference may be associated with the impact of mortality from external causes on the male population, with a higher proportion of deaths on public roads and other places, being higher in the north and northeast regions[ 5 , 11 ]. For marital status, considering the population over 20 years old, married person have a greater chance of dying in hospital, compared at home group. Similar finds were reported by other recent study[ 1 ]. These finds may partially explain due marital status be influenced by age, with single (and younger) population being more affected by death from external causes. When comparing among Brazilian regions, in the northeast and north, widowed and divorced person had a higher frequency of death at home than married and single people. A deeper analysis is need to investigate how different contexts of familiar and marital status affect the place of death distribution. From the total deaths in Brazil in 2022, 51.6% of individuals were classified as white, 45.7% as black or brown/mixed color, and a small portion as yellow and as indigenous people. Compared to other regions, the north is the only region in which indigenous people have a lower proportion of deaths in hospitals, compared to the national average. In general, there is a higher proportion of deaths among the indigenous population at home or classified as other places. Considering the limited information about the end-of-life context of the indigenous population in Brazil, more studies are needed to identify in which places and conditions these deaths occur, considering that this population have important barriers to access healthcare services[ 12 ]. The difference in death frequencies in hospital between white and non-white people indicates an inequity access to less social privilege groups, and, in the context of end-of-life care, it may imply in lower and later support for suffering conditions[ 13 ]. Education level seems to be a social factor related to access to health care at the end of life in Brazil, where the population with less schooling had a 29% lower chance of dying in hospital, compared to the population with 8 or more years of education. The difference was greater in the north and northeast regions, where the difference was 41% and 45% respectively. The difference in the chance of dying in hospital was smaller in the southeast, with a 12% lower chance in the population with less education. This fact was also identified by previous research[ 1 , 7 ]. For vulnerable population in Brazil, the occurrence of death at home indicates lack of access to structured health services, even when there is risk of life or proximity to death conditions, which raises concerns about end-of-life complications occurring unassisted or without adequate technical and professional support. This scenario allied with limited availability of nursing homes, hospices or other healthcare facilities, maintain the predominance of deaths in hospital, for those who can access it. Sociodemographic limitations reduce the guarantee of equity in access to health services with comprehensive care, palliative care and end-of-life support for people with social vulnerability associated with serious or advanced-stage illnesses. Limiting access or adequate end-of-life care can result in a poor quality of the dying process, such as, for example, deficiencies in the optimized control of symptoms, lack of professional support for the associated suffering, vulnerability to obstinate interventions, occurrence of dysthanasia, and other complications. Although hospital setting remains the paradigm for place of death allocation, it is not always prepared to offer the appropriate end-of-life care, considering the lack of palliative and end-of-life care public policies in Brazil, which allied with sociocultural barriers to dialogue about death with the general population, make it difficult to offer patient-centered support, and can limit the adoption of practices associated with the quality of the dying process, such as promoting the person's autonomy and individuality, applying shared decision on therapeutic resources, expanding support family and caregivers, and adequate symptom control and end-of-life care[ 14 ] In a study carried out in 2021, which ranked the quality of dying and death among several countries, when considering the access and quality of palliative care provision and its relationship with society, among the 81 countries evaluated, Brazil ranked 79th, confirming an extensive limitation of this approach at the end of life, below several countries with equivalent or even lower income, partly due to the difficulty of offering palliative care widely to the population[ 14 ]. And in the 2020 World Atlas of Palliative Care and Hospice, Brazil appears in an intermediate classification of access to palliative care[ 15 ]. Such data indicate that the supply of palliative care is limited and can affect the quality of care at the end of life and how the Brazilian population experiences the death process in their context. And, considering the sociocultural diversity and demographic differences between regions of the country, the lack of public policies can increase divergence in access and promotion of quality services for different populations. In Brazil, the Unified Health System (SUS) does not have an effective palliative and end of life care policy yet, beside this approach is cited in others health public policies, as in primary health care, home care and health assistance networks. Recently, in 2024, the National Palliative Care Policy (PNCP)[ 16 ] was approved within the scope of the SUS, but it still needs to be widely applied in healthcare system to influence public health indicators associated with quality in end of life care of the Brazilian population. CONCLUSIONS Understanding the factors involved in the distribution and allocation of population mortality makes it possible to verify the differences among Brazilian regions and the influence of sociodemographic factors in death occurrences, which may indicate limitations in the access to health services and adequate support for care offered at the end of life. In Brazil, the occurrence of death at hospital is more frequently associated with socioeconomic privilege groups (white people, higher educational level, more developed regions). To present this disparities, among other aspects, expands the debate on social demands, public policies and bioethical challenges present in the care offered in the death process. The impacts of sociodemographic factors on the allocation of the dying process indicate the need to increase equity of access to quality care at the end of life for the most vulnerable populations, such as the indigenous and black population, those with less education and from regions with limitations in the access to health services. Specific studies are needed to identify in greater depth the influence of sociocultural aspects that affect end-of-life care in the diversity of population groups and regions in middle-income countries, like Brazil. Declarations Author Contribution FCIM: Conception and design of the research, literature review, data collection, analysis and interpretation of data, writing and review of the manuscript. CCP: Conception of the research, literature review, data interpretation,writing and review of the manuscript. LFR: Conception of the research, literature review, data interpretation, writing and review of the manuscript. TP: Conception of the research, literature review, data interpretation, writing and review of the manuscript. Data Availability The anonymised data collected are available as public open data via the DATASUS (Ministry of Health/Brazil) online data repository (Available in: https://datasus.saude.gov.br/mortalidade-desde-1996-pela-cid-10). Conflict of interest statement On behalf of all authors, the corresponding author states that there is no conflict of interest. References Seitz K, Cohen J, Deliens L, Cartin A, de la Castañeda C, Cardozo EA, et al. Place of death and associated factors in 12 Latin American countries: A total population study using death certificate data. J Glob Health. 2022;12:04031. Costa V. The Determinants of Place of Death: An Evidence-Based Analysis. Ont Health Technol Assess Ser. 2014;14(16):1–78. Jiang J, May P. Proportion of deaths in hospital in European countries: trends and associations from panel data (2005–2017). Eur J Public Health. 2021;31(6):1176–83. 10.1093/eurpub/ckab169 . Lopes S, Bruno de Sousa A, Delalibera M, Namukwaya E, Cohen J, Gomes B. The rise of home death in the COVID-19 pandemic: a population-based study of death certificate data for adults from 32 countries, 2012–2021. J eClin Med. 2024;68:102399. https://doi.org/10.1016/j.eclinm.2023.102399 . Marcucci FCICM, Rosenberg JP, Yates P. Trends in place of death in Brazil and analysis of associated factors in elderly populations from 2002 to 2013. Geriatr Gerontol Aging. 2017;11(1):10–7. Lehmann KRBD, Fernandes B, Oliveira DK, Dal’Negro SH, Campos AC. Where do older adults die in Brazil? An analysis of two decades. Geriatr Gerontol Aging. 2022;16:e0220019. 10.53886/gga.e0220019 . Szwarcwald CL, Boccolini CS, da Silva de Almeida W, Soares Filho AM, Malta DC. COVID-19 mortality in Brazil, 2020-21: consequences of the pandemic inadequate management. Arch Pub Health. 2022;80(1):255. Brazil. Ministry of Health. Sistema de Informações sobre Mortalidade (SIM) [Website]. Brasília: Ministry of Health. 2024. Available in: https://datasus.saude.gov.br/mortalidade-desde-1996-pela-cid-10 . Accessed 01 May 2024. Durán D, Anyosa RC, Nicolau B, Kaufman JS. Uncovering the impact of COVID-19 on the place of death of cancer patients in South America. Cad Saude Publica. 2023;39(11):e00057423. PNUD. Programa das Nações Unidas para o Desenvolvimento. Brazilian Atlas of Human Development. PNUD Brasil, IPEA e FJP. 2022. Available in: http://www.atlasbrasil.org.br/ . Accessed 10 Jul 2024. Linares MF, Paparotto Lopes SM, Brasil Moreira AE, Vargas PA, Santos Silva ARd, Ajudarte Lopes M. Causes of death in Brazil: analysis by geographic regions and in the highest populated cities of São Paulo. Braz J Oral Sci. 2020;19:e200266. Santos RV, Welch JR, Pontes AL, Garnelo L, Moreira Cardoso A, Coimbra CEA Jr. Health of Indigenous Peoples in Brazil: Inequities and the Uneven Trajectory of Public Policies. Oxford University Press: Oxford Research Encyclopedia of Global Public Health;; 2022. https://doi.org/10.1093/acrefore/9780190632366.013.33 . Oliveira RGd C, APd G, Carpio AGS, Oliveira CG, RBd, Corrêa RM. Desigualdades raciais e a morte como horizonte: considerações sobre a COVID-19 e o racismo estrutural. Cad Saude Publica. 2020;36(9):e00150120. https://doi.org/10.1590/0102-311X00150120 . Finkelstein EA, Bhadelia A, Goh C, Baid D, Singh R, Bhatnagar S, et al. Cross Country Comparison of Expert Assessments of the Quality of Death and Dying 2021. J Pain Symptom Manag. 2022;63(4):e419–29. WHPCA. Worldwide Hospice Palliative Care Alliance. Global Atlas of Palliative Care. 2 nd. ed. London: Worldwide Hospice Palliative Care Alliance; 2020. Brazil, May. Ministry of Health. Portaria GM/MS Nº 3.681, de 7 de maio de 2024. Institui a Política Nacional de Cuidados Paliativos - PNCP no âmbito do Sistema Único de Saúde - SUS, por meio da alteração da Portaria de Consolidação GM/MS nº 2, de 28 de setembro de 2017. Brasilia: D.O.U., 22 2024; 98(1): 215. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5205278","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367604422,"identity":"e4e59480-c276-416b-9556-516cdd4b7fa3","order_by":0,"name":"Fernando Marcucci","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAi0lEQVRIiWNgGAWjYBAC9gbGBoYPJGnhOcDYwDiDRC0MDMw8pGlhP9y62TanLo+B/fDRDcRp4Ulsu5277XAxA09a2g2itNhLMIK0HEhskOAxI04LD0iL5bY6UrUwbmMmRQvQLzd7tx1ObCPaLzzsx5/d+Al0WD/74WPEaYEDNtKUj4JRMApGwSjACwD2eS4fv/s4lAAAAABJRU5ErkJggg==","orcid":"","institution":"Hospital Dr. Anísio Figueiredo – Zona Norte de Londrina","correspondingAuthor":true,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Marcucci","suffix":""},{"id":367604423,"identity":"f377beee-76a5-4c6f-9f6e-e67100c7d862","order_by":1,"name":"Carla Corradi-Perini","email":"","orcid":"","institution":"Pontifícia Universidade Católica do Paraná","correspondingAuthor":false,"prefix":"","firstName":"Carla","middleName":"","lastName":"Corradi-Perini","suffix":""},{"id":367604424,"identity":"15db5150-6c68-4a5c-b00e-e9f70236fe89","order_by":2,"name":"Luis Fernando Rodrigues","email":"","orcid":"","institution":"Hospital do Amor/ Barreto´s Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Fernando","lastName":"Rodrigues","suffix":""},{"id":367604425,"identity":"491ddd01-17c0-4927-b51b-95b08ae9e5e9","order_by":3,"name":"Tania Pastrana","email":"","orcid":"","institution":"RWTH Aachen University","correspondingAuthor":false,"prefix":"","firstName":"Tania","middleName":"","lastName":"Pastrana","suffix":""}],"badges":[],"createdAt":"2024-10-04 16:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5205278/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5205278/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12982-025-00683-7","type":"published","date":"2025-05-20T15:58:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67097880,"identity":"7e389c3f-728c-482f-a931-144a76b0f01b","added_by":"auto","created_at":"2024-10-21 07:51:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46965,"visible":true,"origin":"","legend":"\u003cp\u003eBrazilian regions map, classified by HDI, with total population and percentage (2022).\u003c/p\u003e\n\u003cp\u003eSource: IBGE, 2022. PNUD, 2022.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5205278/v1/a78d119bca724d0691c75a62.png"},{"id":67097877,"identity":"158ec870-efa9-43ae-a5ea-3646c9bb3ff1","added_by":"auto","created_at":"2024-10-21 07:51:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119812,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1. Total number and percentage of place of death distribution in Brazil (2002 to 2022).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5205278/v1/73421b1d2270c52868cdf748.png"},{"id":67097878,"identity":"2d0f9d12-8e14-45ca-9e3c-7d86a40fe600","added_by":"auto","created_at":"2024-10-21 07:51:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84604,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2. Place of death distribution classified by age groups, sex and ethnic/skin color groups in Brazil (2022).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5205278/v1/c77b8f1a36cdaf3fa48455d8.png"},{"id":83460260,"identity":"194057c3-a059-4574-a63e-de5057a9ae13","added_by":"auto","created_at":"2025-05-26 16:12:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1141374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5205278/v1/8b46eccb-b370-4105-9e5f-7906076b7d73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sociodemographic factors associated with the place of death of the Brazilian population and regional variations","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe way in which society experiences the dying process, and the multiple aspects associated with this moment, is influenced by social, cultural, economic and political aspects. From the perspectives of healthcare system and public policies, understanding the factors associated with the death and dying process, such as the place of death, the determined causes, the sociodemographic influences and the differences between regions of the country can support the definition of planned arrangement and assistance that meet the multiple needs associated with the end of life[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough death in the hospital environment is a paradigm in the current health system, in some countries, particularly those with higher income and wider access to palliative care provision for the population, there is an increasing trend of deaths at home or in non-hospital institutions[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. On the other hand, studies carried out in low or middle-income countries show great variation in the distribution of places of death, limitation of long-term care institutions and death at home may indicate lack of access to structured health services[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have identified that the distribution of place of death has great variability between countries, but Brazil stands out for the large proportion of deaths in hospitals compared to other nations, and, less frequently, at homes, with this distribution varying among its states[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, in recent years, several social, epidemiological and public policy changes may affect the way people die and how healthcare support is offered at the end of life, such as population aging, the COVID-19 pandemic period, economic crises, creation or changes in health programs and public policies, among other factors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, this study aims to identify and compare the sociodemographic factors associated with the distribution of the place of death of the Brazilian population, and to verify the differences among its regions.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThe retrospective ecologic study was carried out at population level by searching the public database of the Mortality Information System (SIM)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] of the Brazilian Ministry of Health, to collect data relating to all deaths registered in the country and classified in its five regions (north, northeast, central-west, southeast and south), between 2002 and 2022.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Brazilian regions map, classified by HDI, with total population and percentage (2022).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: IBGE, 2022. PNUD, 2022.\u003c/p\u003e \u003cp\u003eThe proportions of deaths were analyzed according to the place of occurrence (hospital, home, other healthcare facilities and other places [public places, other places and unknown]), age group (less than 1 year old, between 1 and 19 years old, 20 to 59 years old, 60 to 79 years old and over 80 years old), sex (male and female), ethnic/skin color reported classification (white, black, brown/mixed color, yellow/asian and indigenous people), educational level by years of studies (none, between 1 to 7 years and 8 or more years of study) and marital status (married, single, widowed/divorced), according to the standardized classifications available in the database.\u003c/p\u003e \u003cp\u003eA descriptive time-series analysis of the places of death distribution and sociodemographic characteristics data was applied for the last 20 years period, presenting frequencies and percentages. A population deaths rates analysis for COVID-19 pandemic period was done. To measure the relative association of sociodemographic factors with the place of death, for national and each region, a non-adjusted odd-ratio (OR) was applied and presented with 95% confidence interval (CI). The Z test was used and p-value less than 0.05 was considered for statistical significance, in comparison between the reference category for each sociodemographic factor, performed by the MedCalc software (MedCalc Software Ltd.).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOver the period there was an average increase in the number of deaths of 1.9% per year between 2002 (982,807 deaths) and 2019 (1,349,801), due to the COVID-19 pandemic there was an increase of 15.3% in 2020, and a further 17.7% in 2021, reaching a peak of 1,832,649 deaths, followed by a reduction of -15.7% in 2022.\u003c/p\u003e\n\u003ch3\u003ePlace of death distribution and region comparison\u003c/h3\u003e\n\u003cp\u003eThe proportional average of the distribution of deaths in hospitals was 66.8%, from 2002 to 2019 (minimum of 65.2, in 2003) and reached 69.1% during the pandemic period, in 2021. In 2022, there was a difference of 9,7% between southt (68,4%) and northeast (58,7%). Deaths at home progressively reduced from 23. 5%, in 2002, to 18.4% in 2021, and there was a slight increase to 21,1%, in 2022.\u003c/p\u003e \u003cp\u003eDespite having a small proportional distribution, there was a significant increase in deaths in other healthcare facilities, from 1.5% in 2002 to 6.9% in 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a result, in the northeast there was a higher proportion of deaths at home, with 27.2%, and 17.8% in the southeast (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe occurrence of deaths in other healthcare facilities, which covers emergency care settings, primary health units, long-term care institutions, among other health services, was 6.9% in Brazil, and varied from 4.2% in south region to 8.9% in the southeast region. Deaths in other places, such as public roads, different or ignored locations, were higher in the north region (10.7%), and 4.9% in the southeast (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSociodemographic factors association\u003c/h3\u003e\n\u003cp\u003eMortality under 20 years of age is particularly high in the north region of Brazil, representing around 8.5% of total deaths, while in other regions it is less than 5%. For occurrences up to 1 year old, there is a greater prevalence of deaths in hospital (91.9% in Brazil). Deaths over 60 years old represent 71.1% of occurrences in Brazil, and are highest in the south (74.8%) and southeast regions (74.1%), and lowest in the north region (59.2%). The group between 60 and 79 years old has a higher proportion of deaths in hospital (69.5%), compared to the group over 80 years old (64.7%), which presents a deviation towards home deaths (19.9% ​and 26.3%, respectively), as found in previous studies[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This shift was more significant in the northeast (25.7\u0026ndash;37.9%) and north (from 22\u0026ndash;34.6%) regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The chance of dying in a hospital compared to at home in the population under 60 years of age was 1.2 times greater compared to those aged 60 years or over, and was even greater in the north and northeast regions (1.6 times greater). Only in the central-west region this difference was not significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn relation to the classification by sex, evaluated for those over 20 years of age, it is observed that men have a lower proportion of deaths in hospitals, with a deviation towards occurrences in other places, probably due external causes of deaths in public places or traffic roads, in which 87, 4% are men. In Brazil, men have a lower chance of dying in hospitals than women (OR\u0026thinsp;=\u0026thinsp;0.87, 95% CI\u0026thinsp;=\u0026thinsp;0.86\u0026ndash;0.87), with this difference being smaller in the central-west region (OR\u0026thinsp;=\u0026thinsp;0. 78, 95% CI\u0026thinsp;=\u0026thinsp;0.75\u0026ndash;0.80).\u003c/p\u003e \u003cp\u003eRegarding differences related to skin color, the population classified as white has a 1.2 times higher chance of dying in hospitals than at home, when compared to other non-whites groups, and this difference was not significant only in the southeast region. The proportion of deaths of white and yellow individuals are similar, and higher in hospitals than brown and black individuals. For the population classified as indigenous people, the proportion of deaths in hospitals was even lower. These deaths outside the hospital are displaced to occurrences in other healthcare facilities for black people, on public roads for brown/mixed color and indigenous people, and at home and other places for indigenous people (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen analyzing educational level, it appears that the population identified with no education had a lower proportion of deaths in hospital (58.4%), compared to those with 1 to 7 years of study (65.9%) and those with more than 8 years of study (68.2%). These differences were greater in the north and northeast regions, with an increase in the proportion of incidents at home. When comparing occurrences odds in hospital and home deaths with the time of study in the population over 20 years old, it appears that the population with less time in school had a 30% lower chance of dying in hospital, compared to the population with 8 or more years of education. This difference was greater in the north and northeast regions, where the difference was 41% and 45% respectively. The difference in the chance of dying in hospital was smaller in the southeast, with a 12% lower chance in the population with less education.\u003c/p\u003e \u003cp\u003eIn general, in Brazil those identified as single had a proportion of death in hospital of 57.9%, and lower compared to the group of widowed/divorced (66.6%) and married (70.2%), with those difference particularly allocated to occurrences on public roads. The population over 20 years old registered as married had a 1.34 times greater chance of dying in hospital, than compared to at home death.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of deaths in Brazilian population (2022), classified by place of death and sociodemographic factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRAZIL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCentral-West\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSoutheast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePop. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214.828.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100,0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.133.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.951.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27,9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.905.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7,9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e90.231.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42,0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e30.606.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e14,2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.544.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e100,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e6,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e403.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e26,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e103.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e6,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e695.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e45,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e245.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e15,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLACE OF DEATH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.008.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e65,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e61,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e236.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e58,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e67,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e475.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e68,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e166.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e67,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e21,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e22,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e27,2%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e18,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e123.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e17,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e51.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e20,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther healthcare facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e5,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e5,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e6,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e61.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e8,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e10.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e4,2%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther places\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e10,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e8,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e7,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e4,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e17.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e7,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGE GROUP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 1 y/o\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e4,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e2,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e2,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e1,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e3.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e1,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e01\u0026ndash;19 y/o\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e4,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e2,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e2,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e1,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e3.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e1,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;59 y/o\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e383.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e24,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e32,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e27,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e28,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e158.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e22,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e54.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e22,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;79 y/o\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e602.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e39,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e34,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e35,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e38,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e282.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e40,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e102.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e41,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 y/o and over\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e495.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e32,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e24,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e32,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e27,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e232.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e33,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e80.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e32,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSEX*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e806.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e54,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e60,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e211.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e55,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e57,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e356.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e53,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e128.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e54,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e674.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e45,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e40,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e171.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e44,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e42,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e316.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e47,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e109.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e46,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eETHNIC/SKIN COLOR*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e765.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e51,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e20,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e25,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e43,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e406.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e60,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e198.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e83,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown/Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e549.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e37,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e69,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e236.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e61,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e46,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e181.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e27,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e24.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e10,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e8,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e6,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e9,2%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e7,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e68.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e10,2%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e4,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow/Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e0,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndigenous people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e0,2%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEDUCATION LEVEL (years of study) *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e16,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e23,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e28,2%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e17,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e73.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e10,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e24.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e10,2%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 to 7 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e648.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e43,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e40,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e145.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e38,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e41,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e311.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e46,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e115.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e48,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 years or over\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e371.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e25,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e24,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e18,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e28,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e192.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e28,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e60.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e25,2%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMARITAL STATUS*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e469.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e31,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e28,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e114.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e30,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e30,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e219.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e32,5%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e81.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e34,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e491.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e33,2%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e23,7%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e27,4%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e31,8%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e248.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e36,9%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e85.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e36,1%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e25,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e33,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e31,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e26,3%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e154.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e23,0%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e46.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003e19,6%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eData classified as Unknown was excluded. *Only in adult population (20 years old or over).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable 2. Association analysis (odd-ratio/ confidence interval) of sociodemographic factors with place of death, in adults (20 years old and over) in Brazil (2022).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRAZIL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNortheast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentral-West\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoutheast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI (95%)\u003cbr\u003e\u0026nbsp;p value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI (95%)\u003cbr\u003e\u0026nbsp;p value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI (95%)\u003cbr\u003e\u0026nbsp;p value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI (95%)\u003cbr\u003e\u0026nbsp;p value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI (95%)\u003cbr\u003e\u0026nbsp;p value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI (95%)\u003cbr\u003e\u0026nbsp;p value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.19-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.58-\u0026nbsp;1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.57-1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.99-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.02-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.02-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(Ref.: 60 y/o or over / Under 60 y/o)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP = 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP = 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.86-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.83-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,89-0,92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.75-0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.84-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.84-0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(Ref.: Male/ Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic/ Skin color\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.21-1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.15-1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.01-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.10-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.99-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.07-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(Ref.: White/ Non white)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP = 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0,71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.70-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.56-0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.54-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.75-0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0,88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.86-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.81-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(Ref.: \u0026nbsp;Under 7 year of study/ 8 yers or over)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1,34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.33-1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.29-1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.24-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.40-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.34-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1,36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.32-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(Ref.: Married/ Unmarried)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" valign=\"top\" style=\"width: 714px;\"\u003e\n \u003cp\u003eRef.: Reference category. OR: Odd Ration. CI: Confidence Interval. *Non-significant difference (p\u0026gt;0,05).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"DISCUTION","content":"\u003cp\u003eIn this study, there was a progressive increase in the number of deaths of around 1.9% per year, with the exception of the years 2020 and 2021, which were greatly influenced by the COVID-19 pandemic, with a partial return to the trend in the values ​​analyzed in 2022[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The distribution of deaths maintained the predominance of occurrences in hospitals, a slight reduction in those recorded at home, and with a significant increase in deaths in other healthcare facilities, although to overall distribution it have a small population proportion, as found in previous studies before COVID-19 pandemic period[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt can be seen that during the pandemic peak of death, there was an increase of 35,8% of yearly deaths in comparison from 2019 and 2021 [excess death of 480,848 in 2021). Considering population proportion of deaths, while in 2019 was 0,64% (with mean 0,59% from 2002 and 2019), it reached 0,86% in 2021, and return to 0,72 in 2022. The impact on place of death distribution occurred mainly in hospitals settings, with increase of 2,1%, and 0,5% for other health care facilities[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While occurrences at home decreased 1,2%, as seem for other places (less 0,8%, comparison between 2019 and 2021).\u003c/p\u003e \u003cp\u003eAmong Brazilian regions, the north and northeast have lower proportions of deaths in hospital. While in the north these may be explained to the increased frequency on public roads and other public places, in the northeast these deaths were allocated at homes and public roads. The southeast is the only region in which deaths in other healthcare facilities have a higher proportion than the national average, however, more data is needed to verify in which kind of establishments these occurrences are distributed. For example, deaths in emergency care settings may indicate an unexpected or unplanned event, while occurrences in long-term care institutions (ex. nursing homes and hospices) are often associated with aging and chronic diseases, and may have an expected and planned end of life. However, this information is not classified in the analyzed database, and more detailed study is necessary.\u003c/p\u003e \u003cp\u003eThus, there is a disparity in the distribution of places of death among the regions of Brazil, with the southeast region presenting higher rates of institutionalization of deaths in health services (hospital and other health facilities), while the northeast and north regions have lower frequency of these occurrences, with a higher proportion of deaths at home. This difference may be associated with Human Development Index (HDI) differences among Brazilian regions, since states form the north and northeast have lower HDI ranking (mean of 0,70), while Southeast and South have the highest HDI (mean of 0,79)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePopulation aging, associated with an increased frequency of chronic diseases as main cause of mortality, implies an overload on healthcare services. In Brazil, over 70% of all deaths occurs in the population over 60 years old, as found by previous studies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. There is a shift in the occurrence of death from the hospital to the home in the older age groups, from 20\u0026ndash;26.3%, for the population aged 60 to 79 years and over 80 years, respectively. This shift is more significant in the northeast (25.7\u0026ndash;37.9%) and north (from 22\u0026ndash;34.6%) regions. In the north and northeast regions, the chance of dying in hospital under the age of 60 was 1.6 times greater than in the older age groups.\u003c/p\u003e \u003cp\u003eAs identified by previous studies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], when comparing the distribution of deaths between the sexes in Brazil, women had a higher proportion of deaths in hospitals (69%) compared to men (62%). The biggest difference was observed in the northeast, followed by the central-west. These difference may be associated with the impact of mortality from external causes on the male population, with a higher proportion of deaths on public roads and other places, being higher in the north and northeast regions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor marital status, considering the population over 20 years old, married person have a greater chance of dying in hospital, compared at home group. Similar finds were reported by other recent study[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These finds may partially explain due marital status be influenced by age, with single (and younger) population being more affected by death from external causes. When comparing among Brazilian regions, in the northeast and north, widowed and divorced person had a higher frequency of death at home than married and single people. A deeper analysis is need to investigate how different contexts of familiar and marital status affect the place of death distribution.\u003c/p\u003e \u003cp\u003eFrom the total deaths in Brazil in 2022, 51.6% of individuals were classified as white, 45.7% as black or brown/mixed color, and a small portion as yellow and as indigenous people. Compared to other regions, the north is the only region in which indigenous people have a lower proportion of deaths in hospitals, compared to the national average. In general, there is a higher proportion of deaths among the indigenous population at home or classified as other places. Considering the limited information about the end-of-life context of the indigenous population in Brazil, more studies are needed to identify in which places and conditions these deaths occur, considering that this population have important barriers to access healthcare services[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe difference in death frequencies in hospital between white and non-white people indicates an inequity access to less social privilege groups, and, in the context of end-of-life care, it may imply in lower and later support for suffering conditions[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEducation level seems to be a social factor related to access to health care at the end of life in Brazil, where the population with less schooling had a 29% lower chance of dying in hospital, compared to the population with 8 or more years of education. The difference was greater in the north and northeast regions, where the difference was 41% and 45% respectively. The difference in the chance of dying in hospital was smaller in the southeast, with a 12% lower chance in the population with less education. This fact was also identified by previous research[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor vulnerable population in Brazil, the occurrence of death at home indicates lack of access to structured health services, even when there is risk of life or proximity to death conditions, which raises concerns about end-of-life complications occurring unassisted or without adequate technical and professional support. This scenario allied with limited availability of nursing homes, hospices or other healthcare facilities, maintain the predominance of deaths in hospital, for those who can access it.\u003c/p\u003e \u003cp\u003eSociodemographic limitations reduce the guarantee of equity in access to health services with comprehensive care, palliative care and end-of-life support for people with social vulnerability associated with serious or advanced-stage illnesses. Limiting access or adequate end-of-life care can result in a poor quality of the dying process, such as, for example, deficiencies in the optimized control of symptoms, lack of professional support for the associated suffering, vulnerability to obstinate interventions, occurrence of dysthanasia, and other complications.\u003c/p\u003e \u003cp\u003eAlthough hospital setting remains the paradigm for place of death allocation, it is not always prepared to offer the appropriate end-of-life care, considering the lack of palliative and end-of-life care public policies in Brazil, which allied with sociocultural barriers to dialogue about death with the general population, make it difficult to offer patient-centered support, and can limit the adoption of practices associated with the quality of the dying process, such as promoting the person's autonomy and individuality, applying shared decision on therapeutic resources, expanding support family and caregivers, and adequate symptom control and end-of-life care[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn a study carried out in 2021, which ranked the quality of dying and death among several countries, when considering the access and quality of palliative care provision and its relationship with society, among the 81 countries evaluated, Brazil ranked 79th, confirming an extensive limitation of this approach at the end of life, below several countries with equivalent or even lower income, partly due to the difficulty of offering palliative care widely to the population[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. And in the 2020 World Atlas of Palliative Care and Hospice, Brazil appears in an intermediate classification of access to palliative care[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Such data indicate that the supply of palliative care is limited and can affect the quality of care at the end of life and how the Brazilian population experiences the death process in their context. And, considering the sociocultural diversity and demographic differences between regions of the country, the lack of public policies can increase divergence in access and promotion of quality services for different populations.\u003c/p\u003e \u003cp\u003eIn Brazil, the Unified Health System (SUS) does not have an effective palliative and end of life care policy yet, beside this approach is cited in others health public policies, as in primary health care, home care and health assistance networks. Recently, in 2024, the National Palliative Care Policy (PNCP)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] was approved within the scope of the SUS, but it still needs to be widely applied in healthcare system to influence public health indicators associated with quality in end of life care of the Brazilian population.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eUnderstanding the factors involved in the distribution and allocation of population mortality makes it possible to verify the differences among Brazilian regions and the influence of sociodemographic factors in death occurrences, which may indicate limitations in the access to health services and adequate support for care offered at the end of life. In Brazil, the occurrence of death at hospital is more frequently associated with socioeconomic privilege groups (white people, higher educational level, more developed regions). To present this disparities, among other aspects, expands the debate on social demands, public policies and bioethical challenges present in the care offered in the death process.\u003c/p\u003e \u003cp\u003eThe impacts of sociodemographic factors on the allocation of the dying process indicate the need to increase equity of access to quality care at the end of life for the most vulnerable populations, such as the indigenous and black population, those with less education and from regions with limitations in the access to health services. Specific studies are needed to identify in greater depth the influence of sociocultural aspects that affect end-of-life care in the diversity of population groups and regions in middle-income countries, like Brazil.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFCIM: Conception and design of the research, literature review, data collection, analysis and interpretation of data, writing and review of the manuscript. CCP: Conception of the research, literature review, data interpretation,writing and review of the manuscript. LFR: Conception of the research, literature review, data interpretation, writing and review of the manuscript. TP: Conception of the research, literature review, data interpretation, writing and review of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe anonymised data collected are available as public open data via the DATASUS (Ministry of Health/Brazil) online data repository (Available in: https://datasus.saude.gov.br/mortalidade-desde-1996-pela-cid-10).\u003c/p\u003e\u003ch2\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSeitz K, Cohen J, Deliens L, Cartin A, de la Casta\u0026ntilde;eda C, Cardozo EA, et al. Place of death and associated factors in 12 Latin American countries: A total population study using death certificate data. 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Brasilia: D.O.U., 22 2024; 98(1): 215.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mortality, Sociodemographic Factors, Social Determinants in Health, Vulnerable Populations, Cause of Deaths","lastPublishedDoi":"10.21203/rs.3.rs-5205278/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5205278/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the factors associated with the death and dying process, such as the place of occurrence and the sociodemographic influences, can support the definition of health assistance and public policies. Limited information is available for middle-income countries, like Brazil. This study identified and compared the association of sociodemographic factors (age, sex, ethnic/skin color identification, educational level and marital status) with the place of death in Brazil and its regions, in a population level research. In Brazil, death at hospital was more frequently associated with socioeconomic privilege groups (white people, higher educational level, more developed regions). Older age groups, male, unmarried groups and lower education level were related with higher odd to death at home, which raises concerns about unassisted or limited support in these occurrences. The debate on social demands, public policies and bioethical challenges associated with the assistance offered on death occurrences is need.\u003c/p\u003e","manuscriptTitle":"Sociodemographic factors associated with the place of death of the Brazilian population and regional variations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-21 07:51:28","doi":"10.21203/rs.3.rs-5205278/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-23T17:18:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-16T22:40:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338198273087430480117744498815263182851","date":"2024-12-01T19:09:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33853131434229622562763170965816437596","date":"2024-11-29T16:26:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-22T15:12:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259399049553802585246349551814291398677","date":"2024-11-12T11:36:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189099158436134667884294957437443628641","date":"2024-11-12T01:45:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-25T17:46:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-21T11:12:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-18T09:34:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2024-10-04T16:19:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0500696f-f5f6-41ed-8c4e-fe213522e3ba","owner":[],"postedDate":"October 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:08:05+00:00","versionOfRecord":{"articleIdentity":"rs-5205278","link":"https://doi.org/10.1186/s12982-025-00683-7","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2025-05-20 15:58:28","publishedOnDateReadable":"May 20th, 2025"},"versionCreatedAt":"2024-10-21 07:51:28","video":"","vorDoi":"10.1186/s12982-025-00683-7","vorDoiUrl":"https://doi.org/10.1186/s12982-025-00683-7","workflowStages":[]},"version":"v1","identity":"rs-5205278","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5205278","identity":"rs-5205278","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}