Background: Case-control studies have been used extensively in determining the aetiology of rare diseases. However, case-control studies often suffer from participation bias in the control group, resulting in biased odds ratios that cause problems with interpretation. Participation bias can be hard to detect and is often ignored. Methods: Population data can be used in place of the possibly biased control group, to investigate whether participation bias may have affected the results in previous studies, or in place of controls in future studies. We demonstrate this approach by reanalysing and comparing the results of two case-control studies: Type 1 diabetes in Yorkshire children and stroke in Indian adults. Findings: Using population data to represent the control groups reduced the width of the confidence intervals given in the original studies and confirmed the findings for the two diabetes risk factors used; caesarean birth (odds ratio (OR) = 2.12 (1.53, 2.95) compared with 1.84 (1.09, 3.10)) and amniocentesis (OR = 3.38 (2.09, 5.47) compared with 3.85 (1.34, 11.04)). The three stroke risk factors investigated were found to have increased odds ratios when using population data; hypertension (OR = 5.645 (5.639, 5.650) compared with 3.807 (2.114, 6.856)), diabetes (OR = 12.212 (12.200, 12.224) compared with 3.473 (1.757, 6.866)) and smoking (OR = 5.701 (5.696, 5.707) compared with 2.242 (1.255, 4.005)). Interpretation: Participation bias can greatly affect the results of a study and cause some potential risk factors to be over-or underestimated. This approach allows previous studies to be investigated for participation bias and presents an alternative to a control group in future studies, while improving precision.
Cite this paper
Keeble, C. , Barber, S. , Baxter, P. , Parslow, R. and Law, G. (2014) Reducing Participation Bias in Case-Control Studies: Type 1 Diabetes in Children and Stroke in Adults. Open Journal of Epidemiology
, 129-134. doi: 10.4236/ojepi.2014.43018
 Keeble, C., Barber, S., Law, G.R. and Baxter, P.D. (2013) Participation Bias Assessment in Three High Impact Journals. Sage Open, 3. http://dx.doi.org/10.1177/2158244013511260
 Haapea, M., Miettunen, J., Veijola, J., Lauronen, E., Tanskanen, P. and Isohanni, M. (2007) Nonparticipation May Bias the Results of a Psychiatric Survey—An Analysis from the Survey Including Magnetic Resonance Imaging within the Northern Finland 1966 Birth Cohort. Social Psychiatry and Psychiatric Epidemiology, 42, 403-409. http://dx.doi.org/10.1007/s00127-007-0178-z
 Lopez, R., Frydenberg, M. and Baelum, V. (2008) Non-Participation and Adjustment for Bias in Casecontrol Studies of Periodontitis. European Journal of Oral Sciences, 116, 405-411. http://dx.doi.org/10.1111/j.1600-0722.2008.00567.x
 Tam, C.C., Higgins, C.D. and Rodrigues, L.C. (2011) Effect of Reminders on Mitigating Participation Bias in a Case-Control Study. BMC Medical Research Methodology, 11, 33. http://dx.doi.org/10.1186/1471-2288-11-33
 Mezei, G. and Kheifets, L. (2006) Selection Bias and Its Implications for Case-Control Studies: A Case Study of Magnetic Field Exposure and Childhood Leukaemia. International Journal of Epidemiology, 35, 397-406.
 Eckmann, C., Wasserman, M., Latif, F., Roberts, G. and Beriot-Mathiot, A. (2013) Increased Hospital Length of Stay Attributable to Clostridium Difficile Infection in Patients with Four Co-Morbidities: An Analysis of Hospital Episode Statistics in Four European Countries. European Journal of Health Economics, 14, 835-846. http://dx.doi.org/10.1007/s10198-013-0498-8
 Childs, T., Scowcroft, A. and Todd, S. (2013) Gender and Regional Differences in the Treatment for Hypertension: A Pharmacoepidemiological Analysis of the General Practice Research Database (GPRD) in the Context of Hypertension in Atrial Fibrillation (AF) Patients. Journal of Human Hypertension, 27, 648.
 Crossfield, S.S.R. and Clamp, S.E. (2013) Electronic Health Records Research in a Health Sector Environment with Multiple Provider Types. HEALTHINF 2013 Proceedings of the International Conference on Health Informatics.
 Sortso, C., Thysegen, L.C. and Bronnum-Hansen, H. (2011) Database on Danish Population-Based Registers for Public Health and Welfare Research. Scandinavian Journal of Public Health, 39, 17-19. http://dx.doi.org/10.1177/1403494811399171
 Ludvigsson, J.F., Otterblad-Olausson, P., Pettersson, B.U. and Ekbom, A. (2009) The Swedish Personal Identity Num- ber: Possibilities and Pitfalls in Healthcare and Medical Research. European Journal of Epidemiology, 24, 659-667. http://dx.doi.org/10.1007/s10654-009-9350-y
 McKinney, P.A., Parslow, R., Gurney, K., Law, G., Bodansky, H.J. and Williams, D.R.R. (1997) Antenatal Risk Factors for Childhood Diabetes Mellitus; A Case-Control Study of the Medical Record Data in Yorkshire, UK. Diabetologia, 40, 933-939.
 Sorganvi, V., Kulkarni, M.S., Kadeli, D. and Atherga, S. (2014) Risk Factors for Stroke: A Case Control Study. IJCRR, 6, 46-52.
 Birth Choice UK (2011) Graphs Of Historical Caesarean Section Rates. www.birthchoiceuk.com
 Cambridge Fetal Care (2013) Amniocentesis Test. www.fetalcare.co.uk
 Office of Population Censuses and Surveys (1995) Subnational Population Projections, Series PP3, No. 9, Table 5: 1993-Based Population Projections, 1993-2016: Sex and Quinary Age-Groups, p. 61.
 World Health Statistics 2012 (2012) Page 113. http://apps.who.int/iris/bitstream/10665/44844/1/9789241564441_eng.pdf?ua=1
 International Diabetes Federation, (2014) Diabetes: Facts and Figures. http://www.idf.org/worlddiabetesday/toolkit/gp/facts-figures
 World Bank, (2014) Smoking prevalence, females (% of Adults). http://data.worldbank.org/indicator/SH.PRV.SMOK.FE
 World Bank (2014) Smoking Prevalence, Males (% of Adults). http://data.worldbank.org/indicator/SH.PRV.SMOK.MA
 World Bank (2014) Population (Total). http://data.worldbank.org/indicator/SP.POP.TOTL
 Rightdiagnosis.com (2014) Statistics by Country for Stroke. http://www.rightdiagnosis.com/s/stroke/stats-country.htm.
 R Core Team (2012) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
 Berkson, J. (1946) Limitations of the Application of Fourfold Table Analysis to Hospital Data. Biometrics Bulletin, 2, 47-53. http://dx.doi.org/10.2307/3002000