Self-assessed health status and neighborhood context

ABSTRACT

In recent years, there has been growing interest in the relationship between the characteristics of neighborhoods and the health and well-being of residents. The focus on neighborhood as a health determinant is based on the hypothesis that residing in a disadvantaged neighborhood can negatively influence health outcomes beyond the effect of individual characteristics. In this article, we examine three possible ways of measuring neighborhood socio-economic status, and how they each impact on self-reported health status beyond the effect contributed by individual-level factors. Using individual-level data from the Household Income and Labor Dynamics Australia survey combined with neighborhood-level (suburb) data, we tested the proposition that how one measures neighborhood socio-economic characteristics may provide an important new insight into understanding the links between individual-level outcomes and neighborhood-level characteristics. The findings from the analysis illustrate that although individual-level factors may be important to understanding health outcomes, how one accounts for neighborhood-level socio-economic status may be equally important. The findings suggest that in developing place-based health programs, policy makers need to account for the complex interactions between individual drivers and the potential complexities of accounting for neighborhood socio-economic status.

KEYWORDS:

• Neighborhood context
• place-based health policy
• self-reported health
• socio-economic status

Notes

The Thiel index is calculated as:

where π_r refers to a particular household’s proportion of the whole neighborhood population. All logarithmic calculations use the natural log. Higher diversity index scores reflect greater diversity of income distribution within that suburb. The maximum level of entropy is given by the natural log of the number of groups (i.e., income categories) used in the calculations.

We fitted models with reference to the following equation:

where y_ij is the outcome on the categorical dependent variable accounting for self assessed health status for individual i in suburb j; IC_ij is a range of variables accounting for the characteristics of individual i in suburb j; BC_ij is a range of variables accounting for behavioral characteristics of individual i in suburb j; NC_j is a range of variables accounting for the characteristics of the suburb j; λ is a correction accounting for the clustering of individuals within a given suburb.