A random spatial sampling method in a rural developing nation

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Abstract

Background

Nonrandom sampling of populations in developing nations has limitations and can inaccurately estimate health phenomena, especially among hard-to-reach populations such as rural residents. However, random sampling of rural populations in developing nations can be challenged by incomplete enumeration of the base population.

Methods

We describe a stratified random sampling method using geographical information system (GIS) software and global positioning system (GPS) technology for application in a health survey in a rural region of Guatemala, as well as a qualitative study of the enumeration process.

Results

This method offers an alternative sampling technique that could reduce opportunities for bias in household selection compared to cluster methods. However, its use is subject to issues surrounding survey preparation, technological limitations and in-the-field household selection. Application of this method in remote areas will raise challenges surrounding the boundary delineation process, use and translation of satellite imagery between GIS and GPS, and household selection at each survey point in varying field conditions. This method favors household selection in denser urban areas and in new residential developments.

Conclusions

Random spatial sampling methodology can be used to survey a random sample of population in a remote region of a developing nation. Although this method should be further validated and compared with more established methods to determine its utility in social survey applications, it shows promise for use in developing nations with resource-challenged environments where detailed geographic and human census data are less available.

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Most cited references 14

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Don't spin the pen: two alternative methods for second-stage sampling in urban cluster surveys

Rebecca F. Grais, Angela MC Rose, Jean-Paul Guthmann (2007)

In two-stage cluster surveys, the traditional method used in second-stage sampling (in which the first household in a cluster is selected) is time-consuming and may result in biased estimates of the indicator of interest. Firstly, a random direction from the center of the cluster is selected, usually by spinning a pen. The houses along that direction are then counted out to the boundary of the cluster, and one is then selected at random to be the first household surveyed. This process favors households towards the center of the cluster, but it could easily be improved. During a recent meningitis vaccination coverage survey in Maradi, Niger, we compared this method of first household selection to two alternatives in urban zones: 1) using a superimposed grid on the map of the cluster area and randomly selecting an intersection; and 2) drawing the perimeter of the cluster area using a Global Positioning System (GPS) and randomly selecting one point within the perimeter. Although we only compared a limited number of clusters using each method, we found the sampling grid method to be the fastest and easiest for field survey teams, although it does require a map of the area. Selecting a random GPS point was also found to be a good method, once adequate training can be provided. Spinning the pen and counting households to the boundary was the most complicated and time-consuming. The two methods tested here represent simpler, quicker and potentially more robust alternatives to spinning the pen for cluster surveys in urban areas. However, in rural areas, these alternatives would favor initial household selection from lower density (or even potentially empty) areas. Bearing in mind these limitations, as well as available resources and feasibility, investigators should choose the most appropriate method for their particular survey context.

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Mortality, crime and access to basic needs before and after the Haiti earthquake: a random survey of Port-au-Prince households.

Athena R. Kolbe, Royce A. Hutson, Harry Shannon … (2015)

On 12 January 2010 an earthquake measuring 7.0 on the Richter Scale struck Haiti, causing unprecedented death, injury and destruction for an event of this magnitude. Our aim was to generate a rapid assessment of the primary consequences for the population of the metropolitan area of Port-au-Prince, the national capital. During the summer of 2009 we conducted a survey of 1,800 households in metropolitan Port-au-Prince. Six weeks after the earthquake, we attempted to trace these households in order to re-interview them. The questionnaire examined mortality and injuries generated by the natural disaster, as well as the character of victimization, food security and living arrangements following the quake. Data analysis incorporated sampling weights and adjusted for clustering within households. The original 2009 survey featured a 90 per cent response rate; in 2010 we re-interviewed 93 per cent of these households. We estimate that 158,679 people in Port-au-Prince (95 per cent CI 136,813-180,545) died during the quake or in the six-week period afterwards owing to injuries or illness. Children were at particular risk for death. In the six weeks after the earthquake, 10,813 people (95 per cent CI 6,726-14,900) were sexually assaulted, the vast majority of whom were female. In the same period 4,645 individuals (95 per cent CI 1,943-7,347) were physically assaulted. Of all households, 18.6 per cent (95 per cent CI 16.6-20.8) were experiencing severe food insecurity six weeks after the earthquake. 24.4 per cent (95 per cent CI 22.1-26.9) of respondents' homes were completely destroyed. Many residents of Port-au-Prince died during or as a result of the earthquake, albeit fewer than were widely reported. More than half of the capital's population experienced moderate to severe food insecurity, though remittances are a major protective factor in promoting food security. Survivors continue to experience high levels of sexual assault and limited access to durable shelter.

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Methods for health surveys in difficult settings: charting progress, moving forward

Kristof Bostoen, Oleg Bilukha, Bridget Fenn … (2007)

Health surveys are a very important component of the epidemiology toolbox, and play a critical role in gauging population health, especially in developing countries. Research on health survey methods, however, is sparse. In particular, current sampling methods are not well adapted for certain 'difficult' settings, such as emergencies, remote regions without easily available sampling frames, hidden and vulnerable population groups, urban slums and populations living under strong political pressure. This special issue of Emerging Themes in Epidemiology is entirely devoted to survey methods in such settings, and builds upon a successful conference in London highlighting problems with current approaches and possible ways forward. Greater investment in research on health survey methods is needed and will have beneficial effects for populations in need.

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Author and article information

Contributors

Michelle C Kondo

Kent DW Bream

Frances K Barg

Charles C Branas

Journal

Journal ID (nlm-ta): BMC Public Health

Journal ID (iso-abbrev): BMC Public Health

Title: BMC Public Health

Publisher: BioMed Central

ISSN (Electronic): 1471-2458

Publication date Collection: 2014

Publication date (Electronic): 10 April 2014

Volume: 14

Page: 338

Affiliations

[1 ]United States Department of Agriculture—Forest Service, Northern Research Station, 100 North 20th St Suite 205, Philadelphia, PA 19103, USA

[2 ]Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Blockley Hall, Philadelphia, PA 19104-6021, USA

[3 ]Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, 141-2 Anatomy and Chemistry, 3620 Hamilton Walk, Philadelphia, PA 19104, USA

Article

Publisher ID: 1471-2458-14-338

DOI: 10.1186/1471-2458-14-338

PMC ID: 4021077

PubMed ID: 24716473

SO-VID: d3d31fe5-6f9b-4b70-ad30-b3d99bf12ccb

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

A random spatial sampling method in a rural developing nation

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Abstract

Background

Methods

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AIP Publishing: Coronavirus

Most cited references 14

Don't spin the pen: two alternative methods for second-stage sampling in urban cluster surveys

Mortality, crime and access to basic needs before and after the Haiti earthquake: a random survey of Port-au-Prince households.

Methods for health surveys in difficult settings: charting progress, moving forward

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