Logo image
Back
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
Journal article   Open access   Peer reviewed

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

N Berger, T K Kaufman, MDM Bader, D M Sheehan, S J Mooney, K M Neckerman, A G Rundle and G S Lovasi
Journal of epidemiology and community health (1979), v 71(Suppl 1), pA63
01 Sep 2017
DOI: https://doi.org/10.1136/jech-2017-SSMAbstracts.129
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1136/jech-2017-ssmabstracts.129View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze),  Open
url
https://doi.org/10.1136/jech-2017-SSMAbstracts.129View
Published, Version of Record (VoR) Open

Abstract

Census Censuses Classification Food Models Nets Population Sensitivity analysis Statistical analysis Studies
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.

Metrics

7 Record Views
1 citations in Web of Science

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being
#10 Reduced Inequalities

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Public, Environmental & Occupational Health
Logo image