Quantifying the influence of location of residence on blood pressure in urbanising South India: a path analysis with multiple mediators

Author:

Sørensen Tina B.1ORCID,Vansteelandt Stijn12,Wilson Robin3,Gregson John1,Shankar Bhavani45,Kinra Sanjay1,Dangour Alan D.15

Affiliation:

1. Faculty of Epidemiology and Population Health , London School of Hygiene and Tropical Medicine , London , UK

2. and Department of Applied Mathematics, Computer Science and Statistics , University of Ghent , Gent , Belgium

3. Geography & Environment , University of Southampton , Southampton , UK

4. Department of Geography , The University of Sheffield , Sheffield , UK

5. and London Centre for Integrative Research in Agriculture and Health (LCIRAH) , London , UK

Abstract

Abstract Objectives: The current study aims to estimate the causal effect of increasing levels of urbanisation on mean SBP, and to decompose the direct and indirect effects via hypothesised mediators. Methods: We analysed data from 5, 840 adults (≥ 18 years) from the Andhra Pradesh Children and Parents study (APCAPS) conducted in 27 villages in Telangana, South India. The villages experienced different amounts of urbanisation during preceding decades and ranged from a rural village to a medium sized town. We estimated urbanisation levels of surveyed villages by combining remote sensing data of night-time light intensity (NTLI), measured by unitless digital numbers, with satellite imagery and ground surveying of village boundaries. We performed mediation analysis using linear mixed-effects models with SBP as the outcome, log-transformed continuous NTLI as the exposure, and three composite mediators summarising information on (i) socio-demographics (e.g., occupation and education); (ii) lifestyle and mental health (e.g., diet and depression); (iii) metabolic factors (e.g., fasting glucose and triglycerides). All models fitted random intercepts to account for clustering by villages and households and adjusted for confounders. Results: The NTLI range across the 27 villages was 62 to 1081 (4.1 to 7.0 on the log scale). Mean SBP was 122.7 mmHg (±15.7) among men and 115.8 mmHg (±14.2) among women. One unit (integer) log-NTLI increase was associated with a rise in mean SBP of 2.1 mmHg (95% CI 0.6, 3.5) among men and 1.3 mmHg (95% CI 0.0, 2.6) among women. We identified a positive indirect effect of log-NTLI on SBP via the metabolic pathway, where one log-NTLI increase elevated SBP by 4.6 mmHg (95% CI 2.0, 7.3) among men and by 0.7 mmHg (95% 0.1, 1.3) among women. There was a positive indirect effect of log-NTLI on SBP via the lifestyle and mental health pathway among men, where one log-NTLI increase elevated SBP by 0.7 mmHg (95% CI 0.1, 1.3). Observed negative direct effects of log-NTLI on SBP and positive indirect effects via the socio-demographic pathway among both genders; as well as a positive indirect effect via the lifestyle and mental health pathway among women, were not statistically significant at the 5% level. The sizes of effects were approximately doubled among participants ≥40 years of age. Conclusion: Our findings offer new insights into the pathways via which urbanisation level may act on blood pressure. Large indirect effects via metabolic factors, independent of socio-demographic, lifestyle and mental health factors identify a need to understand better the indirect effects of environmental cardiovascular disease (CVD) risk factors that change with urbanisation. We encourage researchers to use causal methods in further quantification of path-specific effects of place of residence on CVDs and risk factors. Available evidence-based, cost-effective interventions that target upstream determinants of CVDs should be implemented across all socio-demographic gradients in India.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Epidemiology

Reference109 articles.

1. Ainsworth, B. E., W. L. Haskell, S. D. Herrmann, N. Meckes, D. R. BassettJr., C. Tudor-Locke, J. L. Greer, J. Vezina, M. C. Whitt-Glover, and A. S. Leon. 2011a. “Compendium of Physical Activities: A Second Update of Codes and MET Values.” Medicine & Science in Sports & Exercise 43 (8): 1575–81.https://doi.org/10.1249/mss.0b013e31821ece12.

2. Ainsworth, B. E., W. L. Haskell, S. D. Herrmann, N. Meckes, D. R. BassettJr., C. Tudor-Locke, J. L. Greer, J. Vezina, M. C. Whitt-Glover, A. S. Leon. 2011b. Compendium of Physical Activity. Also available at https://sites.google.com/site/compendiumofphysicalactivities/ (accessed August 22, 2014).

3. Allender, S., C. Foster, L. Hutchinson, and C. Arambepola. 2008. “Quantification of Urbanization in Relation to Chronic Diseases in Developing Countries: A Systematic Review.” Journal of Urban Health : Bulletin of the New York Academy of Medicine 85 (6): 938–51. https://doi.org/10.1007/s11524-008-9325-4.

4. Allender, S., B. Lacey, P. Webster, M. Rayner, M. Deepa. P. Scarborough, C. Arambepola, M. Datta, and V. Mohan. 2010. “Level of Urbanization and Noncommunicable Disease Risk Factors in Tamil Nadu, India.” Bulletin of the World Health Organization 88 (4): 297–304. https://doi.org/10.2471/blt.09.065847.

5. Allender, S., K. Wickramasinghe, M. Goldacre, D. Matthews, and P. Katulanda. 2011. “Quantifying Urbanization as a Risk Factor for Noncommunicable Disease.” Journal of Urban Health: Bulletin of the New York Academy of Medicine 88 (5): 906–18. https://doi.org/10.1007/s11524-011-9586-1.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3