期刊论文详细信息
BMC Research Notes
Inclusion of mobile telephone numbers into an ongoing population health survey in New South Wales, Australia, using an overlapping dual-frame design: impact on the time series
David G Steel1  Raymond A Ferguson2  Margo L Barr1 
[1] National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, Australia;Centre for Epidemiology and Evidence, NSW Ministry of Health, 73 Miller Street, North Sydney, Australia
关键词: Time series;    Overlapping dual-frame;    Sample survey;    Telephone health survey;   
Others  :  1130558
DOI  :  10.1186/1756-0500-7-517
 received in 2014-05-18, accepted in 2014-08-04,  发布年份 2014
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【 摘 要 】

Background

Since 1997, the NSW Population Health Survey (NSWPHS) had selected the sample using random digit dialing of landline telephone numbers. When the survey began coverage of the population by landline phone frames was high (96%). As landline coverage in Australia has declined and continues to do so, in 2012, a sample of mobile telephone numbers was added to the survey using an overlapping dual-frame design. Details of the methodology are published elsewhere. This paper discusses the impacts of the sampling frame change on the time series, and provides possible approaches to handling these impacts.

Methods

Prevalence estimates were calculated for type of phone-use, and a range of health indicators. Prevalence ratios (PR) for each of the health indicators were also calculated using Poisson regression analysis with robust variance estimation by type of phone-use. Health estimates for 2012 were compared to 2011. The full time series was examined for selected health indicators.

Results

It was estimated from the 2012 NSWPHS that 20.0% of the NSW population were mobile-only phone users. Looking at the full time series for overweight or obese and current smoking if the NSWPHS had continued to be undertaken only using a landline frame, overweight or obese would have been shown to continue to increase and current smoking would have been shown to continue to decrease. However, with the introduction of the overlapping dual-frame design in 2012, overweight or obese increased until 2011 and then decreased in 2012, and current smoking decreased until 2011, and then increased in 2012. Our examination of these time series showed that the changes were a consequence of the sampling frame change and were not real changes. Both the backcasting method and the minimal coverage method could adequately adjust for the design change and allow for the continuation of the time series.

Conclusions

The inclusion of the mobile telephone numbers, through an overlapping dual-frame design, did impact on the time series for some of the health indicators collected through the NSWPHS, but only in that it corrected the estimates that were being calculated from a sample frame that was progressively covering less of the population.

【 授权许可】

   
2014 Barr et al.; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]NSW Ministry of Health, NSW Population Health Surveys. http://www.health.nsw.gov.au/surveys/Pages/default.aspx webcite
  • [2]Australian Bureau of Statistics: Population Survey Monitor. Catalogue no 4103.0. Canberra: ABS; 1996.
  • [3]Australian Communications and Media Authority (ACMA): Communications report 2010–11. Melbourne: ACMA; 2011. http://www.acma.gov.au/webwr/_assets/main/lib410148/communications_report_2010-11.pdf webcite
  • [4]Kuusela V, Callegaro M, Vehovar V: The influence of mobile telephones on telephone surveys. In Advances in Telephone Survey Methodology. Edited by Lepkowski J, Tucker C, Brick M, De Leeuw E, Japec L, Lavrakas PJ, Link M, Sangste R. Hoboken, NJ: Wiley; 2007:87-112.
  • [5]Lee S, Brick JM, Brown ER, Grant D: Growing cell-home population and non-coverage bias in traditional random digit dial telephone health surveys. Health Serv Res 2010, 45(4):1121-1139.
  • [6]Lynn P, Kaminska O: The impact of mobile phones on survey measurement error. Institute for Social and Economic Research Working Paper Series No 2011-07. Essex: University of Essex; 2011. https://www.iser.essex.ac.uk/publications/working-papers/iser/2011-07.pdf webcite
  • [7]National Health Interview Survey. http://www.cdc.gov/nchs/nhis.htm webcite
  • [8]Blumberg SJ, Luke JV: Wireless substitution: Estimates from the National Health Interview Survey. January - June 2012. National Centre for Health Statistics; 2012. http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless201212.PDF webcite
  • [9]Barr ML: Predicting when declining landline frame coverage will impact on the overall health estimates for the NSW Population Health Survey. Sydney: Centre for Epidemiology and Research, NSW Department of Health; 2008. http://www.health.nsw.gov.au/surveys/other/Documents/predicting-when-mobile-only-impacts-2008.pdf webcite
  • [10]Pennay D, Bishop N: Profiling the ‘mobile phone only’ population: A study of Australians with a mobile phone and no landline telephone. Melbourne: The Social Research Centre Pty Ltd; 2009.
  • [11]Barr ML, van Ritten JJ, Steel DG, Thackway SV: Inclusion of mobile phone numbers into an ongoing population health survey in New South Wales, Australia: design, methods, call outcomes, costs and sample representativeness. BMC Med Res Methodology 2012, 12:177.
  • [12]Barr ML, Ferguson RA, Hughes PJ, Steel DG: Inclusion of mobile phone numbers into an ongoing population health survey in New South Wales, Australia: final weighting strategy. University of Wollongong Working Paper 2014. http://niasra.uow.edu.au/content/groups/public/@web/@inf/@math/documents/doc/uow167062.pdf webcite
  • [13]SAS Institute: The SAS System for Windows version 9.2. Cary, NC: SAS Institute Inc; 2009. Further information available from http://www.sas.com webcite
  • [14]Zou G: A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol 2004, 159:702-706.
  • [15]Lee J, Tan CS, Chia KS: A practical guide for multivariate analysis of dichotomous outcomes. Ann Acad Med Singapore 2009, 38:714-719.
  • [16]Cole SR: Analysis of complex survey data using SAS Computer Methods Programs. Biomed 2001, 64:65-69.
  • [17]Altman DG, Bland JM: Interaction revisited: the differences between two estimates. BMJ 2003, 326:219.
  • [18]Van den Brakel JA, Smith PA, Compton S: Quality procedures for survey transitions – experiments, time series and discontinuities. Surv Res Methods 2008, 2(3):123-141.
  • [19]Australian Communications and Media Authority (ACMA): Communications report 2011–12. Melbourne: ACMA; 2013. http://www.acma.gov.au/webwr/_assets/main/lib550049/comms_report_2011-12.pdf webcite
  • [20]Pennay D: Profiling the ‘mobile phone only’ population: Results from a dual- frame telephone survey using a landline and mobile phone sample frame, ASCPRI Social Science Methodology conference proceedings. ASCPRI 2010, 2010:2010.
  • [21]Livingston M, Dietze P, Ferris J, Pennay D, Hayes L, Lenton S: Surveying alcohol and other drug use through telephone sampling: a comparison of landline and mobile phone samples. BMC Med Res Methodol 2013, 13:41.
  • [22]Mohorko A, de Leeuw E, Hox J: Coverage bias in European Telephone Surveys: Development of landline and mobile phone coverage across countries and over time. Survey Methods. Insights from the Field 2013. Retrieved from http://surveyinsights.org/?p=828 webcite
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