This thesis used only secondary, publicly available data for the data analysis. Even though participants were not directly recruited, each of the over 13,000 cases of dengue from 1999-2015 is linked to a person living in Dominica. Ethical practice in research requires consideration of benefits and harms when conducting research to ensure that no harm is done to the participants. In this case an ethical concern could be raised in terms of identifiers that would have the
potential to link a person to the incidence case. There are a number of debates concerning which pieces of information represent identifying data (AGENS 2008; Law 2005). The most apparent
identifiers are name and address; however, occupation, religious affiliation or ethnicity can also be distinct identifiers in certain communities.
The type of secondary data set that causes the most concern with regards to ethical situations is the data set that is collected with regards to an interaction with or intervention on the human subject (CPHS 2014). Ethical concerns regarding secondary use of this data set can include the potential harm to the individual people of the original research study in not having informed consent, especially among vulnerable populations (AGENS 2008; Tripathy 2013). The consent of the individual must be particular to a specific researcher as well as for a specific purpose. The idea of informed consent can get quite complicated as the researcher cannot plan which research project may request their dataset, and thereby will not be able to inform the individuals about possible future uses for the data that was collected originally (CPHS 2014; Law 2005).
Dengue is a reportable disease in Dominica and the data is collected by the state, as well as by the international agencies such as PAHO and CARPHA for surveillance purposes in public health research, this thesis does not represent a deviation from its original intended use. Public data sets for this thesis such as dengue cases per year, per parish from PAHO, CARPHA and the Health Department of Dominica were prepared with the assertion that the data set would be made available to the public and, as a result, are not independently identifiable and their analysis would not engage human subjects directly. As the data set was also without any identifying information such as name, age or address, or even identifiers which fall in the grey area, such as religious affiliation or ethnicity. Therefore, risk to the direct identification of any individual dengue case was minimal and not an ethical issue for this thesis.
The data sets regarding the social determinants of health such as socio-economic status,
data was presented as publicly available data sets in aggregate form with no direct link or
identifier to an individual person within each parish. As a result, the secondary data sets that was be accessed for the purpose of this thesis presents a minimal risk to the human population they were derived from and do not constitute an ethical issue (AGENS 2008; CPHS 2014; Law 2005). Using a quantitative research method, Geographic Information Systems (GIS) was used to
combine multiple data sources to develop and create a visual representation of vulnerability, as outlined in the Water Associated Disease Index (WADI) framework. Using GIS has its own set of ethical issues, as GIS, ―…allows for closer identification of geographic data through the availability of differing degrees of granularity,‖ (Trainor and Dougherty, 2000, page 135). The
ability to triangulate or to combine the data is what constitutes the possible ethical issue, thereby allowing precise identification of individuals even if standard identifying information had been removed from the data set. A methodology that would allow the researcher to triangulate
identity via information procured from GIS has been outlined (Trainor and Dougherty 2000; Law 2005). As there are no identifiers attached to each incidence case of dengue, it was not possible to triangulate or combine the data to determine a specific identity within the data set using GIS technology.
This research project was approved by the Research Ethics Committee at Lancaster University in June 2016.
Chapter 4 Results 4.1 Results Introduction
This chapter will begin by briefly reviewing some key points on dengue and the methodology regarding the construction of the Water Associated Disease Index (WADI). Integrated
approaches are required in order to be able to reduce vector or pathogen exposure so as to decrease human susceptibility to disease. The aim of this thesis was to test the WADI tool, an evidence-based approach to highlighting areas of vulnerability to dengue disease.
The WADI has been proven to be an effective tool in determining vulnerable areas in large populous, heterogeneous countries endemic for dengue (Dickin et al; Schuster and Wallace; Fullerton et al 2014). The aim of this thesis was to determine if that same level of effectiveness could be demonstrated in a less populous, less heterogeneous dengue endemic region by
constructing and validating it for the island of Dominica. These smaller island nations which are also dengue endemic regions have been largely ignored by researchers. The results of the WADI model validation through regression, comparison to a non-index model and then revised using results from the non-index model, indicate that the Water Associated Disease index model may also be a useful tool for less populous and less heterogeneous regions, such as the 24 island nations in the dengue endemic region of Latin America and the Caribbean.
The results show the importance of the ecological and environmental factors in conjunction with the social factors to the increased risk of transmission of the dengue virus, and that the WADI can be used to highlight vulnerable areas to that risk in an endemic area. By using Geographic Information Systems software the WADI value can provide a visual representation of the vulnerable areas in an endemic region Dominica by creating colour coded maps of the area.
Following will be an exploration of the data set in terms of incidence counts and the components generated from the indicator data. The results of the model validation through regression and comparison to the non-index model and the revised WADI model will be reported in detail and the findings outlined.