Deprivation Indices, Population Health and Geography: An Evaluation of the Spatial Effectiveness of Indices at Multiple Scales

Abstract

Area-based deprivation indices (ABDIs) have become a common tool with which to investigate the patterns and magnitude of socioeconomic inequalities in health. ABDIs are also used as a proxy for individual socioeconomic status. Despite their widespread use, comparably less attention has been focused on their geographic variability and practical concerns surrounding the Modifiable Area Unit Problem (MAUP) than on the individual attributes that make up the indices. Although scale is increasingly recognized as an important factor in interpreting mapped results among population health researchers, less attention has been paid specifically to ABDI and scale. In this paper, we highlight the effect of scale on indices by mapping ABDIs at multiple census scales in an urban area. In addition, we compare self-rated health data from the Canadian Community Health Survey with ABDIs at two census scales. The results of our analysis confirm the influence of spatial extent and scale on mapping population health-with potential implications for health policy implementation and resource distribution.KEYWORDS Deprivation indices, MAUP, Population health, Scale. A BRIEF BIOGRAPHY OF POPULATION HEALTH INDICES COMMONLY USED IN CANADAThe use of census data to quantify socioeconomic deprivation is a generally wellaccepted method of identifying populations with poorer health outcomes.1-5 The history of census-based area deprivation indices dates back to at least until 1971, when the Department of the Environment (DOE) in the United Kingdom used data taken from the census to identify localities where a high proportion of households were exposed to adverse social and economic conditions. 6 The indices were developed to more effectively identify areas in need of resources to improve quality of life. Publications stemming from The Black Report, 7 the Whitehall, 8 and Acheson studies 9 launched additional public scrutiny of the relationship between socioeconomic gradients and health status. These studies have spurred a relatively new yet increasingly popular framework that uses socioeconomic data taken from the census to quantify deprivation and demonstrate its relationship with population health. 2,[10][11][12][13][14] Schuurman, Bell, and Oliver are with the

Distance to health services affects local-level vaccine efficacy for pneumococcal conjugate vaccine (PCV) among rural Filipino children

“…However, there are also other factors, such as care-seeking behaviors, socioeconomic status, and population density, which determine which subpopulations are at higher risk for disease (17). Many of these factors also have a geographic expression; theoretical and empirical evidence from public health, geography, and sociology indicate that people with similar characteristics cluster in space, creating larger spatial patterns within a population (18,19). Thus, spatial analysis of PCV trial data in other regions may similarly provide insight to which populations should be targeted Note: Numbers in the three estimate columns are the SARAR model regression coefficients for each endpoint; numbers in the SE columns are the corresponding SEs for the regression coefficients; exact P values are also provided for each estimate.…”

Deprivation, Healthcare Accessibility and Satisfaction: Geographical Context and Scale Implications

“…Obvious differences of population between census tracts and census blocks exist when analyzing a social index. Schuurman et al (2007) found significant differences in populations classified by deprivation in census blocks and census tracts. We therefore claim that some health-related processes and relationships may be similar at different scales and contexts, but all oversimplification in the interpretation of statistical results should be avoided, and the MAUP and the UGCoP always need to be monitored to ensure the best possible decision making.…”

“…Despite the important role that the area-based measures of deprivation and healthcare accessibility play for health policy making, little attention has been paid to evaluating the contextual and scale implications of these measures, and only few studies have addressed this issue (Cabrera-Barona et al 2016b;Dumedah et al 2008;Haynes and Gale 2000;Schuurman et al 2007;Wei et al 2017). The phenomenon of the modifiable areal unit problem (MAUP) usually causes the results of statistical analyses to differ according to the scale and size of the spatial reporting units (Briant et al 2010;Openshaw 1984).…”

Deprived yet healthy: Neighbourhood-level resilience in New Zealand

“…Other studies have either not explored factors associated with resilience (Tunstall et al, 2007) or have used area units with large populations (Doran et al, 2006;Mitchell et al, 2009;Tunstall et al, 2007). Researchers argue that the modifiable area unit effect is best ameliorated by using a small unit of analysis (Shuurman, Bell, Dunn, & Oliver, 2007). Conversely, the sparsely populated rural areas and relatively small population of New Zealand can be a concern when using rates due to small denominators leading to spurious high proportions and the potential lack of representativeness .…”