What is the NEAP?Ignoring people's daily mobility and exposures to nonresidential contexts may lead to erroneous results in epidemiological studies of people's exposures to and the health impact of environmental factors.
The neighborhood effect averaging problem (NEAP) refers to the problem that, "for health outcomes that are also affected by exposures to environmental factors in people's nonresidential neighborhoods as they move around in their daily life (mobility-dependent exposures), using residence-based neighborhoods to estimate individual exposures to and the health impact of environmental factors will tend to overestimate the statistical significance and effect size of the neighborhood effect because it ignores the confounding effect of neighborhood effect averaging that arises from human daily mobility." The NEAP was first identified and described in Kwan (2018).
How does neghborhood effect averaging operate?Neghborhood effect averaging operates as follows: "Given that most people move around to undertake their daily activities (e.g., shop, attend school, or go to work), they are exposed to the environmental contexts of many different areas outside their residential neighborhoods in the course of a day. As a person travels to areas outside of his or her residential neighborhood, the person may experience similar or different levels of exposure when compared to that of his or her residential neighborhood. Because of the diversity in the intensity of the environmental factor in question (e.g., air pollution) over space in any study area, a person's exposure level in nonresidential neighborhoods may be higher, lower, or similar when compared to the exposure level experienced in his or her residential neighborhood. As indicated by recent studies, the probability distribution of individual residence-based exposure approximates a bell-shaped distribution, which means that many people have exposure levels around the mean value for the population of the study area, while fewer people have very high or low exposure levels. Therefore, a person who lives in a neighborhood with a high level of an environmental factor (and thus exposure) will visit areas that are more likely to have lower levels of such environmental factor as a result of his or her daily mobility, while a person who lives in a neighborhood with a low level of the environmental factor will visit areas that are more likely to have higher levels of such environmental factor. For those who have residence-based exposure levels around the mean value, their exposure levels in nonresidential neighborhoods tend to be similar to those of their residential neighborhoods because they will visit areas that are less likely to have significantly different levels of such environmental factor in their daily life."
"As a result, the neighborhood effect assessed with a traditional residence-based approach for individuals whose residence-based exposures are much higher than the mean exposure will be overestimated because these individuals tend to experience lower levels of exposure outside their residential neighborhoods, which attenuates their high exposures in their residential neighborhoods. On the other hand, the neighborhood effect for individuals whose residence-based exposures are much lower than the mean exposure will be underestimated because these individuals tend to experience higher levels of exposure outside their residential neighborhoods, which moderates their low exposures in their residential neighborhoods."
"Taking people's daily mobility into account (which will generate more accurate assessments for mobility-dependent exposures) will therefore lead to an overall tendency toward the mean exposure because exposure levels for people whose residence-based exposures are lower or higher than the mean exposure will tend toward the mean exposure, thus moderating the influence of the environmental factor in their residential neighborhoods on their health behaviors or outcomes. This is neighborhood effect averaging, which is a fundamental methodological issue when examining the health effects of mobility-dependent exposures and may be called the neighborhood effect averaging problem (NEAP)."
Policy implications of the NEAP"The implications of neighborhood effect averaging for public health policies is that increasing the mobility of those who live in disadvantaged neighborhoods through better, safer, and more reliable public transit, in addition to improving neighborhood quality in situ, may be helpful for improving their health outcomes."
References-- Please see the following article for a discussion of the NEAP:
Kwan, M.-P. (2018) The neighborhood effect averaging problem (NEAP): An elusive confounder of the neighborhood effect. International Journal of Environmental Research and Public Health, 15: 1841.
-- The following recent studies provide strong evidence for the NEAP:
Dewulf, B., T. Neutens, W. Lefebvre, G. Seynaeve, C. Vanpoucke, C. Beckx, and N. van de Weghe (2016) Dynamic assessment of exposure to air pollution using mobile phone data. International Journal of Health Geogrraphics, 15: 14.
Kim, J., and M.-P. Kwan. 2019. Beyond commuting: Ignoring individuals' activity-travel patterns may lead to inaccurate assessments of their exposure to traffic congestion. International Journal of Environmental Research and Public Health, 16(1), 89.
Yu, H., A. Russell, J. Mulholland, and Z. Huang (2018) Using cell phone location to assess misclassification errors in air pollution exposure estimation. Environmental Pollution, 233: 261-266.
Last Updated on February 18, 2019.