Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

ACC 2024 Congress

Catch up on all the top stories and latest evidence in cardiology research with our ACC 2024 Congress hub page.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2018

Open Access 01-12-2018 | Research article

Self-selection in a population-based cohort study: impact on health service use and survival for bowel and lung cancer assessed using data linkage

Published in: BMC Medical Research Methodology | Issue 1/2018

Login to get access

Abstract

Background

In contrast to aetiological associations, there is little empirical evidence for generalising health service use associations from cohort studies. We compared the health service use of cohort study participants diagnosed with bowel or lung cancer to the source population of people diagnosed with these cancers in New South Wales (NSW), Australia to assess the representativeness of health service use of the cohort study participants.

Methods

Population-based cancer registry data for NSW residents aged ≥45 years at diagnosis of bowel or lung cancer were linked to the 45 and Up Study, a NSW population-based cohort study (N~ 267,000). We measured hospitalisation, emergency department (ED) attendance and all-cause survival, and risk factor associations with these outcomes using administrative data for cohort study participants and the source population. We assessed bias in prevalence and risk factor associations using ratios of relative frequency (RRF) and relative odds ratios (ROR), respectively.

Results

People from major cities, non-English speaking countries and with comorbidites were under-represented among cohort study participants diagnosed with bowel (n = 1837) or lung (n = 969) cancer by 20–50%. Cohort study participants had similar hospitalisation and ED attendance compared with the source population. One-year survival after major surgical resection was similar, but cohort study participants had up to 25% higher post-diagnosis survival (lung cancer 3-year survival: RRF = 1.24, 95% confidence interval 1.12,1.37). Except for area-based socioeconomic position, risk factors associations with health service use measures and survival appeared relatively unbiased.

Conclusions

Absolute measures of health service use and risk factor associations in a non-representative sample showed little evidence of bias. Non-comparability of risk factor measures of cohort study participants and non-participants, such as area-based socioeconomic position, may bias estimates of risk factor associations. Primary and outpatient care outcomes may be more vulnerable to bias.
Appendix
Available only for authorised users
Literature
1.
Browning CJ, Kendig H. Cohort profile: the Melbourne longitudinal studies on healthy ageing program. Int J Epidemiol. 2010;39:e1–7.CrossRefPubMed
2.
Huisman M, Poppelaars J, van der Horst M, Beekman AT, Brug J, van Tilburg TG, et al. Cohort profile: the longitudinal aging study Amsterdam. Int J Epidemiol. 2011;40:868–76.CrossRefPubMed
3.
Steptoe A, Breeze E, Banks J, Nazroo J. Cohort profile: the English longitudinal study of ageing. Int J Epidemiol. 2013;42:1640–8.CrossRefPubMed
4.
Martin S, Haren M, Taylor A, Middleton S, Wittert G. Members of the Florey Adelaide male ageing study. Cohort profile: the Florey Adelaide male ageing study (FAMAS). Int J Epidemiol. 2007;36:302–6.CrossRefPubMed
5.
Kearney PM, Cronin H, O'Regan C, Kamiya Y, Savva GM, Whelan B, et al. Cohort profile: the Irish longitudinal study on ageing. Int J Epidemiol. 2011;40:877–84.CrossRefPubMed
6.
Schooling C, Chan W, Leung S, Lam T, Lee S, Shen C, et al. Cohort profile: Hong Kong Department of Health elderly health service cohort. Int J Epidemiol. 2016;45:64–72.
7.
Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43:61–8.CrossRefPubMed
8.
Wong R, Michaels-Obregon A, Palloni A. Cohort profile: the Mexican health and aging study (MHAS). Int J Epidemiol. 2017;46:e2.CrossRefPubMed
9.
10.
Reijneveld SA, Stronks K. The impact of response bias on estimates of health care utilization in a metropolitan area: the use of administrative data. Int J Epidemiol. 1999;28:1134–40.CrossRefPubMed
11.
Lamers LM. Medical consumption of respondents and non-respondents to a mailed health survey. Eur J Pub Health. 1997;7:267–71.CrossRef
12.
Drivsholm T, Eplov LF, Davidsen M, Jørgensen T, Ibsen H, Hollnagel H, et al. Representativeness in population-based studies: a detailed description of non-response in a Danish cohort study. Scand J Public Health. 2006;34:623–31.CrossRefPubMed
13.
Osler M, Schroll M. Differences between participants and non-participants in a population study on nutrition and health in the elderly. Eur J Clin Nutr. 1992;46:289–95.PubMed
14.
Banks E, Redman S, Jorm L, Armstrong B, Bauman A, Beard J, et al. Cohort profile: the 45 and up study. Int J Epidemiol. 2008;37:941–7.CrossRefPubMed
15.
Australian Institute of Health and Welfare. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2011. Australian Burden of Disease Study series no. 3. BOD 4. Canberra: AIHW; 2016.
16.
Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, et al. The global burden of cancer 2013. JAMA Oncol. 2015;1:505–27.CrossRefPubMedCentral
17.
Farmer P, Frenk J, Knaul FM, Shulman LN, Alleyne G, Armstrong L, et al. Expansion of cancer care and control in countries of low and middle income: a call to action. Lancet. 2010;376:1186–93.
18.
Forrest LF, Adams J, Wareham H, Rubin G, White M. Socioeconomic inequalities in lung cancer treatment: systematic review and meta-analysis. PLoS Med. 2013;10:e1001376.CrossRefPubMedPubMedCentral
19.
Aarts MJ, Lemmens VEPP, Louwman MWJ, Kunst AE, Coebergh JWW. Socioeconomic status and changing inequalities in colorectal cancer? A review of the associations with risk, treatment and outcome. Eur J Cancer. 2010;46:2681–95.CrossRefPubMed
20.
Australian Bureau of Statistics. 4390.0 Private Hospitals, Australia 2011–12. Canberra: ABS; 2013.
21.
Australian Institute of Health and Welfare. Australian hospital statistics 2011–12. Health services series no. 50. Cat. no. HSE 134. Canberra: AIHW; 2013.
22.
Australian Institute of Health and Welfare. Cancer in Australia 2017. Cancer series no. 101. Cat. no. CAN 100. Canberra: AIHW; 2017.
23.
Goldsbury DE, O’Connell DL, Girgis A, Wilkinson A, Phillips JL, Davidson PM, et al. Acute hospital-baseColombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay Jd services used by adults during the last year of life in New South Wales, Australia: a population-based retrospective cohort study. BMC Health Serv Res. 2015;15:537.
24.
Australian Bureau of Statistics. 2039.0 - Information Paper: An Introduction to Socio-Economic Indexes for Areas (SEIFA), Australia, 2006. Canberra: ABS; 2008.
25.
Australian Bureau of Statistics. 1216.0.15.003 - Australian Standard Geographical Classification (ASGC) Remoteness Area Correspondences. Canberra: ABS; 2011.
26.
Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173:676–82.
27.
Nilsen RM, Vollset SE, Gjessing HK, Skjaerven R, Melve KK, Schreuder P, et al. Self-selection and bias in a large prospective pregnancy cohort in Norway. Paediatr Perinat Epidemiol. 2009;23:597–608.CrossRefPubMed
28.
Nohr EA, Frydenberg M, Henriksen TB, Olsen J. Does low participation in cohort studies induce bias? Epidemiology. 2006;17:413–8.CrossRefPubMed
29.
Bray F, Colombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay J, editors. Cancer Incidence in Five Continents, Vol. XI (electronic version). Lyon: International Agency for Research on Cancer; 2017. http://​ci5.​iarc.​fr. Accessed 3 Mar 2018.
30.
Creighton N, Perez D, Cotter T. Smoking-attributable cancer mortality in NSW, Australia, 1972–2008. Public Health Res Pract. 2015;25:e2531530.CrossRefPubMed
31.
Mealing NM, Banks E, Jorm LR, Steel DG, Clements MS, Rogers KD. Investigation of relative risk estimates from studies of the same population with contrasting response rates and designs. BMC Med Res Methodol. 2010;10:26.CrossRefPubMedPubMedCentral
32.
33.
34.
Gilthorpe MS, Wilson RC. Rural/urban differences in the association between deprivation and healthcare utilisation. Soc Sci Med. 2003;57:2055–63.CrossRefPubMed
35.
Hernan MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15:615–25.CrossRefPubMed
36.
Sørensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, et al. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health. 2012;12:80.CrossRefPubMedPubMedCentral
37.
Babitsch B, Gohl D, von Lengerke T. Re-revisiting Andersen's behavioral model of health services use: a systematic review of studies from 1998-2011. Psychosoc Med 2012;9:Doc11.
38.
Rothman K, Greenland S, Lash T. Validity in epidemiologic studies. In: Rothman K, Greenland S, Lash T, editors. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.
39.
Macleod U, Mitchell ED, Burgess C, Macdonald S, Ramirez AJ. Risk factors for delayed presentation and referral of symptomatic cancer: evidence for common cancers. Br J Cancer. 2009;101:S92–S101.CrossRefPubMedPubMedCentral
40.
Smith LK, Pope C, Botha JL. Patients' help-seeking experiences and delay in cancer presentation: a qualitative synthesis. Lancet. 2005;366:825–31.CrossRefPubMed
41.
Willett WC, Blot WJ, Colditz GA, Folsom AR, Henderson BE, Stampfer MJ. Merging and emerging cohorts: not worth the wait. Nature. 2007;445:257–8.CrossRefPubMed
42.
Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;352:i1981.CrossRefPubMed
43.
Gundgaard J, Ekholm O, Hansen EH, Rasmussen NK. The effect of non-response on estimates of health care utilisation: linking health surveys and registers. Eur J Pub Health. 2008;18:189–94.CrossRef
44.
Rinne ST, Wong ES, Lemon JM, Perkins M, Bryson CL, Liu CF. Survey nonresponders incurred higher medical utilization and lower medication adherence. Am J Manag Care. 2015;21:e1–8.PubMed
Metadata
Title
Self-selection in a population-based cohort study: impact on health service use and survival for bowel and lung cancer assessed using data linkage
Publication date
01-12-2018
Published in
BMC Medical Research Methodology / Issue 1/2018
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-018-0537-3

Other articles of this Issue 1/2018

BMC Medical Research Methodology 1/2018 Go to the issue

Research article

Norm values and psychometric properties of the brief symptom inventory-18 regarding individuals between the ages of 60 and 95

Research article

Systematic review of the psychometric properties of instruments to measure sexual desire

Research article

A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease

Research article

A comparison of metrics and performance characteristics of different search strategies for article retrieval for a systematic review of the global epidemiology of kidney and urinary diseases

Debate

Analysis of randomised trials with long-term follow-up

Research article

Comparison of nuisance parameters in pediatric versus adult randomized trials: a meta-epidemiologic empirical evaluation