Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses

Mahapatra P, Shibuya K, Lopez AD, et al. Civil registration systems and vital statistics: successes and missed opportunities. Lancet. 2007;370(9599):1653–63.

PubMed  Google Scholar 

Harron K, Dibben C, Boyd J, et al. Challenges in administrative data linkage for research. Big Data Soc. 2017;4(2):2053951717745678.

PubMed  PubMed Central  Google Scholar 

Hand DJ. Statistical challenges of administrative and transaction data. J R Stat Soc Ser A Stat Soc. 2018;181(3):555–605.

Google Scholar 

Porta M. A dictionary of epidemiology. Oxford: Oxford University Press; 2014.

Google Scholar 

Westreich D. Epidemiology by design: a causal approach to the health sciences. Oxford: Oxford University Press; 2019.

Google Scholar 

Harron KL, Doidge JC, Knight HE, et al. A guide to evaluating linkage quality for the analysis of linked data. Int J Epidemiol. 2017;46(5):1699–710.

PubMed  PubMed Central  Google Scholar 

Teixeira RA, Naghavi M, Guimarães MDC, et al. Quality of cause-of-death data in Brazil: Garbage codes among registered deaths in 2000 and 2015. Rev Bras Epidemiol. 2019;22.

Blakely T, Robson B, Atkinson J, et al. Unlocking the numerator-denominator bias. I: Adjustments ratios by ethnicity for 1991–94 mortality data. The New Zealand Census-Mortality Study. N Z Med J. 2002;114(1147):39.

Sayers A, Ben-Shlomo Y, Blom AW, et al. Probabilistic record linkage. Int J Epidemiol. 2016;45(3):954–64.

PubMed  Google Scholar 

Doidge JC, Harron KL. Reflections on modern methods: linkage error bias. Int J Epidemiol. 2019;48(6):2050–60.

PubMed  PubMed Central  Google Scholar 

Grath-Lone LM, Libuy N, Etoori D, et al. Ethnic bias in data linkage. Lancet Digit Health. 2021;3(6):e339.

PubMed  Google Scholar 

Bohensky M. Bias in data linkage studies. In: Harron K, Goldstein H, Dibben C, editors. Methodological developments in data linkage. London: Wiley; 2015. p. 63–82.

Google Scholar 

Gilbert R, Lafferty R, Hagger-Johnson G, et al. GUILD: GUidance for information about linking data sets†. J Public Health. 2018;40(1):191–8.

Google Scholar 

Benchimol EI, Smeeth L, Guttmann A, et al. The REporting of studies conducted using observational routinely-collected health data (RECORD) statement. PLOS Med. 2015;12(10):e1001885.

PubMed  PubMed Central  Google Scholar 

Nanayakkara C, Christen P, Ranbaduge T, et al. Evaluation measure for group-based record linkage. Int J Popul Data Sci. 2019;4(1):1127.

PubMed  PubMed Central  CAS  Google Scholar 

Ford T, Mansfield KL, Markham S, et al. The challenges and opportunities of mental health data sharing in the UK. Lancet Digit Health. 2021;3(6):e333–6.

PubMed  Google Scholar 

Harron K. Data linkage in medical research. BMJ Med. 2022;1(1): e000087.

Google Scholar 

Lash TL, Fox MP, MacLehose RF, et al. Good practices for quantitative bias analysis. Int J Epidemiol. 2014;43(6):1969–85.

PubMed  Google Scholar 

Barreto ML, Ichihara MY, Pescarini JM, et al. Cohort profile: the 100 Million Brazilian Cohort. Int J Epidemiol. 2022;51(2):e27–38.

PubMed  Google Scholar 

Nery JS, Ramond A, Pescarini JM, et al. Socioeconomic determinants of leprosy new case detection in the 100 Million Brazilian Cohort: a population-based linkage study. Lancet Glob Health. 2019;7(9):e1226–36.

PubMed  PubMed Central  Google Scholar 

Ali MS, Ichihara MY, Lopes LC, et al. Administrative data linkage in Brazil: potentials for health technology assessment. Front Pharmacol. 2019;10(984).

Paixao ES, Cardim LL, Falcao IR, et al. Cohort profile: Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS) Birth Cohort. Int J Epidemiol. 2020;50(1):37–8.

PubMed Central  Google Scholar 

Barreto ML, Ichihara MY, Almeida BA, et al. The Center for Data and Knowledge Integration for Health (CIDACS). Int J Popul Data Sci. 2019;4(2):04.

Google Scholar 

Barbosa GCG, Ali MS, Araujo B, et al. CIDACS-RL: a novel indexing search and scoring-based record linkage system for huge datasets with high accuracy and scalability. BMC Medical Inform Decis Mak. 2020;20(1):289.

Google Scholar 

de Brauw A, Gilligan DO, Hoddinott J, et al. The impact of Bolsa Família on Women’s decision-making power. World Dev. 2014;59:487–504.

Google Scholar 

Hunter W, Sugiyama NB. Transforming subjects into citizens: insights from Brazil’s Bolsa Família. Perspect Politics. 2014;12(4):829–45.

Google Scholar 

Pescarini JM, Williamson E, Nery JS, et al. Effect of a conditional cash transfer programme on leprosy treatment adherence and cure in patients from the nationwide 100 Million Brazilian Cohort: a quasi-experimental study. Lancet Infect Dis. 2020;20(5):618–27.

PubMed  PubMed Central  Google Scholar 

Pescarini JM, Williamson E, Ichihara MY, et al. Conditional Cash Transfer Program and Leprosy Incidence: Analysis of 12.9 Million Families From the 100 Million Brazilian Cohort. Am J Epidemiol. 2020.

Pescarini JM, Craig P, Allik M, et al. Evaluating the impact of the Bolsa Familia conditional cash transfer program on premature cardiovascular and all-cause mortality using the 100 million Brazilian cohort: a natural experiment study protocol. BMJ Open. 2020;10(11):e039658.

PubMed  PubMed Central  Google Scholar 

Ramos D, da Silva NB, Ichihara MY, et al. Conditional cash transfer program and child mortality: a cross-sectional analysis nested within the 100 Million Brazilian Cohort. PLOS Med. 2021;18(9):e1003509.

PubMed  PubMed Central  Google Scholar 

Malta DC, Stopa SR, Pereira CA, et al. Cobertura de Planos de Saúde na população brasileira, segundo a Pesquisa Nacional de Saúde, 2013. Cien Saude Colet. 2017;22:179–90.

PubMed  Google Scholar 

Castro MC, Massuda A, Almeida G, et al. Brazil’s unified health system: the first 30 years and prospects for the future. Lancet. 2019;394(10195):345–56.

PubMed  Google Scholar 

Costa LFL, de Mesquita Silva Montenegro M, Rabello Neto DdL, et al. Estimating completeness of national and subnational death reporting in Brazil: application of record linkage methods. Popul Health Metr. 2020;18(1):22.

Morgan SL, Winship C. Counterfactuals and Causal inference: methods and principles for social research. 2nd ed. New York: Cambridge University Press; 2014.

Google Scholar 

Munafò MR, Tilling K, Taylor AE, et al. Collider scope: when selection bias can substantially influence observed associations. Int J Epidemiol. 2018;47(1):226–35.

PubMed  Google Scholar 

Hernán MA, Cole SR. Invited Commentary: causal diagrams and measurement bias. Am J Epidemiol. 2009;170(8):959–62.

PubMed  PubMed Central  Google Scholar 

de Oliveira GL, Chagas ALS. Effects of a cash transfer programme on origin–destination migration flows. Reg Sci Policy Pract. 2020;12(1):83–104.

Google Scholar 

Almeida D, Gorender D, Ichihara MY, et al. Examining the quality of record linkage process using nationwide Brazilian administrative databases to build a large birth cohort. BMC Med Inform Decis Mak. 2020;20(1):173.

PubMed  PubMed Central  Google Scholar 

Hagger-Johnson G, Harron K, Gonzalez-Izquierdo A, et al. Identifying possible false matches in anonymized hospital administrative data without patient identifiers. Health Serv Res. 2015;50(4):1162–78.

PubMed  Google Scholar 

Rodrigues NCP, Daumas RP, Almeida ASd, et al. Risk factors for the ill-defined causes of death in the Brazilian states: a multilevel analysis. Cien Saude Colet. 2018;23:3979–88.

Ha S, Hu H, Mao L, et al. Potential selection bias associated with using geocoded birth records for epidemiologic research. Ann Epidemiol. 2016;26(3):204–11.

PubMed  PubMed Central  Google Scholar 

Luft J, Ingham H. The Johari Window: a graphic model of awareness in interpersonal relations. Hum Relations Train News. 1961;5(9):6–7.

Google Scholar 

Allison PD. Missing data. Thousand Oaks: Sage; 2002.

Google Scholar 

Perkins NJ, Cole SR, Harel O, et al. Principled approaches to missing data in epidemiologic studies. Am J Epidemiol. 2018;187(3):568–75.

PubMed  Google Scholar 

Harron K, Doidge JC, Goldstein H. Assessing data linkage quality in cohort studies. Ann Hum Biol. 2020;47(2):218–26.

PubMed  PubMed Central  Google Scholar 

Vasileiou E, Simpson CR, Shi T, et al. Interim findings from first-dose mass COVID-19 vaccination roll-out and COVID-19 hospital admissions in Scotland: a national prospective cohort study. Lancet. 2021;397(10285):1646–57.

PubMed  PubMed Central  CAS  Google Scholar 

Gray L, Gorman E, White IR, et al. Correcting for non-participation bias in health surveys using record-linkage, synthetic observations and pattern mixture modelling. Stat Methods Med Res. 2020;29(4):1212–26.

PubMed  Google Scholar 

Cha AE, Cohen RA. Demographic variation in Health Insurance Coverage: United States, 2020. National Health Statistics Reports 1922;169(2022). doi:https://doi.org/10.15620/cdc:113097.

Mulholland RH, Vasileiou E, Simpson CR, et al. Cohort profile: early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II) database. Int J Epidemiol. 2021;50(4):1064–74.

PubMed  Google Scholar 

França E, Ishitani LH, Teixeira R, et al. Changes in the quality of cause-of-death statistics in Brazil: garbage codes among registered deaths in 1996–2016. Popul Health Metr. 2020;18(1):20.

PubMed  PubMed Central  Google Scholar 

Clark D, Dibben C. A guide to CHI-UPRN Residential Linkage (CURL) File Edinburgh, United Kingdom: ADR Scotland; 2020.

Bhopal R, Fischbacher C, Povey C, et al. Cohort profile: Scottish Health and Ethnicity Linkage Study of 4.65 million people exploring ethnic variations in disease in Scotland. Int J Epidemiol 2011;40(5):1168–75.

O’Reilly D, Rosato M, Connolly S. Unlinked vital events in census-based longitudinal studies can bias subsequent analysis. J Clin Epidemiol. 2008;61(4):380–5.

PubMed  Google Scholar 

Lawlor DA, Tilling K, Davey SG. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45(6):1866–86.

PubMed  Google Scholar 

Shaw RJ, Mackay D, Pell JP, et al. The relationship between antihypertensive medications and mood disorders: analysis of linked healthcare data for 1.8 million patients. Psychol Med. 2021;51(7):1183–91.

Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615–25.

PubMed  Google Scholar 

Greenland S. For and against methodologies: some perspectives on recent causal and statistical inference debates. Eur J Epidemiol. 2017;32(1):3–20.

PubMed  Google Scholar 

Pirracchio R, Carone M, Rigon MR, et al. Propensity score estimators for the average treatment effect and the average treatment effect on the treated may yield very different estimates. Stat Methods Med Res. 2013;25(5):1938–54.

PubMed  Google Scholar 

Jones KH, Laurie G, Stevens L, et al. The other side of the coin: Harm due to the non-use of health-related data. Int J Med Inf. 2017;97:43–51.

Google Scholar 

Routen A, Akbari A, Banerjee A, et al. Strategies to record and use ethnicity information in routine health data. Nat Med. 2022;28(7):1338–42.

PubMed  CAS  Google Scholar 

留言 (0)

沒有登入
gif