Are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria?
Abstract Objective When using global positioning systems (GPS) to assess an individual’s exposure to their environment, a first step in data cleaning is to establish minimum GPS ‘inclusion criteria’ (a set of rules used to determine which GPS data are able to be included in analyses). Care is needed...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-08-01
|
| Series: | BMC Research Notes |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13104-018-3681-2 |
| _version_ | 1818792264447557632 |
|---|---|
| author | Suzanne Mavoa Karen Lamb David O’Sullivan Karen Witten Melody Smith |
| author_facet | Suzanne Mavoa Karen Lamb David O’Sullivan Karen Witten Melody Smith |
| author_sort | Suzanne Mavoa |
| collection | DOAJ |
| description | Abstract Objective When using global positioning systems (GPS) to assess an individual’s exposure to their environment, a first step in data cleaning is to establish minimum GPS ‘inclusion criteria’ (a set of rules used to determine which GPS data are able to be included in analyses). Care is needed at this stage to avoid any data exclusion (data loss) systematically biasing results in terms of characteristics of the environment and participants. The extent of potential systematic bias in sample retention due to GPS data loss and application of GPS inclusion criteria is unknown. The aim of this study was to describe differences in sample size and socio-demographic characteristics of the retained sample when applying three different GPS inclusion criteria. The study assessed 7-day GPS data collected from children (aged 9–13 years) recruited from nine schools in Auckland, New Zealand as part of the Kids in the City study. Results Participants from ethnic minorities and those attending schools in lower socioeconomic areas were disproportionately excluded from the retained samples. This highlights potential equity implications in basing the assessment of exposure—which ultimately influences research results on the relationship between environment and health—on non-representative GPS data. |
| first_indexed | 2024-12-18T15:24:29Z |
| format | Article |
| id | doaj.art-72bf0470d397488fbdb8efe4f3248816 |
| institution | Directory Open Access Journal |
| issn | 1756-0500 |
| language | English |
| last_indexed | 2024-12-18T15:24:29Z |
| publishDate | 2018-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj.art-72bf0470d397488fbdb8efe4f32488162022-12-21T21:03:18ZengBMCBMC Research Notes1756-05002018-08-011111710.1186/s13104-018-3681-2Are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria?Suzanne Mavoa0Karen Lamb1David O’Sullivan2Karen Witten3Melody Smith4Non Communicable Disease Unit, Melbourne School of Population & Global Health, The University of MelbourneInstitute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin UniversityDepartment of Geography, University of California, BerkeleySHORE and Whariki Research Centre, School of Public Health, Massey UniversitySchool of Nursing, The University of AucklandAbstract Objective When using global positioning systems (GPS) to assess an individual’s exposure to their environment, a first step in data cleaning is to establish minimum GPS ‘inclusion criteria’ (a set of rules used to determine which GPS data are able to be included in analyses). Care is needed at this stage to avoid any data exclusion (data loss) systematically biasing results in terms of characteristics of the environment and participants. The extent of potential systematic bias in sample retention due to GPS data loss and application of GPS inclusion criteria is unknown. The aim of this study was to describe differences in sample size and socio-demographic characteristics of the retained sample when applying three different GPS inclusion criteria. The study assessed 7-day GPS data collected from children (aged 9–13 years) recruited from nine schools in Auckland, New Zealand as part of the Kids in the City study. Results Participants from ethnic minorities and those attending schools in lower socioeconomic areas were disproportionately excluded from the retained samples. This highlights potential equity implications in basing the assessment of exposure—which ultimately influences research results on the relationship between environment and health—on non-representative GPS data.http://link.springer.com/article/10.1186/s13104-018-3681-2GPSInclusion criteriaMissing dataBiasExposureChildren |
| spellingShingle | Suzanne Mavoa Karen Lamb David O’Sullivan Karen Witten Melody Smith Are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria? BMC Research Notes GPS Inclusion criteria Missing data Bias Exposure Children |
| title | Are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria? |
| title_full | Are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria? |
| title_fullStr | Are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria? |
| title_full_unstemmed | Are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria? |
| title_short | Are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria? |
| title_sort | are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria |
| topic | GPS Inclusion criteria Missing data Bias Exposure Children |
| url | http://link.springer.com/article/10.1186/s13104-018-3681-2 |
| work_keys_str_mv | AT suzannemavoa aredisadvantagedchildrenmorelikelytobeexcludedfromanalysiswhenapplyingglobalpositioningsystemsinclusioncriteria AT karenlamb aredisadvantagedchildrenmorelikelytobeexcludedfromanalysiswhenapplyingglobalpositioningsystemsinclusioncriteria AT davidosullivan aredisadvantagedchildrenmorelikelytobeexcludedfromanalysiswhenapplyingglobalpositioningsystemsinclusioncriteria AT karenwitten aredisadvantagedchildrenmorelikelytobeexcludedfromanalysiswhenapplyingglobalpositioningsystemsinclusioncriteria AT melodysmith aredisadvantagedchildrenmorelikelytobeexcludedfromanalysiswhenapplyingglobalpositioningsystemsinclusioncriteria |