Medical Geography: A Promising Field of Application for Geostatistics

Abstract

The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. It presents, however, several methodological challenges that arise from the fact that data are typically aggregated over irregular spatial supports and consist of a numerator and a denominator (i.e. population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: estimation of the underlying disease risk, detection of areas with significantly higher risk, and analysis of relationships with putative risk factors. The analysis is illustrated using age-adjusted cervix cancer mortality rates recorded over the 1970-1994 period for 118 counties of four states in the Western USA. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlation with two putative explanatory variables: percentage of habitants living below the federally defined poverty line, and percentage of Hispanic females. Area-to-point kriging formulation creates continuous maps of mortality risk, reducing the visual bias associated with the interpretation of choropleth maps. Stochastic simulation is used to generate realizations of cancer mortality maps, which allows one to quantify numerically how the uncertainty about the spatial distribution of health outcomes translates into uncertainty about the location of clusters of high values or the correlation with covariates. Last, geographically-weighted regression highlights the non-stationarity in the explanatory power of covariates: the higher mortality values along the coast are better explained by the two covariates than the lower risk recorded in Utah.

Spatial Relationship Quantification between Environmental, Socioeconomic and Health Data at Different Geographic Levels

“…To account for the reliability of the kriged risks in the regression, each observation receives a weight that is the reciprocal of the kriging variance [33]. …”

Spatial analysis of drug poisoning deaths in the American West, particularly Utah

“…A number of conditions particularly smoking related cancers, are far lower in UT than other states (Devesa et al, 1999; Goovaerts, 2009; Goovaerts and Gebreab, 2008 Merrill et al 1999 and Tudor-Locke et al 2007) which has been attributed to about 70% of the population being members of the Church of Jesus Christ of Latter-day Saints (LDS faith) (http://www.theARDA.com) that adhere to a health code prohibiting use of tobacco, alcohol, tea, coffee and illicit drugs (Merrill et al, 1999; Merrill and Lyon, 2005; Merrill and Folsom; 2005) and a mandate to obey the laws of the land (LDS Church, 2013). UT has the highest concentration of a single religion in the United States (Merrill, 2004); and consequently the lowest levels of smoking, alcohol consumption and illicit drug use in the USA (Merrill, 2004).…”

Expansion of Protected Areas under Climate Change: An Example of Mountainous Tree Species in Taiwan

“…where 1000 is the number of realizations, and ( ) is the number from the 1000 realizations in which the simulated species were present at location k. The joint probability (Pj, global uncertainty) in m occurrence locations ( , , , ⋯ ) was written as follows [63,64]:…”

“…The joint probabilities increase with the critical probability under each scenario (Table 3). Kerry et al (2010) and Goovaerts (2009) suggest the critical probabilities 75% and 90% are enough to generate a reliable joint probability. In this study, the joint probabilities under each scenario are larger than 0.5 when the critical probability reaches 0.75 [63,69].…”