There continues to be high rates of infant mortality in poor and minority communities (Sparks et al. 2013; Sparks et al. 2009; Cossman et al, 2010). This is particularly true for rural areas with high concentration of minority households that also tend to be poor (Xu et al. 2014; Sparks and Sparks 2010). The purpose of this response is to discuss the contributions to this area of mortality specifically through spatial models. The continued increase in differentials of infant mortality in rural areas is a cause of concern and continued research.
When discussing infant mortality rates specifically when discussing minority infant mortality rates, much of the research has not focused on the role of space and place as a contributor to infant mortality (Xu et al. 2014; McLaughlin et al. 2007). Spatial statistical modeling serves to fill in the gap of not only how we define and discuss space and place in relation to individuals but also society as a whole (Xu et al. 2014; Sparks et al. 2010). With a better understanding of space and place comes new areas of discussion for not only improvement but also functionality that best serves those who interact in spaces and places that is unlike what may have previously occurred. Spatial modeling provides more in depth information as to how space and place function to inform a number of outcomes including infant mortality (Sparks et al. 2013; Xu et al. 2014; Yang et al. 2015). Spatial modeling gives greater detail into patterns of health and mortality that serve to benefit a variety of industry and agency which can translate into improved health outcomes for those who are at risk for higher rates of infant mortality as a result of living in a high-risk area.
Research studies show that there are higher rates of infant mortality in nonmetropolitan areas as a result of lack of access to needed health resources that occur less in metropolitan areas (McLaughlin et al. 2007; Xu et al. 2014; Sparks et al. 2009). When nonmetropolitan areas continue to have smaller pathways to access compared to metropolitan areas, there is a higher likelihood of higher mortality which would include infant mortality. With a lack of access to the nearest hospital for treatment and preventative measures throughout pregnancy in the case of expectant mothers, there is a higher rate of infant mortality due to complications and concerns that would be preventative in nature as a result of having quality access to health care services. McLaughlin and colleagues (2007) make the point that when we discuss inequality related to health, inequality by definition becomes a multilayered source of mortality (McLaughlin et al. 2007). As a result, when inequality is discussed, the framework of the discussion has to be multi-dimensional in order to capture all of the varied aspects that are impacted by inequality.
Previous statistically analysis in the area of mortality and health care research often contributed to the short sited nature of framing the issue of inequality (Xu et al. 2014; McLaughlin et al. 2007; Sparks and Sparks 2010). As a result, in order to get a more complete picture of inequality, the use of spatial statistical modeling is imperative. Spatial statistically modeling provides the insight into the highest areas of need and also the proximity to needed resources in order to better inform the allocation of resources for preventative measures at a larger scale.
Historically cities had higher mortality rates (Xu et al. 2014) and as a result of using spatial statistical modeling, there was greater insight into the historical health of a neighborhood and also insight into future health research. In the case of excess nonmetropolitan death, spatial statistical models provided the data needed to make the case for more research being needed in nonmetropolitan areas (McLaughlin et al. 2007; Xu et al. 2014; Sparks et al. 2013). This research shows that there is a need for reinvestment into nonmetropolitan areas in order to prevent further excess mortality and devastation of nonmetropolitan land. This is critical for areas that have historically experienced wide scale inequality specifically in the rural South and also Native American reservations (Sparks et al. 2013; Xu et al. 2014).
Using the idea that McLaughlin and colleagues (2007) present of spatial inequality equates to mortality inequality, which Sparks and Sparks (2010) also contend, makes the argument for the need of not only resource reinvestment but also ecological reinvestment through various means such as the addition of green spaces and the elimination of known spaces that contribute to poor health outcomes such as revitalizing unused spaces, cleaning rivers, reimaging recycling/landfill structures.
These are some of the ways that spatial statistical analysis can inform not only health practices but help to combat rising infant mortality rates in areas of high inequality that are often nonmetropolitan in nature.