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
|
|