Human Movement Patterns on the Thailand-Myanmar Border

January 7, 2019 updated by: University of Oxford

Movement and Migration Patterns Among a Cohort of Villagers From the Thailand-Myanmar Border

The epidemiology and ecology of malaria in humans includes complex interactions between human hosts and mosquito vectors. These interactions are spatio-temporal in nature and are heavily dependent on transportation capabilities and seasonal conditions. Where and when infections are acquired is not well understood in the Greater Mekong Subregion (GMS), where there are numerous vectors, many with different behaviours and habitats. For example, many infections appear to be associated with forests or forest edges and some of the most important mosquito vectors in the region are forest dwellers (Obsomer, Defourny, and Coosemans 2007). Interventions that target houses at night-time (e.g. mosquito nets), have had limited success in the GMS, most likely because at least some infections are acquired during the day or outside of the home (Dolan et al. 1993; Luxemburger et al. 1994).

While overall malaria incidence in the region appears to be declining, the disease remains persistent in small subregions, for example along international borders joining Thailand with Myanmar. It will be crucial for elimination efforts to address the persistent malaria in these regions, most likely requiring the use of novel and spatially targeted approaches.

Increasingly, spatial data and analyses are used in disease research (Linard and Tatem 2012; Pybus et al. 2016; Tatem et al. 2012), however most spatial analyses are at aggregate scales, using data from provincial or state levels. More detailed studies have a single geographic reference point per individual in the study, frequently the home (Mosha et al. 2014; Parker et al. 2015). These studies allow researchers to investigate potential clustering of cases within and between houses ("hotspots") (Bejon et al. 2014; Bousema et al. 2012; Mosha et al. 2014). Even these detailed studies typically ignore the spaces in which people spend time outside of their home and where they may acquire infection: schools; places of worship and work; forest camps and temporary shelters. Given that many malaria infections in the GMS are acquired outside of the home, in areas that are not usually mapped, this information is important for developing strategies to prevent transmission and will be crucial for achieving elimination.

Researchers in other substantive areas have already begun mapping the movement patterns of study subjects so that exposure to a variety of environmental exposures outside of the home can be assessed (Matthews and Yang 2013; Vazquez-Prokopec et al. 2010). Early approaches relied on travel surveys or travel diaries, both having bias of unknown magnitude. Modern wearable global positioning satellite (GPS) instruments (loggers or trackers) and geographic information science (GIS) enable detailed mapping and quantification of human movement patterns. Through analysing differences in the movement patterns between humans who do versus those that do not acquire infectious diseases, it may be possible to identify a narrower set of geographic spaces in which disease transmission is occurring. Public health interventions could then target those risk areas.

Most of these detailed studies have been done in economically developed settings and urban environments. Infectious diseases such as malaria remain persistent in resource-poor, rural, and remote areas - the very regions that are least likely to be studied with detailed approaches (Sachs and Malaney 2002).

Study Overview

Status

Completed

Conditions

Study Type

Observational

Enrollment (Actual)

54

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

    • Tak
      • Mae Sot, Tak, Thailand
        • Shoklo Malaria Research Unit

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

20 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

  • The participants are Karen or Burmese ethnic group
  • Participants must be above 20 years of age
  • capable of keeping track of the device
  • walking beyond village boundaries
  • willing to consent to the study

Description

Inclusion Criteria:

  • The participants are Karen or Burmese ethnic group
  • Participants must be above 20 years of age
  • Capable of keeping track of the device
  • Walking beyond village boundaries
  • Willing to consent to the study

Exclusion Criteria:

Individuals who do not meet inclusion criteria.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Time-stamped locations
Time Frame: 1 year
A series of spatial data sets that can then be mapped and analyzed
1 year
The latitude and longitude of the GPS logging reading.
Time Frame: 1 year
The GPS logging devices will automatically take a reading every 30 minutes.
1 year
The elevation of the GPS logging reading.
Time Frame: 1 year
The GPS logging devices will automatically take a reading every 30 minutes.
1 year
Date and time of the GPS logging reading.
Time Frame: 1 year
The GPS logging devices will automatically take a reading every 30 minutes.
1 year
Acceptability among participants about carrying the GPS logging device during
Time Frame: 1 year
Participants will also be interviewed throughout the study period and one of the research questions
1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Questionnaires
Time Frame: 1 year
Participants' reported travel histories comparing them to the GPS logger data.
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 14, 2017

Primary Completion (Actual)

February 17, 2018

Study Completion (Actual)

February 17, 2018

Study Registration Dates

First Submitted

March 8, 2017

First Submitted That Met QC Criteria

March 15, 2017

First Posted (Actual)

March 22, 2017

Study Record Updates

Last Update Posted (Actual)

January 8, 2019

Last Update Submitted That Met QC Criteria

January 7, 2019

Last Verified

January 1, 2019

More Information

Terms related to this study

Other Study ID Numbers

  • SMRU1607

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Epidemiology

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