3. Chapter 3: METHODOLOGY
3.5 Baseline phase: research methods
A mixed methods design, was used in two parts of this study: part one used interviewing and participatory qualitative methods; and part two used quantitative methods with a particular emphasis on the use of Global Positioning Systems (GPS), combined with in-depth interviewing and a socio-economic questionnaire.
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Elders’ memories of HAT interventions (interviews)
In-depth interviews were conducted with 24 elders (16 men and 8 women) from eight villages (Baseline 1, Figure 3.3). The villages were selected through purposive sampling methods, based on their location in known local HAT foci. Four villages were selected in the area where, in 2010 (i.e. within 12 months of the study) Médecins sans Frontières (MSF) still detected HAT cases (HAT foci) during an active screening and treatment campaign. The other four villages were selected from the area where no recent or historical cases have been reported (non-HAT foci) [72]. Participants were selected using snowball sampling techniques [208]: starting with an initial small sample of elders in each location, who were subsequently asked to identify others who may be willing to be interviewed. This approach led us from one elder to another in each village and finally eight women and fifteen men were interviewed until we reached data saturation point [208]. The average reported age of the participants was 71 years albeit most of them were unable to state their exact year of birth. Interviews were conducted at participants’ homes using an interview guide with several general open-ended questions, and probes to obtain further details on their memories of HAT and disease control interventions.
Community risk analysis
Perceived risk to tsetse and HAT (seasonal calendars, participatory mapping)
Sixteen focus groups discussions (FGDs) were held in four villages (Baseline 2, Figure 3.3) using two different participatory approaches: i) seasonal calendar (discussed in eight FGDs) and ii) participatory mapping (discussed in eight FGDs). To capture gender-specific views, groups were organized according to gender and on average, each group (regardless of gender), had 11 participants. Each group drew symbols in order to identify different seasons of the year, and based on these calendars, discussions on gender- related activities implemented throughout the year were held. When the calendars were completed, three petri dishes with different numbers of tsetse (some containing a few, some many, and some many more) were distributed to the groups. They were instructed to place each petri dish on the top of each season depending on the experienced intensity of the contact with the tsetse in each season (Figure 3.4). Discussion on gender related activities that facilitate this contact were introduced in due course.
Participatory mapping techniques were used to explore patterns of daily and seasonal movements within the village and its surroundings. This was carried out to explore if there are any additional special zones of human-tsetse contact which may have been missed in more temporally-oriented seasonal calendars. Each group was asked to draw a map of the village with important landmarks within and outside the villages (Figure 3.5). The discussions which followed were focused around frequency and patterns of
44 human migration within the village and its environs.
Exploring actual risk of tsetse bites (GPS techniques, in-depth interviews, socio-demographic questionnaire)
Actual risk of tsetse bites among men and women was evaluated using GPS mapping together with in- depth interviews and demographic questionnaire to help interpret the GPS data. Sixty households were
Figure 3.4: Seasonal calendars as drawn by participants
45 randomly selected in four villages (Baseline 2, Figure 3.3). Randomisation was carried out using a household village list which was obtained from the village chiefs. Households on the list were numbered and numbers selected randomly. A single volunteer from the household was approached and after the baseline acceptance interview, the time for the next visit was agreed upon; if no participant was obtained from a selected household a neighbouring household was approached until the final number of participants and equal gender distribution was obtained. The socio-economic information was collected, prior to the distribution of GPS trackers, using a questionnaire. At sunrise, participants were given small light GPS trackers (I-GotU GT-120, Mobile Action U.K., dimension 44.5 x 28.5 x13 mm; weight 20g) to carry on their upper arms (Figure 3.6) from sunrise to sunset (~11 hours on average). Participants were instructed to carry on with their usual duties through the day without changing their usual routine. Trackers were recording and storing participants’ location every 3 minutes. At sunset, I collected tracking units to download the data onto my personal computer. Individual movement maps were created for each participant using Google Earth [209]. The research team returned the next morning with the individual maps and carried out in-depth interviews with participants about their previous day’s activities.
Data analysis
Qualitative analysis
Discussions and interviews were analysed using a thematic analysis approach [208]. All audio recordings
Figure 3.6: Two participants carrying the GPS tracker on their upper arms
46 were translated and transcribed by trained field assistants with backgrounds in social sciences. Transcriptions, saved as Word documents, were read numerous times until the obvious codes were identified. Text was then coded using MAXQDA software [210] and participants’ quotes, organized by codes, were collated into a matrix table, which allowed further examination of themes and silent messages. This analytical method was applied to all other interviews and FGD data collected in my research, including data gathered in the implementation and evaluation phases and the stakeholders’ reflections phases, unless otherwise described in relevant sections.
Quantitative analysis: socio-demographic questionnaire; GPS trekking and mapping analysis
Data from the socio-economic questionnaires were analysed by social and economic categories to obtain an average profile of my participants.
Tracking data was successfully downloaded from 58 GPS units (28 women, 30 men). These data were saved in gpx format and imported into ArcGIS 10.1 (esri 2012) for analysis. Using the same software, ‘risk zones’ were defined as being 10, 20, 50 and 100 m on each side of the river in order to delimit areas where riverine tsetse concentrate. Henceforth, these buffers are termed “tsetse risk zones”. Tsetse risk zones were chosen as a standard measure since according to the published literature [211] on the same tsetse species found in the study area (Glossina fuscipes fuscipes Newstead), flies are found largely within 10 m of river banks and no flies were caught further then 100 meters away. Individual movement maps were then overlaid on the top of tsetse risk zones. The areas of intersections between individual movement maps and tsetse risk zones were identified by appearance of waypoints and used for further analysis.
All the movements in each village were plotted to examine potential zones of aggregation of movement within the village. A convex hull polygon (the smallest area that contains all the waypoints) [212] was drawn for each village to mark the area covered by participants’ movements. The maps were overlaid with land use layers5 [213] in ArcGIS to determine what kind of land-use zones the participants crossed in the course of their daily movements.
The effect of gender on the proportion of time that a person was in a ‘risk’ zone was statistically analysed by fitting general linear models (GLM) with a binomial error structure and a logit link. The total number of points recorded by the GPS carried by each person was specified as the binomial denominator and the number of points within the risk zone was the response variable. Gender was specified as a factor. If the
5 An aggregated 1:250,000 scale land-cover map derived from Landsat TM Imagery (part of the Africover project) was used to overlay tracks.
47 data were overdispersed then a quasibinomial model was fitted to the data. Analyses were carried out using R [214].