Exploring Incidence-Prevalence Patterns in Spatial Epidemiology via Neighborhood Rough Sets

Exploring Incidence-Prevalence Patterns in Spatial Epidemiology via Neighborhood Rough Sets

Sharmila Banu K. (VIT University, Vellore, India) and B.K. Tripathy (School of Computing Science and Engineering, VIT University, Vellore, India)
DOI: 10.4018/IJHISI.2017010103
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Epidemiological studies are largely purposed to provide outcomes that may be used for interventions and development programs. In recent years, geo-referencing of epidemiological data has become one of the vital features. Often, epidemiological data collected for regions under study will show areas that are affected with certain diseases in the form of incidence or prevalence information. As well, such information may be spatially mapped and used for further analysis on pattern comparisons. A key objective of such analytic works would be to come up with effective interventions, including planning for remedial measures. In this paper, the authors propose the use of Neighborhood Rough Set Theory (NRST) to be applied innovatively within a mapped area featured with incident-prevalent cases of a disease and computing the similarity patterns among various affected areas in a region. The similarity statistics and/or indices thus computed may aid in planning remedial measures in a geographic region whose sub-regions are marked with various incidence-prevalence information.
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1. Introduction

Policymakers, scientists, educators and other stakeholders such as administrators have always considered public health to be a key discipline for guiding future directions and planning of population disease prevention and control measures, predicting epidemiologic trends and variability in the demands on healthcare manpower and care services delivery. Boulos (2004), for example, emphasizes the need to study, analyse and uncover patterns in an epidemic to come up with policies which will include potential interventions and developmental efforts.

Epidemiologic studies may be designed either as descriptive for the purpose of developing hypothesis or analytic (including case-based, cohort and ecological studies) for the purpose of hypothesis testing. Case-based (case-control), cohort and ecological studies are all observational studies; essentially, a case-based design aims at investigating the relationship between causes of certain diseases and outcomes; a cohort design aims at deducing the associated risks of certain diseases with exposures; and finally, ecological studies include the understanding of environmental role in the occurrence of certain diseases at the group level. The environment parameter is tricky because more than one perspective may be applied to its interpretation. One is that people belonging in the same environment are exposed to the same set of parameters which is called the spatial autocorrelation as noted by Miller (2004). With increasing distances, it is argued that this correlation withers down with respect to a specific environment.

Ferguson et al. (2016) highlight information on health networks and document the shortcomings of improper distribution of health facilities given that most such networks are not always optimized. The authors recall Kost et al. (2015) who studied a geographic region in Thailand and discovered that some parts of the region have no access to a cardiac healthcare facility. Geo-referencing of medical infrastructure helps in identifying the effective locations of healthcare services. In fact, similarity of health status across geographical locations may be maintained as part of health networks. If this information is readily available, then strategic locations for health centres can be identified quickly and improved access to care facilities can be provided at an optimal distance for populations from different parts of a region.

Importantly, healthcare needs should and could be identified for similar regions and need-based access to critical services and facilities can be strategized. Studying disease spread across locations should also include specific consideration to spatial autocorrelation of regions. Epidemiologists define prevalent cases to be those who are affected with a specific disease during the period of study. The onset of the disease is not considered. Incident cases are those segments of population developing the disease outcome during the period of study. Numerous disease incidence-prevalence research on the spatial spread of diseases has been published. Examples to date include studies on Parkinson’s disease (e.g., Willis et al., 2010), human lyme disease (e.g., Kugeler et al., 2015), neural tube birth defect (e.g., Bai et al., 2010), and human cell lymphoma virus type 1 (HTLV-1) (e.g., European Centre for Disease Prevention and Control’s Technical Report, 2015). All of these works help to identify the affected population across a specific area or country and across the world. The geo-referencing of related epidemiologic data provides valuable insights into the geographies affected with the same disease instances and calls for seeking spatial associations.

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