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P27 Can we better capture longitudinal exposure to the neighbourhood environment? a latent class growth analysis of the obesogenic environment in new york city, 1990–2010
  1. N Berger1,
  2. TK Kaufman2,
  3. MDM Bader3,
  4. DM Sheehan2,
  5. SJ Mooney4,
  6. KM Neckerman5,
  7. AG Rundle2,
  8. GS Lovasi6
  1. 1Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, UK
  2. 2Department of Epidemiology, Columbia University Mailman School of Public Health, New York City, USA
  3. 3Department of Sociology, Centre on Health, Risk and Society, American University, Washington, USA
  4. 4Department of Epidemiology, School of Public Health, Harborview Injury Prevention and Research Centre, University of Washington, Seattle, USA
  5. 5Columbia Population Research Centre, New York City, USA
  6. 6Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Urban Health Collaborative, Drexel University, Philadelphia, USA


Background The growing availability of (non-)commercial historical datasets opens a new avenue of research on how long-term exposure to the neighbourhood environment affects health. However, traditional tools for longitudinal analysis (e.g. mixed models) are limited in their ability to operationalise long-term exposure. This study aims to summarise longitudinal exposure to the neighbourhood using latent class growth analysis (LCGA). Using the National Establishment Time-Series (NETS) 1990–2010, we analysed the trajectory of change in New York City (NYC) in the number of unhealthy food businesses – a potential indicator of an obesogenic environment.

Methods The NETS is a commercial dataset providing retail business information in the United States. NYC data were acquired for the period 1990–2010. Businesses were grouped into researcher-defined categories based on Standard Industrial Classification codes and other fields such as business name. All businesses were re-geocoded to ensure accurate localisation. We defined access to BMI-unhealthy businesses (characterised as selling calorie-dense foods such as pizza and pastries) as the total number of BMI-unhealthy businesses present in each NYC census tract (n=2,167) in January of each year. We conducted LCGA in Mplus to identify census tracts with similar trajectories of BMI-unhealthy businesses. We used model fit statistics and interpretability to determine the number of classes. Using the final models, we assigned census tracts to latent classes. We predicted class membership with socio-demographic variables from the Census (population size, income, and ethnic composition) using multinomial logistic regressions and reported predicted probabilities with 95% CI. Sensitivity analyses were undertaken.

Results The final models include 5 and 10 latent classes, respectively. The 5-class solution indicates an overall increase in the number of BMI-unhealthy businesses over time and shows a pattern of fanning out: the higher the value in 1990, the greater the increase over time. Classes are associated with 1990 population size, income, proportion of Black residents (all p<0.001), proportion of Hispanic residents (p=0.033), and 1990–2010 change in population size and income (p<0.001). The 10-class solution identifies two pairs of classes with similar 1990 values, but different trajectories. Differences in those trajectories are associated with population size and ethnic composition (p<0.001).

Conclusion This study illustrates how LCGA contributes to the understanding of long-term exposure to the obesogenic environment. The technique can easily be applied to other aspects of the neighbourhood and to other geographies. When linked with health data, identified latent classes can be used to assess how longitudinal exposure to changing neighbourhoods affects health.

  • neighbourhood
  • methods
  • obesity

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