Advertisement

Journal of Community Health

, Volume 42, Issue 5, pp 974–982 | Cite as

Residential and GPS-Defined Activity Space Neighborhood Noise Complaints, Body Mass Index and Blood Pressure Among Low-Income Housing Residents in New York City

  • Kosuke Tamura
  • Brian Elbel
  • Basile Chaix
  • Seann D. Regan
  • Yazan A. Al-Ajlouni
  • Jessica K. Athens
  • Julie Meline
  • Dustin T. Duncan
Original Paper

Abstract

Little is known about how neighborhood noise influences cardiovascular disease (CVD) risk among low-income populations. The aim of this study was to investigate associations between neighborhood noise complaints and body mass index (BMI) and blood pressure (BP) among low-income housing residents in New York City (NYC), including the use of global positioning system (GPS) data. Data came from the NYC Low-Income Housing, Neighborhoods and Health Study in 2014, including objectively measured BMI and BP data (N = 102, Black = 69%), and 1 week of GPS data. Noise reports from “NYC 311” were used to create a noise complaints density (unit: 1000 reports/km2) around participants’ home and GPS-defined activity space neighborhoods. In fully-adjusted models, we examined associations of noise complaints density with BMI (kg/m2), and systolic and diastolic BP (mmHg), controlling for individual- and neighborhood-level socio-demographics. We found inverse relationships between home noise density and BMI (B = −2.7 [kg/m2], p = 0.009), and systolic BP (B = −5.3 mmHg, p = 0.008) in the fully-adjusted models, and diastolic BP (B = −3.9 mmHg, p = 0.013) in age-adjusted models. Using GPS-defined activity space neighborhoods, we observed inverse associations between noise density and systolic BP (B = −10.3 mmHg, p = 0.019) in fully-adjusted models and diastolic BP (B = −7.5 mmHg, p = 0.016) in age-adjusted model, but not with BMI. The inverse associations between neighborhood noise and CVD risk factors were unexpected. Further investigation is needed to determine if these results are affected by unobserved confounding (e.g., variations in walkability). Examining how noise could be related to CVD risk could inform effective neighborhood intervention programs for CVD risk reduction.

Keywords

Neighborhood noise exposure Low-income housing residents Geographic information systems Global positioning systems Health disparities 

Notes

Acknowledgements

We thank the research assistants for this project: Maliyhah Al-Bayan; Shilpa Dutta; William Goedel; Brittany Gozlan; Kenneth Pass; James Williams; and Abebayehu Yilma. We thank Jeff Blossom for geocoding the participants’ addresses. We also thank the participants for engaging in this research.

Funding

The NYC Low-Income Housing, Neighborhoods and Health Study was supported by the NYU-HHC Clinical and Translational Science Institute (CTSI) Pilot Project Awards Program (Dr. Dustin Duncan, Principal Investigator). The NYU-HHC CTSI is supported in part by grant UL1TR000038 (Dr. Bruce Cronstein, Principal Investigator and Dr. Judith Hochman, co-Principal Investigator) from the National Center for Advancing Translational Sciences of the National Institutes of Health. During the manuscript development, Dr. Tamura has been supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (Grant R01DK097347 to Dr. Brian Elbel).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Lewington, S., et al. (2002). Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet, 360(9349), 1903–1913.CrossRefPubMedGoogle Scholar
  2. 2.
    National Center for Health Statistics, Health, United States, 2015 (2016). With special feature on racial and ethnic health disparities. Hyattsville, MD: National Center for Health Statistics.Google Scholar
  3. 3.
    Centers for Disease Control and Prevention (2010). Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation—United States, 2007–2009. MMWR Morbidity and Mortality Weekly Report, 59(39), 261–265.Google Scholar
  4. 4.
    Centers for Disease Control and Prevention (2010). Vital signs: State-specific obesity prevalence among adults—United States, 2009. MMWR Morbidity and Mortality Weekly Report, 59(30), 951–955.Google Scholar
  5. 5.
    Centers for Disease Control and Prevention (2011). Vital signs: Prevalence, treatment, and control of hypertension—United States, 1999–2002 and 2005–2008. MMWR Morbidity and Mortality Weekly Report, 60(4), 103–108.Google Scholar
  6. 6.
    Puhl, R. M., & Heuer, C. A. (2010). Obesity stigma: important considerations for public health. American Journal of Public health, 100(6), 1019–1028.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Lee, H., et al. (2013). Cardiovascular Disease Among Black Americans: Comparisons between the US Virgin Islands and the 50 US States. Public Health Reports, 128(3), 170–178.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Sommer, I., et al. (2015) Socioeconomic inequalities in non-communicable diseases and their risk factors: an overview of systematic reviews. BMC Public Health, 15, 914.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Sallis, J. F., & Glanz, K. (2009). Physical activity and food environments: Solutions to the obesity epidemic. The Milbank Quarterly, 87(1), 123–154.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Stokols, D. (1992). Establishing and maintaining healthy environments. Toward a social ecology of health promotion. The American Psychologist, 47(1), 6–22.CrossRefPubMedGoogle Scholar
  11. 11.
    Stokols, D. (2000). Social ecology and behavioral medicine: Implications for training, practice, and policy. Behavioral Medicine, 26(3), 129–138.CrossRefPubMedGoogle Scholar
  12. 12.
    Barber, S., et al. (2016). Double-jeopardy: The joint impact of neighborhood disadvantage and low social cohesion on cumulative risk of disease among African American men and women in the Jackson Heart Study. Social Science and Medicine, 153, 107–115.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Rachele, J. N., Giles-Corti, B., & Turrell, G. (2016). Neighbourhood disadvantage and self-reported type 2 diabetes, heart disease and comorbidity: A cross-sectional multilevel study. Annals of Epidemiology, 26(2), 146–150.CrossRefPubMedGoogle Scholar
  14. 14.
    Dzhambov, A. M., & Dimitrova, D. D. (2016) Long-term self-reported exposure to occupational noise is associated with BMI-defined obesity in the US general population. American Journal of Industrial Medicine, 59(11), 1009–1019.CrossRefPubMedGoogle Scholar
  15. 15.
    Dzhambov, A. M., & Dimitrova, D. D. (2015). Road traffic noise exposure association with self-reported body mass index. Noise Control Engineering Journal, 63(6), 572–581.CrossRefGoogle Scholar
  16. 16.
    Pyko, A., et al. (2015). Exposure to traffic noise and markers of obesity. Occupational and Environmental Medicine, 72(8), 594–601.CrossRefPubMedGoogle Scholar
  17. 17.
    Meline, J., et al. (2015). Road, rail, and air transportation noise in residential and workplace neighborhoods and blood pressure (RECORD Study). Noise and Health, 17(78), 308–319.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Eriksson, C., et al. (2014). Long-term aircraft noise exposure and body mass index, waist circumference, and type 2 diabetes: a prospective study. Environmental Health Perspectives, 122(7), 687–694.PubMedPubMedCentralGoogle Scholar
  19. 19.
    Brownson, R. C., et al. (2009). Measuring the built environment for physical activity: State of the science. American Journal of Preventive Medicine, 36(4 Suppl), S99–123 e12.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Chaix, B. (2009). Geographic life environments and coronary heart disease: A literature review, theoretical contributions, methodological updates, and a research agenda. Annual review of Public Health, 30, 81–105.CrossRefPubMedGoogle Scholar
  21. 21.
    Tenailleau, Q. M., et al. (2015). Assessing residential exposure to urban noise using environmental models: Does the size of the local living neighborhood matter? Journal of Exposure Science and Environmental Epidemiology, 25(1), 89–96.CrossRefPubMedGoogle Scholar
  22. 22.
    Kheirbek, I., et al. (2014). Spatial variation in environmental noise and air pollution in New York City. Journal of Urban Health, 91(3), 415–431.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Boruff, B.J., A. Nathan, & S. Nijenstein (2012) Using GPS technology to (re)-examine operational definitions of ‘neighbourhood’ in place-based health research. International Journal of Health Geographics, 11, 22.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Chaix, B., et al. (2013). GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference? Health and place, 21, 46–51.CrossRefPubMedGoogle Scholar
  25. 25.
    Duncan, D. T., et al. (2014). Examination of how neighborhood definition influences measurements of youths access to tobacco retailers: A methodological note on spatial misclassification. American Journal of Epidemiology, 179(3), 373–381.CrossRefPubMedGoogle Scholar
  26. 26.
    Duncan, D. T., et al. (2017). Quantifying spatial misclassification in exposure to noise complaints among low-income housing residents across New York City neighborhoods: A Global Positioning System (GPS) study. Annals of Epidemiology, 27(1), 67–75.CrossRefPubMedGoogle Scholar
  27. 27.
    Duncan, D. T., et al. (2014). Application of global positioning system methods for the study of obesity and hypertension risk among low-income housing residents in New York City: A spatial feasibility study. Geospatial Health, 9(1), 57–70.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Duncan, D. T., & Regan, S. D. (2015) Mapping multi-day GPS data: a cartographic study in NYC. Journal of Maps, 12, 1–3.Google Scholar
  29. 29.
    Duncan, D. T., et al. (2016). Feasibility and acceptability of Global Positioning System (GPS) methods to study the spatial contexts of substance use and sexual risk behaviors among young men who have sex with men in new york city: A P18 cohort sub-study. PloS ONE, 11(2), e0147520.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Zenk, S.N., et al.( 2011) Activity space environment and dietary and physical activity behaviors: A pilot study. Health and Place, 17(5), 1150–1161.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Krenn, P. J., et al. (2011). Use of global positioning systems to study physical activity and the environment: a systematic review. American Journal of Preventive Medicine, 41(5), 508–515.CrossRefPubMedGoogle Scholar
  32. 32.
    Christian, W. J. (2012) Using geospatial technologies to explore activity-based retail food environments. Spatial and Spatio-Temporal Epidemiology, 3(4), 287–295.CrossRefPubMedGoogle Scholar
  33. 33.
    Data, N.O., 311 Service Requests from 2010 to Present. 2016: NYC Open Data.Google Scholar
  34. 34.
    Salamon, J., Jacoby, C., & Bello, J. P. (2014) A dataset and taxonomy for urban sound research: Proceedings of the ACM International Conference on Multimedia, ACM.Google Scholar
  35. 35.
    Zheng, Y., et al. (2014) Diagnosing New York city’s noises with ubiquitous data: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM.Google Scholar
  36. 36.
    Duncan, D. T., et al. (2016). Perceived spatial stigma, body mass index and blood pressure: a global positioning system study among low-income housing residents in New York City. Geospatial Health, 11(2), 399.CrossRefPubMedGoogle Scholar
  37. 37.
    Hess, P. L., et al. (2007). Barbershops as hypertension detection, referral, and follow-up centers for black men. Hypertension, 49(5), 1040–1046.CrossRefPubMedGoogle Scholar
  38. 38.
    Ravenell, J., et al. (2013). A novel community-based study to address disparities in hypertension and colorectal cancer: A study protocol for a randomized control trial. Trials, 14, 287.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    van Kempen, E. E. M. M., et al. (2002). The association between noise exposure and blood pressure and ischemic heart disease: A meta-analysis. Environmental Health Perspectives, 110(3), 307–317.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Dzhambov, A. M. (2015). Long-term noise exposure and the risk for type 2 diabetes: A meta-analysis. Noise and Health, 17(74), 23–33.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Population HealthNew York University School of MedicineNew YorkUSA
  2. 2.Wagner Graduate School of Public ServiceNew York UniversityNew YorkUSA
  3. 3.Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public HealthParisFrance
  4. 4.Inserm, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public HealthParisFrance

Personalised recommendations