Estimation of vaccination coverage from electronic healthcare records; methods performance evaluation – A contribution of the ADVANCE-project

English version

Autoři: Toon Braeye ^aff001; Vincent Bauchau ^aff003; Miriam Sturkenboom ^aff004; Hanne-Dorthe Emborg ^aff007; Ana Llorente García ^aff008; Consuelo Huerta ^aff008; Elisa Martin Merino ^aff008; Kaatje Bollaerts ^aff004
Působiště autorů: Sciensano, Brussels, Belgium ^aff001; Hasselt University, Hasselt, Belgium ^aff002; GSK Vaccines, Wavre, Belgium ^aff003; P95 Epidemiology and Pharmacovigilance, Leuven, Belgium ^aff004; VACCINE.GRID foundation, Basel, Switzerland ^aff005; University Medical Center Utrecht, Julius Global Health, Utrecht, the Netherlands ^aff006; Statens Serum Institut, Copenhagen, Denmark ^aff007; BIFAP database, Spanish Agency of Medicines and Medical Devices, Madrid, Spain ^aff008
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222296

Souhrn

Introduction

The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines, using existing electronic healthcare record (eHR) databases in Europe.

Part of the data in such sources is missing due to incomplete follow-up hampering the accurate estimation of vaccination coverage. We compared different methods for coverage estimation from eHR databases; naïve period prevalence, complete case period prevalence, period prevalence adjusted for follow-up time, Kaplan-Meier (KM) analysis and (adjusted) inverse probability weighing (IPW).

Methods

We created simulation scenarios with different proportions of completeness of follow-up. Both completeness independent and dependent from vaccination date and status were considered. The root mean squared error (RMSE) and relative difference between the estimated and true coverage were used to assess the performance of the different methods for each of the scenarios. We included data examples on the vaccination coverage of human papilloma virus and pertussis component containing vaccines from the Spanish BIFAP database.

Results

Under completeness independent from vaccination date or status, several methods provided estimates with bias close to zero. However, when dependence between completeness of follow-up and vaccination date or status was present, all methods generated biased estimates. The IPW/CDF methods were generally the least biased. Preference for a specific method should be based on the type of censoring and type of dependence between completeness of follow-up and vaccination. Additional insights into these aspects, might be gained by applying several methods.

Klíčová slova:

Biology and life sciences – Organisms – Eukaryota – Physical sciences – Research and analysis methods – Database and informatics methods – Animals – People and places – Mathematics – Probability theory – Simulation and modeling – Geographical locations – Europe – Medicine and health sciences – Microbiology – Medical microbiology – Microbial pathogens – Pathology and laboratory medicine – Pathogens – Vertebrates – Amniotes – Mammals – Infectious diseases – Viral pathogens – Viruses – Immunology – Vaccination and immunization – Public and occupational health – Preventive medicine – Infectious disease control – Vaccines – DNA viruses – Papillomaviruses – Human papillomavirus – Probability distribution – Primates – Apes

Zdroje

1. Haverkate M, D’Ancona F, Johansen K, van der Velden K, Giesecke J, Lopalco PL. Assessing vaccination coverage in the European Union: is it still a challenge? Expert Rev Vaccines. 2011;10: 1195–1205. doi: 10.1586/erv.11.87 21854312

2. Clark A, Sanderson C. Timing of children’s vaccinations in 45 low-income and middle-income countries: an analysis of survey data. The Lancet. 2009;373: 1543–1549. doi: 10.1016/S0140-6736(09)60317-2

3. Akmatov MK, Kretzschmar M, Krämer A, Mikolajczyk RT. Timeliness of vaccination and its effects on fraction of vaccinated population. Vaccine. 2008;26: 3805–3811. doi: 10.1016/j.vaccine.2008.05.031 18565626

4. Derrough T, Olsson K, Gianfredi V, Simondon F, Heijbel H, Danielsson N, et al. Immunisation Information Systems—useful tools for monitoring vaccination programmes in EU/EEA countries, 2016. Euro Surveill Bull Eur Sur Mal Transm Eur Commun Dis Bull. 2017;22. doi: 10.2807/1560-7917.ES.2017.22.17.30519 28488999

5. Force CPST. Recommendation for Use of Immunization Information Systems to Increase Vaccination Rates. J Public Health Manag Pract. 2015;21: 249. doi: 10.1097/PHH.0000000000000092 24912083

6. Miles M, Ryman TK, Dietz V, Zell E, Luman ET. Validity of vaccination cards and parental recall to estimate vaccination coverage: a systematic review of the literature. Vaccine. 2013;31: 1560–1568. doi: 10.1016/j.vaccine.2012.10.089 23196207

7. Bolton P, Holt E, Ross A, Hughart N, Guyer B. Estimating vaccination coverage using parental recall, vaccination cards, and medical records. Public Health Rep. 1998;113: 521–526. 9847923

8. Kalies H, Redel R, Varga R, Tauscher M, von Kries R. Vaccination coverage in children can be estimated from health insurance data. BMC Public Health. 2008;8: 82. doi: 10.1186/1471-2458-8-82 18312683

9. Fonteneau L, Ragot M, Guthmann J-P, Lévy-Bruhl D. Use of health care reimbursement data to estimate vaccination coverage in France: Example of hepatitis B, meningitis C, and human papillomavirus vaccination. Rev DÉpidémiologie Santé Publique. 2015;63: 293–298. doi: 10.1016/j.respe.2015.06.005 26386634

10. Baker MA, Nguyen M, Cole DV, Lee GM, Lieu TA. Post-licensure rapid immunization safety monitoring program (PRISM) data characterization. Vaccine. 2013;31 Suppl 10: K98–112. doi: 10.1016/j.vaccine.2013.04.088 24331080

11. Keny A, Biondich P, Grannis S, Were M. Adequacy and Quality of Immunization Data in a Comprehensive Electronic Health Record System. J Health Inform Afr. 2013;8: 254–722. doi: 10.12856/JHIA-2013-v1-i1-40

12. Li L, Shen C, Li X, Robins JM. On weighting approaches for missing data. Stat Methods Med Res. 2013;22: 14–30. doi: 10.1177/0962280211403597 21705435

13. Molenberghs G, Verbeke G. Models for Discrete Longitudinal Data. Springer; 2005.

14. Seaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res. 2013;22: 278–295. doi: 10.1177/0962280210395740 21220355

15. Laubereau B, Hermann M, Schmitt HJ, Weil J, Kries RV. Detection of delayed vaccinations: a new approach to visualize vaccine uptake. Epidemiol Amp Infect. 2002;128: 185–192. doi: 10.1017/S0950268801006550 12002536

16. R Development Core Team R. A language and environment for statistical computing. Computing. 2006;1. doi: 10.1890/0012-9658(2002)083[3097:CFHIWS]2.0.CO;2

17. Stein-Zamir C, Israeli A. Age-appropriate versus up-to-date coverage of routine childhood vaccinations among young children in Israel. Hum Vaccines Immunother. 2017;13: 2102–2110. doi: 10.1080/21645515.2017.1341028 28696824

18. Salvador Rosa A, Moreno Pérez JC, Sonego D, García Rodríguez LA, de Abajo Iglesias FJ. [The BIFAP project: database for pharmaco-epidemiological research in primary care]. Aten Primaria. 2002;30: 655–661. doi: 10.1016/s0212-6567(02)79129-4 12525343

19. Martín‐Merino E, Llorente‐García A, Montero‐Corominas D, Huerta C. The recording of human papillomavirus (HPV) vaccination in BIFAP primary care database: A validation study. Pharmacoepidemiol Drug Saf. 2019;28: 201–208. doi: 10.1002/pds.4674 30488510

20. López N, Torné A, Franco A, San-Martin M, Viayna E, Barrull C, et al. Epidemiologic and economic burden of HPV diseases in Spain: implication of additional 5 types from the 9-valent vaccine. Infect Agent Cancer. 2018;13. doi: 10.1186/s13027-018-0187-4 29743937

21. Crespo I, Cardeñosa N, Godoy P, Carmona G, Sala MR, Barrabeig I, et al. Epidemiology of pertussis in a country with high vaccination coverage. Vaccine. 2011;29: 4244–4248. doi: 10.1016/j.vaccine.2011.03.065 21496465

22. Ministerio de sanidad, consumo y bienestar social. Coberturas de vacunación [Internet]. Available: http://www.mscbs.gob.es/profesionales/saludPublica/prevPromocion/vacunaciones/HistoricoCoberturas.htm

23. Fadnes LT, Nankabirwa V, Sommerfelt H, Tylleskär T, Tumwine JK, Engebretsen IMS. Is vaccination coverage a good indicator of age-appropriate vaccination? A prospective study from Uganda. Vaccine. 2011;29: 3564–3570. doi: 10.1016/j.vaccine.2011.02.093 21402043

24. Lernout T, Theeten H, Hens N, Braeckman T, Roelants M, Hoppenbrouwers K, et al. Timeliness of infant vaccination and factors related with delay in Flanders, Belgium. Vaccine. 2014;32: 284–289. doi: 10.1016/j.vaccine.2013.10.084 24252698

25. Geskus RB. Cause-Specific Cumulative Incidence Estimation and the Fine and Gray Model Under Both Left Truncation and Right Censoring. Biometrics. 2011;67: 39–49. doi: 10.1111/j.1541-0420.2010.01420.x 20377575

26. Turnbull BW. Nonparametric Estimation of a Survivorship Function with Doubly Censored Data. J Am Stat Assoc. 1974;69: 169–173. doi: 10.2307/2285518

27. Bang H, Robins JM. Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics. 2005;61: 962–973. doi: 10.1111/j.1541-0420.2005.00377.x 16401269

28. Chang MN, Yang GL. Strong Consistency of a Nonparametric Estimator of the Survival Function with Doubly Censored Data. Ann Stat. 1987;15: 1536–1547.