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Bartoll-Roca X, Rodríguez-Sanz M, Sánchez-Ledesma E, Pérez K, Borrell C. Inequalities in life expectancy by educational level and its decomposition in Barcelona, 2004-2018. GACETA SANITARIA 2022; 36:520-525. [PMID: 35337685 DOI: 10.1016/j.gaceta.2021.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To analyse the gap in life expectancy by educational level in the city of Barcelona from 2004 to 2018 and to decompose this gap by age and causes of death. METHOD We computed abridged life tables at the age of 25 years by sex from 2004 to 2018 using standard methods. Educational level was categorised in two groups (lower secondary or less vs. upper secondary or higher education). The life expectancy gap was further decomposed by age and by causes of death based in Arriaga's method in 5-year age blocks up to the age of ≥ 90 years and broad causes of death using ICD-10 codes. RESULTS The life expectancy gap at 25 years by educational level oscillated without trend at around 3.08 years for men and 1.93 years for women. Decomposition by age showed a favourable significant shift in the contribution to this gap from young to older ages for men, with few changes for women. Decomposition by causes of death showed that the diseases concentrating the largest share of the contribution were neoplasms and respiratory and circulatory disease. There was a significant downward trend in external causes for men and in infectious diseases for both men and women but a significant upward trend for respiratory disease for both sexes. CONCLUSIONS The stability of the life expectancy gap by educational level during the period analysed resulted from a combination of divergent trends by age and causes of death among high and low educational levels.
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Affiliation(s)
- Xavier Bartoll-Roca
- Agència de Salut Pública de Barcelona, Barcelona (ASPB), Spain; Institut d'Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain.
| | - Maica Rodríguez-Sanz
- Agència de Salut Pública de Barcelona, Barcelona (ASPB), Spain; Institut d'Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Departament de Cièncias Experimentales i de la Salut, Facultat de Ciències de la Salut i de la Vida, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Katherine Pérez
- Agència de Salut Pública de Barcelona, Barcelona (ASPB), Spain; Institut d'Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Carme Borrell
- Agència de Salut Pública de Barcelona, Barcelona (ASPB), Spain; Institut d'Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Departament de Cièncias Experimentales i de la Salut, Facultat de Ciències de la Salut i de la Vida, Universitat Pompeu Fabra, Barcelona, Spain
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Baca-López K, Fresno C, Espinal-Enríquez J, Flores-Merino MV, Camacho-López MA, Hernández-Lemus E. Metropolitan age-specific mortality trends at borough and neighborhood level: The case of Mexico City. PLoS One 2021; 16:e0244384. [PMID: 33465102 PMCID: PMC7815139 DOI: 10.1371/journal.pone.0244384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 12/08/2020] [Indexed: 11/23/2022] Open
Abstract
Understanding the spatial and temporal patterns of mortality rates in a highly heterogeneous metropolis, is a matter of public policy interest. In this context, there is no, to the best of our knowledge, previous studies that correlate both spatio-temporal and age-specific mortality rates in Mexico City. Spatio-temporal Kriging modeling was used over five age-specific mortality rates (from the years 2000 to 2016 in Mexico City), to gain both spatial (borough and neighborhood) and temporal (year and trimester) data level description. Mortality age-specific patterns have been modeled using multilevel modeling for longitudinal data. Posterior tests were carried out to compare mortality averages between geo-spatial locations. Mortality correlation extends in all study groups for as long as 12 years and as far as 13.27 km. The highest mortality rate takes place in the Cuauhtémoc borough, the commercial, touristic and cultural core downtown of Mexico City. On the contrary, Tlalpan borough is the one with the lowest mortality rates in all the study groups. Post-productive mortality is the first age-specific cause of death, followed by infant, productive, pre-school and scholar groups. The combinations of spatio-temporal Kriging estimation and time-evolution linear mixed-effect models, allowed us to unveil relevant time and location trends that may be useful for public policy planning in Mexico City.
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Affiliation(s)
- Karol Baca-López
- School of Medicine, Autonomous University of the State of Mexico, Toluca, State of Mexico, Mexico
- Computational Genomics Department, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Cristóbal Fresno
- Technology Development Department, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Jesús Espinal-Enríquez
- Computational Genomics Department, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Miriam V Flores-Merino
- School of Chemistry, Autonomous University of the State of Mexico, Toluca, State of Mexico, Mexico
| | - Miguel A Camacho-López
- School of Medicine, Autonomous University of the State of Mexico, Toluca, State of Mexico, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Department, National Institute of Genomic Medicine, Mexico City, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
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Mansour A, Sirichotiratana N, Viwatwongkasem C, Khan M, Srithamrongsawat S. District division administrative disaggregation data framework for monitoring leaving no one behind in the National Health Insurance Fund of Sudan: achieving sustainable development goals in 2030. Int J Equity Health 2021; 20:5. [PMID: 33407542 PMCID: PMC7789368 DOI: 10.1186/s12939-020-01338-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 11/30/2020] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND The aim of this study is to monitor the concept of 'leaving no one behind' in the Sustainable Development Goals (SDGs) to track the implications of the mobilization of health care resources by the National Health Insurance Fund (NHIF) of Sudan. METHODS A cross-sectional study was used to monitor 'leaving no one behind' in NHIF by analyzing the secondary data of the information system for the year 2016. The study categorized the catchment areas of health care centers (HCCS) according to district administrative divisions, which are neighborhood, subdistrict, district, and zero. The District Division Administrative Disaggregation Data (DDADD) framework was developed and investigated with the use of descriptive statistics, maps of Sudan, the Mann-Whitney test, the Kruskal-Wallis test and health equity catchment indicators. SPSS ver. 18 and EndNote X8 were also used. RESULTS The findings show that the NHIF has mobilized HCCs according to coverage of the insured population. This mobilization protected the insured poor in high-coverage insured population districts and left those living in very low-coverage districts behind. The Mann-Whitney test presented a significant median difference in the utilization rate between catchment areas (P value < 0.001). The results showed that the utilization rate of the insured poor who accessed health care centers by neighborhood was higher than that of the insured poor who accessed by more than neighborhood in each state. The Kruskal-Wallis test of the cost of health care services per capita in each catchment area showed a difference (P value < 0.001) in the median between neighborhoods. The cost of health care services in low-coverage insured population districts was higher than that in high-coverage insured population districts. CONCLUSION The DDADD framework identified the inequitable distribution of health care services in low-density population districts leaves insured poor behind. Policymakers should restructure the equation of health insurance schemes based on equity and probability of illness, to distribute health care services according to needs and equity, and to remobilize resources towards districts left behind.
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Affiliation(s)
- Ashraf Mansour
- Department of Public Health Administration, Faculty of Public Health, Mahidol University, Bangkok, 10400 Thailand
| | - Nithat Sirichotiratana
- Department of Public Health Administration, Faculty of Public Health, Mahidol University, Bangkok, 10400 Thailand
| | - Chukiat Viwatwongkasem
- Department of Biostatistics, Faculty of Public Health, Mahidol University, Bangkok, Thailand
| | - Mahmud Khan
- Arnold School of Public Health, University of South Carolina, Columbia, USA
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Oliva-Arocas A, Pereyra-Zamora P, Copete JM, Vergara-Hernández C, Martínez-Beneito MA, Nolasco A. Socioeconomic Inequalities in Mortality among Foreign-Born and Spanish-Born in Small Areas in Cities of the Mediterranean Coast in Spain, 2009-2015. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E4672. [PMID: 32610538 PMCID: PMC7370214 DOI: 10.3390/ijerph17134672] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/14/2020] [Accepted: 06/19/2020] [Indexed: 12/28/2022]
Abstract
Many studies have analysed socioeconomic inequalities and its association with mortality in urban areas. However, few of them have differentiated between native and immigrant populations. This study is an ecological study of mortality by overall mortality and analyses the inequalities in mortality in these populations according to the level of deprivation in small areas of large cities in the Valencian Community, from 2009 to 2015. The census tract was classified into five deprivation levels using an index based on socioeconomic indicators from the 2011 census. Rates and relative risks of death were calculated by sex, age, level of deprivation and country of birth. Poisson regression models have been used. In general, there was a higher risk of death in natives at the levels of greatest deprivation, which did not happen in immigrants. During the 2009-2015 period, there were socioeconomic inequalities in mortality, particularly in natives, who presented a higher risk of death than immigrants. Future interventions and social policies should be implemented in order to reduce inequalities in mortality amongst socioeconomic levels and to maintain the advantage that the immigrant population enjoys.
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Affiliation(s)
- Adriana Oliva-Arocas
- Research Unit for the Analysis of Mortality and Health Statistics, Department of Community Nursing, Preventive Medicine, Public Health and History of Science, University of Alicante, 03080 Alicante, Spain; (A.O.-A.); (J.M.C.); (A.N.)
| | - Pamela Pereyra-Zamora
- Research Unit for the Analysis of Mortality and Health Statistics, Department of Community Nursing, Preventive Medicine, Public Health and History of Science, University of Alicante, 03080 Alicante, Spain; (A.O.-A.); (J.M.C.); (A.N.)
| | - José M. Copete
- Research Unit for the Analysis of Mortality and Health Statistics, Department of Community Nursing, Preventive Medicine, Public Health and History of Science, University of Alicante, 03080 Alicante, Spain; (A.O.-A.); (J.M.C.); (A.N.)
| | - Carlos Vergara-Hernández
- Área de Desigualdades en Salud, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO), 46035 Valencia, Spain;
| | | | - Andreu Nolasco
- Research Unit for the Analysis of Mortality and Health Statistics, Department of Community Nursing, Preventive Medicine, Public Health and History of Science, University of Alicante, 03080 Alicante, Spain; (A.O.-A.); (J.M.C.); (A.N.)
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Ingole V, Marí-Dell’Olmo M, Deluca A, Quijal M, Borrell C, Rodríguez-Sanz M, Achebak H, Lauwaet D, Gilabert J, Murage P, Hajat S, Basagaña X, Ballester J. Spatial Variability of Heat-Related Mortality in Barcelona from 1992-2015: A Case Crossover Study Design. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17072553. [PMID: 32276439 PMCID: PMC7177772 DOI: 10.3390/ijerph17072553] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/01/2020] [Accepted: 04/04/2020] [Indexed: 11/23/2022]
Abstract
Numerous studies have demonstrated the relationship between summer temperatures and increased heat-related deaths. Epidemiological analyses of the health effects of climate exposures usually rely on observations from the nearest weather station to assess exposure-response associations for geographically diverse populations. Urban climate models provide high-resolution spatial data that may potentially improve exposure estimates, but to date, they have not been extensively applied in epidemiological research. We investigated temperature-mortality relationships in the city of Barcelona, and whether estimates vary among districts. We considered georeferenced individual (natural) mortality data during the summer months (June–September) for the period 1992–2015. We extracted daily summer mean temperatures from a 100-m resolution simulation of the urban climate model (UrbClim). Summer hot days (above percentile 70) and reference (below percentile 30) temperatures were compared by using a conditional logistic regression model in a case crossover study design applied to all districts of Barcelona. Relative Risks (RR), and 95% Confidence Intervals (CI), of all-cause (natural) mortality and summer temperature were calculated for several population subgroups (age, sex and education level by districts). Hot days were associated with an increased risk of death (RR = 1.13; 95% CI = 1.10–1.16) and were significant in all population subgroups compared to the non-hot days. The risk ratio was higher among women (RR = 1.16; 95% CI= 1.12–1.21) and the elderly (RR = 1.18; 95% CI = 1.13–1.22). Individuals with primary education had similar risk (RR = 1.13; 95% CI = 1.08–1.18) than those without education (RR = 1.10; 95% CI= 1.05–1.15). Moreover, 6 out of 10 districts showed statistically significant associations, varying the risk ratio between 1.12 (95% CI = 1.03–1.21) in Sants-Montjuïc and 1.25 (95% CI = 1.14–1.38) in Sant Andreu. Findings identified vulnerable districts and suggested new insights to public health policy makers on how to develop district-specific strategies to reduce risks.
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Affiliation(s)
- Vijendra Ingole
- Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain; (V.I.)
- Climate and Health Program (CLIMA), Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain
| | - Marc Marí-Dell’Olmo
- Agència de Salut Pública de Barcelona (ASPB), Barcelona 08023, Spain
- Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona 08041, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
- Correspondence: ; Tel.: +34-93-2384545
| | - Anna Deluca
- Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain; (V.I.)
- Climate and Health Program (CLIMA), Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain
| | - Marcos Quijal
- Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain; (V.I.)
- Climate and Health Program (CLIMA), Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain
- Agència de Salut Pública de Barcelona (ASPB), Barcelona 08023, Spain
- Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona 08041, Spain
| | - Carme Borrell
- Agència de Salut Pública de Barcelona (ASPB), Barcelona 08023, Spain
- Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona 08041, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain
| | - Maica Rodríguez-Sanz
- Agència de Salut Pública de Barcelona (ASPB), Barcelona 08023, Spain
- Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona 08041, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain
| | - Hicham Achebak
- Climate and Health Program (CLIMA), Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain
- Centre for Demographic Studies (CED), Autonomous University of Barcelona, Barcelona 08193, Spain
| | - Dirk Lauwaet
- Environmental Modelling Department, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium
| | - Joan Gilabert
- PCOT, Cartographic and Geological Institute of Catalonia (ICGC), Barcelona 08038, Spain
| | - Peninah Murage
- Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK
| | - Shakoor Hajat
- Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK
| | - Xavier Basagaña
- Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain; (V.I.)
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain
| | - Joan Ballester
- Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain; (V.I.)
- Climate and Health Program (CLIMA), Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain
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