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      A geodatabase of blood pressure level and the associated factors including lifestyle, nutritional, air pollution, and urban greenspace

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          Abstract

          Objectives

          Hypertension is a prevalent chronic disease globally. A multifaceted combination of risk factors is associated with hypertension. Scientific literature has shown the association among individual and environmental factors with hypertension, however, a comprehensive database including demographic, environmental, individual attributes and nutritional status has been rarely studied. Moreover, an integrated spatial-epidemiological approach has been scarcely researched. Therefore, this study aims to provide and describe a geodatabase including individual-based and socio-environmental data related to people living in the city of Mashhad, Iran in 2018.

          Data description

          The database has been extracted from the PERSIAN Organizational Cohort study in Mashhad University of Medical Sciences. The data note includes three shapefiles and a help file. The shapefile format is a digital vector storage format for storing geometric location and associated attribute information. The first shapefile includes the data of population, air pollutants and amount of available green space for each census block of the city. The second shapefile consists of aggregated blood pressure data to the census blocks of the city. The third shapefile comprises the individual characteristics data (i.e., demographic, clinical, and lifestyle). Finally, the fourth file is a guide to the previous data files for users.

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          Most cited references34

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          Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

          Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning. Funding Bill & Melinda Gates Foundation.
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            A call to action and a lifecourse strategy to address the global burden of raised blood pressure on current and future generations: the Lancet Commission on hypertension.

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              Global association between ambient air pollution and blood pressure: A systematic review and meta-analysis

              Although numerous studies have investigated the association of ambient air pollution with hypertension and blood pressure (BP), the results were inconsistent. We performed a comprehensive systematic review and meta-analysis of these studies. Seven international and Chinese databases were searched for studies examining the associations of particulate (diameter 10 μm (PM10)) and gaseous (sulfur dioxide (SO2), nitrogen dioxide (NO2), nitrogen oxides (NOx), ozone (O3), carbon monoxide (CO)) air pollutants with hypertension or BP. Odds ratios (OR), regression coefficients (β) and their 95% confidence intervals were calculated to evaluate the strength of the associations. Subgroup analysis, sensitivity analysis, and meta-regression analysis were also conducted. The overall meta-analysis showed significant associations of long-term exposures to PM2.5 with hypertension (OR = 1.05), and of PM10, PM2.5, and NO2 with DBP (β values: 0.47-0.86 mmHg). In addition, short-term exposures to four (PM10, PM2.5, SO2, NO2), two (PM2.5 and SO2), and four air pollutants (PM10, PM2.5, SO2, and NO2), were significantly associated with hypertension (ORs: 1.05-1.10), SBP (β values: 0.53-0.75 mmHg) and DBP (β values: 0.15-0.64 mmHg), respectively. Stratified analyses showed a generally stronger relationship among studies of men, Asians, North Americans, and areas with higher air pollutant levels. In conclusion, our study indicates a positive association between ambient air pollution and increased BP and hypertension. Geographical and socio-demographic factors may modify the pro-hypertensive effects of air pollutants.
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                Author and article information

                Contributors
                A.Mohammadi@uma.ac.ir
                Epishgar2018@gmail.com
                Firouraghin981@mums.ac.ir
                Nasser.Bagheri@anu.edu.au
                ali.shamsoddini@yahoo.com
                dr.j.abbas@outlook.com
                Kiani.Behzad@gmail.com
                Journal
                BMC Res Notes
                BMC Res Notes
                BMC Research Notes
                BioMed Central (London )
                1756-0500
                18 November 2021
                18 November 2021
                2021
                : 14
                : 416
                Affiliations
                [1 ]GRID grid.413026.2, ISNI 0000 0004 1762 5445, Department of Geography and Urban Planning, Faculty of Social Sciences, , University of Mohaghegh Ardabili, ; Ardabil, Iran
                [2 ]GRID grid.412502.0, ISNI 0000 0001 0686 4748, Department of Human Geography, Faculty of Earth Science, , Shahid Beheshti University, ; Tehran, Iran
                [3 ]GRID grid.411583.a, ISNI 0000 0001 2198 6209, Department of Medical Informatics, School of Medicine, , Mashhad University of Medical Sciences, ; Mashhad, Iran
                [4 ]GRID grid.1001.0, ISNI 0000 0001 2180 7477, Visualization and Decision Analytics (VIDEA) Lab, Centre for Mental Health Research, Research School of Population Health, College of Health and Medicine, , The Australian National University, ; Canberra, Australia
                [5 ]GRID grid.1039.b, ISNI 0000 0004 0385 7472, The Australian Geospatial Health Lab, Health Research Institute, , The University of Canberra, ; Canberra, Australia
                [6 ]GRID grid.449257.9, ISNI 0000 0004 0494 2636, Department of Human Geography, Faculty of Humanities, , Islamic Azad University, Marvdasht Branch, ; Marvdasht, Iran
                [7 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Antai College of Economics and Management, and School of Media and Communication, , Shanghai Jiao Tong University, ; Shanghai, China
                Author information
                https://orcid.org/0000-0002-3327-0942
                https://orcid.org/0000-0001-7217-2598
                https://orcid.org/0000-0002-1297-0448
                https://orcid.org/0000-0003-1097-2797
                https://orcid.org/0000-0001-9757-4331
                https://orcid.org/0000-0002-8830-1435
                http://orcid.org/0000-0002-8816-328X
                Article
                5830
                10.1186/s13104-021-05830-2
                8600347
                34794504
                c1209aa8-4629-437a-91c4-5f62b0adcc0c
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 16 May 2021
                : 3 November 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004748, Mashhad University of Medical Sciences;
                Award ID: IR.MUMS.MEDICAL.REC.1398.785.‎
                Award Recipient :
                Categories
                Data Note
                Custom metadata
                © The Author(s) 2021

                Medicine
                air pollution,green space,geographical information system,hypertension,blood pressure,nutritional factors,spatial analysis

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