Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
권호 표지
DOI QR코드

DOI QR Code

Correlates of Digit Bias in Self-reporting of Cigarette per Day (CPD) Frequency: Results from Global Adult Tobacco Survey (GATS), India and its Implications

  • Jena, Pratap Kumar (Research, Public Health Foundation of India) ;
  • Kishore, Jugal (Department of Community Medicine, Maulana Azad Medical College) ;
  • Jahnavi, G. (Department of Community Medicine, Madha Medical College or Research Institute)
  • Published : 2013.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Background: Cigarette per day (CPD) use is a key smoking behaviour indicator. It reflects smoking intensity which is directly proportional to the occurrence of tobacco induced cancers. Self reported CPD assessment in surveys may suffer from digit bias and under reporting. Estimates from such surveys could influence the policy decision for tobacco control efforts. In this context, this study aimed at identifying underlying factors of digit bias and its implications for Global Adult Tobacco Surveillance. Materials or Methods: Daily manufactured cigarette users CPD frequencies from Global Adult Tobacco Survey (GATS) - India data were analyzed. Adapted Whipple Index was estimated to assess digit bias and data quality of reported CPD frequency. Digit bias was quantified by considering reporting of '0' or '5' as the terminal digits in the CPD frequency. The factors influencing it were identified by bivariate and logistic regression analysis. Results: The mean and mode of CPD frequency was 6.7 and 10 respectively. Around 14.5%, 15.1% and 15.2% of daily smokers had reported their CPD frequency as 2, 5 and 10 respectively. Modified Whipple index was estimated to be 226.3 indicating poor data quality. Digit bias was observed in 38% of the daily smokers. Heavy smoking, urban residence, North, South, North- East region of India, less than primary, secondary or higher educated and fourth asset index quintile group were significantly associated with digit bias. Discussion: The present study highlighted poor quality of CPD frequency data in the GATS-India survey and need for its improvement. Modeling of digit preference and smoothing of the CPD frequency data is required to improve quality of data. Marketing of 10 cigarette sticks per pack may influence CPD frequency reporting, but this needs further examination. Exploring alternative methods to reduce digit bias in cross sectional surveys should be given priority.

Keywords

References

  1. Balhara YP, Jain R (2013). A receiver operated curve-based evaluation of change in sensitivity and specificity of cotinine urinalysis for detecting active tobacco use. J Cancer Res Ther, 9, 84-9. https://doi.org/10.4103/0973-1482.110384
  2. Berkman ET, Dickenson J, Falk EB, et al (2011). Using SMS text messaging to assess moderators of smoking reduction: validating a new tool for ecological measurement of health behaviors. Health Psychol, 30, 186-94. https://doi.org/10.1037/a0022201
  3. Caraballo RS, Giovino GA, Pechacek TF, et al (2001). Factors associated with discrepancies between self-reports on cigarette smoking and measured serum cotinine levels among persons aged 17 years or older: Third National Health and Nutrition Examination Survey, 1988-1994. Am J Epidemiol, 153, 807-14. https://doi.org/10.1093/aje/153.8.807
  4. Caraballo RS, Giovino GA, Pechacek TF (2004). Self-reported cigarette smoking vs. serum cotinine among US adolescents. Nicotine Tob Res, 6, 19-25. https://doi.org/10.1080/14622200310001656821
  5. Carlo GC, Paul HCE, Jutta G (2008). Modelling general patterns of digit preference. Statistical Modelling, 8, 385-401 https://doi.org/10.1177/1471082X0800800404
  6. Centers for Disease Control and Prevention (CDC). Global Tobacco Surveillance System data (GTSSData). http://apps.nccd.cdc.gov/gtssdata/Ancillary/DataReports.aspx?CAID=2 (accessed on Nov 20, 2012).
  7. Chaiton MO, Cohen JE, McDonald WP, et al (2007). The Heaviness Of Smoking Index as a predictor of smoking cessation in Canada. Addict Behav, 32, 1031-42. https://doi.org/10.1016/j.addbeh.2006.07.008
  8. Crocketta GRM, Crocketts AC, Turner SJ (2001). 'Basenumber correlation': a new technique for investigating digit preference and data heaping. Int J Humanities and Arts Computing, 13, 161-79.
  9. Dawe S, Loxton NJ, Hides L, Kavanagh DJ, Mattick RP (2002). Review of diagnostic screening instruments for alcohol and other drug use and other psychiatric disorders (2nd Ed). Australia: Commonwealth Department of Health and Ageing.
  10. Denic S, Khatib F, Saadi H (2004). Quality of age data in patients from developing countries. J Public Health (Oxf), 26, 168-71. https://doi.org/10.1093/pubmed/fdh131
  11. Dhavan P, Bassi S, Stigler MH, et al (2011). Using salivary cotinine to validate self-reports of tobacco use by Indian youth living in low-income neighborhoods. Asian Pac J Cancer Prev, 12, 2551-4.
  12. Directorate General of Health Services (DGHS) (2011). Tobacco Dependence Treatment Guidelines. New Delhi: DGHS, Ministry of Health or Family Welfare, Government of India.
  13. Fagerstrom K (2003). Time to first cigarette; the best single indicator of tobacco dependence? Monaldi Arch Chest Dis, 59, 91-4
  14. Fendrich M, Mackesy-Amiti ME, Johnson TP, Hubbbel A, Wislar JS (2005). Tobacco-reporting validity in an epidemiological drug-use survey. Addict Behav, 30, 175-81. https://doi.org/10.1016/j.addbeh.2004.04.009
  15. Giovino GA, Mirza SA, Samet JM et al (2012). Tobacco use in 3 billion individuals from 16 countries: an analysis of nationally representative cross-sectional household surveys. Lancet, 380, 668-79. https://doi.org/10.1016/S0140-6736(12)61085-X
  16. Global Adult Tobacco Survey Collaborative Group (2011). Tobacco Questions for Surveys: A Subset of Key questions from the Global Adult Tobacco Survey-GATS (2nd Ed). Atalanta, GA: Centers for Disease Control and Prevention.
  17. Hammersley R (1994). A digest of memory phenomena for addiction research. Addiction, 89, 283-93. https://doi.org/10.1111/j.1360-0443.1994.tb00890.x
  18. Harris KJ, Golbeck AL, Cronk NJ, et al (2009). Timeline follow-back versus global self-reports of tobacco smoking: A comparison of findings with non-daily smokers. Psychol Addict Behav, 23, 368-72. https://doi.org/10.1037/a0015270
  19. Hatziandreu EJ, Pierce JP, Fiore MC, et al (1989). The reliability of self-reported cigarette consumption in the United States. Am J Public Hlth, 79, 1020-23. https://doi.org/10.2105/AJPH.79.8.1020
  20. Heatherton TF, Kozlowski LT, Frecker RC, et al (1991). The Fagerstrom test for nicotine dependence: A revision of the Fagerstrom tolerance questionnaire. Br J Addict, 86, 1119-27. https://doi.org/10.1111/j.1360-0443.1991.tb01879.x
  21. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans (2004). Tobacco smoke and involuntary smoking (IARC Monograph 83). Lyon, France: IARC, World Health Organization.
  22. IARC Handbooks of Cancer Prevention (2008). Tobacco Control Vol. 12: Methods for Evaluating Tobacco Control Policies. Lyon, France: IARC, World Health Organization.
  23. International Institute for Population Studies (IIPS) (2010). Global Adult Tobacco Survey (GATS)-India; 2009-10. New Delhi: Ministry of Health or Family Welfare, Government of India.
  24. Jena PK, Kishore J, Bandyopadhyay C (2012). Prevalence and patterns of tobacco use in Asia. Lancet, 380, 1906.
  25. Klesges RC, Debon M, Ray JW (1995). Are self-reports of smoking rate biased? Evidence from the second national health and nutrition examination survey. J Clin Epidemiol, 48, 1225-33. https://doi.org/10.1016/0895-4356(95)00020-5
  26. Latkin CA, Murray LI, Clegg Smith K, et al (2013). The prevalence and correlates of single cigarette selling among urban disadvantaged drug users in Baltimore, Maryland. Drug Alcohol Depend, 10, 007.
  27. Law MR, Morris JK, Watt HC, Wald NJ (1997). The doseresponse relationship between cigarette consumption, biochemical markers and risk of lung cancer. Br J Cancer, 75, 1690-93. https://doi.org/10.1038/bjc.1997.287
  28. Means B, Habina K, Swan GE, et al (1992). Cognitive research on response error in survey questions on smoking. Meryland, USA: National Center for Health Statistics, United States Department of Health and Human Services.
  29. Pardeshi GS (2010). Age heaping and accuracy of age data collected during a community survey in the Yavatmal District, Maharashtra. Indian J Community Med, 35, 391-5. https://doi.org/10.4103/0970-0218.69256
  30. Pcchucek TF, Fox BH, Murray DM et al (1984). Review of techniques for measurement of smoking behavior. In: Matarazzo JD, Weiss SM, Herd JA, Miller NE, eds. Behavioral health: A handbook of health enhancement and disease prevention. New York: Wiley.
  31. Perkins KA, Jao NC, Karelitz JL (2012). Consistency of daily cigarette smoking amount in dependent adults. Psychol Addict Behav, 10, 30287.
  32. Shiffman S (2009). Commentary on Herd or Borland (2009) and Herd et al. (2009): Illuminating the course and dynamics of smoking cessation. Addiction, 104, 2100-01. https://doi.org/10.1111/j.1360-0443.2009.02799.x
  33. Shiffman S (2009). How many cigarettes did you smoke? Assessing cigarette consumption by global report, time-line follow-back, and ecological momentary assessment. Hlth Psychol, 28, 519-26. https://doi.org/10.1037/a0015197
  34. US Department of Health and Human Services (2008). Clinical Practice Guideline - Treating Tobacco Use and Dependence: 2008 Update. Rockville, MD: US Dept of Health and Human Services, Public Health Service.
  35. Wang Z, Zeng Y, Jeune B, et al (1998). Age validation of Han Chinese centenarians. Genus, 54, 123-41.
  36. Warner KE (1978). Possible increases in the underreporting of cigarette consumption. J Am Stat Assn, 73, 314-18. https://doi.org/10.1080/01621459.1978.10481575
  37. West R, McNeill A, Raw M (2000). Smoking cessation guidelines for health professionals: an update. Thorax, 55, 987-99. https://doi.org/10.1136/thorax.55.12.987

Cited by

  1. Electronic Nicotine Delivery Systems (“E-cigarettes”) vol.151, pp.3, 2014, https://doi.org/10.1177/0194599814536847
  2. Using 2 Assessment Methods May Better Describe Dietary Supplement Intakes in the United States vol.145, pp.7, 2015, https://doi.org/10.3945/jn.115.211466