Elucidating the spatially varying relation between cervical cancer and socio-economic conditions in England

Abstract

BackgroundGeographically weighted Poisson regression (GWPR) was applied to the relation between cervical cancer disease incidence rates in England and socio-economic deprivation, social status and family structure covariates. Local parameters were estimated which describe the spatial variation in the relations between incidence and socio-economic covariates.ResultsA global (stationary) regression model revealed a significant correlation between cervical cancer incidence rates and social status. However, a local (non-stationary) GWPR model provided a better fit with less spatial correlation (positive autocorrelation) in the residuals. Moreover, the GWPR model was able to represent local variation in the relations between cervical cancer incidence and socio-economic covariates across space, whereas the global model represented only the overall (or average) relation for the whole of England. The global model could lead to misinterpretation of the relations between cervical cancer incidence and socio-economic covariates locally.ConclusionsCervical cancer incidence was shown to have a non-stationary relationship with spatially varying covariates that are available through national datasets. As a result, it was shown that if low social status sectors of the population are to be targeted preferentially, this targeting should be done on a region-by-region basis such as to optimize health outcomes. While such a strategy may be difficult to implement in practice, the research does highlight the inequalities inherent in a uniform intervention approach.

Spatiotemporal analysis of the relationship between socioeconomic factors and stroke in the Portuguese mainland population under 65 years old

“…Indeed, a socioeconomic factor may be more relevant as determinant of stroke in some municipalities (or group of municipalities) and less in others. Local Poisson GWR results successfully revealed the existence of spatial patterns in the spatially varying coefficients in this study, as already observed in other studies following this methodology for other diseases (St-Hilaire et al, 2010;Cheng et al, 2011;Helbich et al, 2012;Weisent et al, 2012). This pattern was also apparent in a very recent communication focusing on geographic disparities with respect to neighbourhood for heart attack and stroke in Tennessee, USA (Odoi and Busingye, 2014).…”

“…Since it estimates regression coefficients for each location in the study area, maps generated from these data play an important role in exploring and interpreting spatial nonstationarity (Mennis, 2006). With this in mind, several studies have already implemented this method, aiming to improve our understanding of the determinants of geographic disparities of Health (St-Hilaire et al, 2010;Cheng et al, 2011;Helbich et al, 2012;Weisent et al, 2012). There is some controversy surrounding GWR, with some authors stating that this technique is more suitable for exploratory analysis.…”

“…An offset was specified, corresponding to the estimation of the municipality population of men or women <65 years of age in 1994 or 2004, for the 1992-1996 and 2002-2006 periods, respectively. When dealing with disease count data, particularly for diseases with a reduced number of cases, the Poisson model can be a suitable regression model. Other disease analysis studies dealing with small areas have applied the Poisson model to describe a disease distribution (Cheng et al, 2011). Thus, univariate (simple) ordinary Poisson regression models were then fitted to the data using the generalized linear model (Dobson, 2002) function using the R statistical software (R project, 2014d).…”

Hydrology and public health: linking human leptospirosis and local hydrological dynamics in Trinidad, West Indies

“…In the fixed kernel, the optimal distance away from the regression point will be found; for adaptive, the optimal number of neighbors for use in the regression will be found. To avoid large standard errors and bias in local parameter estimates (due to either few data points in a short distance or a large number of points with a long distance to the regression point i) an optimal size of bandwidth is necessary (Cheng et al 2011). In this study, the Akaike Information Criterion with a correction for finite sample sizes (AICc) (Fotheringham et al 2002) was used as indicator to analyze the performance or goodness of fit of the models and the performance of the bandwidth, where the model with the minimum AICc value was selected as having the optimum bandwidth.…”

“…Where Se refers to the local standard error of the kth parameter estimate, taking in account the variation in the data (Cheng et al 2011). Pseudo t-values follow approximately a standard normal distribution if the true regression parameter is zero and mapping them is useful for identifying spatial variations in relationships between explanatory covariates and the outcome covariate.…”