A systematic review of the quality and timeliness of public health data

Wilfred Bonney

Journal of Hospital Administration ›› 2023, Vol. 12 ›› Issue (1) : 16 -23.

PDF (397KB)
Journal of Hospital Administration ›› 2023, Vol. 12 ›› Issue (1) : 16 -23. DOI: 10.5430/jha.v12n1p16
Review Articles
research-article

A systematic review of the quality and timeliness of public health data

Author information +
History +
PDF (397KB)

Abstract

The quality and timeliness of public health data is a topic of prime concern in this information age. Many epidemiologists, health scientists and researchers in the public health domain have consistently emphasized on the importance of the need for the right timely data for the right decision-making at the right time. In other words, there is an urgent need to ensure that the right data reaches the right people at the right time. However, this urgent need appears to be misleading and not achievable in the current public health practices and workflow processes. The workflow processes in the current healthcare environments enable data collection to be delayed and only to be captured when the events have already occurred. In this paper, a systematic review of relevant scientific literature was used to not only explore the complexity and uniqueness of public health data, but also explain why improving the quality and timeliness of public health data is a challenging endeavor for many epidemiologists, health scientists and researchers. Recommendations for streamlining the public health workflow processes to support the generation of high-quality and timely public health data were also discussed in the paper.

Keywords

Public health / Public health data / Data quality / Public health informatics / Public health research

Cite this article

Download citation ▾
Wilfred Bonney. A systematic review of the quality and timeliness of public health data. Journal of Hospital Administration, 2023, 12(1): 16-23 DOI:10.5430/jha.v12n1p16

登录浏览全文

4963

注册一个新账户 忘记密码

CONFLICTS OF INTEREST DISCLOSURE

The author declares no conflicts of interest.

References

[1]

Axelrath S. Challenges Encountered in the Public Health Data Collection of COVID-19 Cases Among People Experiencing Homelessness. JAMA Netw Open. 2022; 5(8): e2229703. PMid: 35980641. https://doi.org/10.1001/jamanetworkopen.2022.29703

[2]

de Bienassis K, Fujisawa R, Hashiguchi TCO, et al. Health data and governance developments in relation to COVID-19: How OECD countries are adjusting health data systems for the new normal. 2022 [cited 2022 Oct 13]. Available from: https://www.oecd-ilibrary.org/social-issues-migration-health/health-data-and-governance-developments-in-relation-to-covid-19_aec7c409-en

[3]

Dyda A, Purcell M, Curtis S, et al. Differential privacy for public health data: An innovative tool to optimize information sharing while protecting data confidentiality. Patterns. 2021; 2(12): 100366. PMid: 34909703. https://doi.org/10.1016/j.patter.2021.100366

[4]

Huyser KR, Horse AJY, Kuhlemeier AA, Huyser MR. COVID-19 Pandemic and Indigenous Representation in Public Health Data. Am J Public Health. 2021; 111(S3): S208-14. PMid: 34709868. https://doi.org/10.2105/AJPH.2021.306415

[5]

Nuzzo JB, Borio LL, Gostin LO. The WHO Declaration of Monkeypox as a Global Public Health Emergency. JAMA. 2022; 328(7): 615. PMid: 35895041. https://doi.org/10.1001/jama.2022.12513

[6]

Peddireddy AS, Xie D, Patil P, et al. From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance [Internet]. In: 2020 IEEE International Conference on Big Data (Big Data). Atlanta, GA, USA: IEEE; 2020 [cited 2022 Oct 13]. 1380-7 p. https://doi.org/10.1109/BigData50022.2020.93784352020

[7]

Soualmia LF, Hollis KF, Mougin F, et al. Health Data, Information, and Knowledge Sharing for Addressing the COVID-19. Yearb Med Inform. 2021; 30(01): 004-7. PMid: 34479377. https://doi.org/10.1055/s-0041-1726541

[8]

World Health Organization. Naming the coronavirus disease (COVID-19) and the virus that causes it. 2020 [cited 2022 Sep 22]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-v irus-that-causes-it

[9]

Martin LT, Nelson C, Yeung D, et al. The Issues of Interoperability and Data Connectedness for Public Health. Big Data. 2022; 10(S1): S19-24. https://doi.org/10.1089/big.2022.0207PMid:36070509 PMCid:PMC9508439

[10]

Cai L, Zhu Y. The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. CODATA. 2015; 14(2): 1-10. https://doi.org/10.5334/dsj-2015-002

[11]

Chelladurai U, Pandian S. A novel blockchain based electronic health record automation system for healthcare. J Ambient Intell Human Comput. 2022; 13(1): 693-703. https://doi.org/10.1007/s12652-021-03163-3

[12]

Shapiro JS, Mostashari F, Hripcsak G, et al. Using Health Information Exchange to Improve Public Health. Am J Public Health. 2011; 101(4): 616-23. PMid: 21330598. https://doi.org/10.2105/AJPH.2008.158980

[13]

Acosta JD, Chandra A, Yeung D, et al. What Data Should Be Included in a Modern Public Health Data System. Big Data. 2022; 10(S1): S9-14. PMid: 36070507. https://doi.org/10.1089/big.2022.0205

[14]

Kadakia KT, Howell MD, DeSalvo KB. Modernizing Public Health Data Systems: Lessons from the Health Information Technology for Economic and Clinical Health (HITECH) Act. JAMA. 2021; 326(5): 385. PMid: 34342612. https://doi.org/10.1001/jama.2021.12000

[15]

Bauer UE, Plescia M. Addressing Disparities in the Health of American Indian and Alaska Native People: The Importance of Improved Public Health Data. Am J Public Health. 2014; 104(S3): S255-7. PMid: 24754654. https://doi.org/10.2105/AJPH.2013.301602

[16]

Choi BCK. The Past, Present, and Future of Public Health Surveillance. Scientifica. 2012; 2012: 1-26. PMid: 24278752. https://doi.org/10.6064/2012/875253

[17]

Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; n71. PMid: 33782057. https://doi.org/10.1136/bmj.n71

[18]

Page MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021; n160. PMid: 33781993. https://doi.org/10.1136/bmj.n160

[19]

Kass-Hout TA, Alhinnawi H. Social media in public health. Br Med Bull. 2013; 108(1): 5-24. PMid: 24103335. https://doi.org/10.1093/bmb/ldt028

[20]

Papagari Sangareddy SR, Aspevig J. New Means of Data Collection and Accessibility. In: Magnuson JA, Dixon BE, editors. Public Health Informatics and Information Systems. Cham: Springer International Publishing; 2020 [cited 2022 Oct 13]. 289-305 p. Available from: https://doi.org/10.1007/978-3-030-41215-9_17

[21]

Sundararaman A, Ramanathan SV. Open Research Issues and Emerging Research Directions in Data Quality for Public Health. In:21st International Conference on Information Quality (ICIQ 2016). Ciudad Real, Spain; 2016. 65-74 p.

[22]

van Panhuis WG, Paul P, Emerson C, et al. A systematic review of barriers to data sharing in public health. BMC Public Health. 2014; 14(1): 1144. PMid: 25377061. https://doi.org/10.1186/1471-2458-14-1144

[23]

MeSH Browser. Public Health: MeSH Descriptor Data 2022. 2022 [cited 2022 Sep 22]. Available from: https://meshb.nlm.nih.gov/record/ui?ui=D011634

[24]

Raymond SA, Gawrylewski HM, Ganter J, et al. CDISC clinical research glossary. Appl Clin Trials. 2007; 16: 12-52.

[25]

Lee LM, Gostin LO. Ethical Collection, Storage, and Use of Public Health Data: A Proposal for a National Privacy Protection. JAMA. 2009; 302(1): 82-4. PMid: 19567443. https://doi.org/10.1001/jama.2009.958

[26]

U.S. Centers for Disease Control and Prevention (CDC). Policy on Public Health Research and Nonresearch Data Management and Access. 2016 [cited 2022 Sep 30]. Available from: https://www.cdc.gov/maso/policy/policy385.pdf

[27]

Krieger N. The Making of Public Health Data: Paradigms, Politics, and Policy. J Public Health Policy. 1992; 13(4): 412. PMid: 1287038. https://doi.org/10.2307/3342531

[28]

Ishwarappa, Anuradha J. A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology. Procedia Comput Sci. 2015; 48: 319-24. https://doi.org/10.1016/j.procs.2015.04.188

[29]

Katal A, Wazid M, Goudar RH. Big data:Issues, challenges, tools and Good practices. In:2013 Sixth International Conference on Contemporary Computing (IC3). Noida, India: IEEE; 2013

[30]

Millham R, Agbehadji IE, Frimpong SO. The Paradigm of Fog Computing with Bio-inspired Search Methods and the “5Vs” of Big Data. In: Fong SJ, Millham RC, editors. Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing. Singapore: Springer Singapore; 2021 [cited 2022 Oct 13]. 145-67 p. https://doi.org/10.1007/978-981-15-6695-0_8

[31]

Burgun A, Bernal-Delgado E, Kuchinke W, et al. Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe. Yearb Med Inform. 2017; 26(01): 235-40. PMid: 29063571. https://doi.org/10.15265/IY-2017-034

[32]

Talend. What is a Data Lake?-[cited 2022 Sep 22]. Available from: https://www.talend.com/resources/what-is-d ata-lake/

[33]

Bertsimas D, Pawlowski C, Zhuo YD. From predictive methods to missing data imputation: an optimization approach. J Mach Learn Res. 2017; 18(1): 7133-71.

[34]

Lin WC, Tsai CF. Missing value imputation: a review and analysis of the literature (2006-2017). Artif Intell Rev. 2020; 53(2): 1487-509. https://doi.org/10.1007/s10462-019-09709-4

[35]

Madley-Dowd P, Hughes R, Tilling K, et al. The proportion of missing data should not be used to guide decisions on multiple imputation. J Clin Epidemiol. 2019; 110: 63-73. PMid: 30878639. https://doi.org/10.1016/j.jclinepi.2019.02.016

[36]

Bonney W. Is it appropriate, or ethical, to use health data collected for the purpose of direct patient care to develop computerized predictive decision support tools? Stud Health Technol Inform. 2009; 143: 115-21. PMid: 19380924. https://doi.org/10.3233/978-1-58603-979-0-115

[37]

Box GEP. Robustness in the Strategy of Scientific Model Building. In: Launer RL,Wilkinson GN, editors. Robustness in Statistics. Elsevier; 1979 [cited 2023 Apr 8]. 201-36 p. https://doi.org/10.1016/B978-0-12-438150-6.50018-2

[38]

World Health Organization. 2016 Annual Report Communicable Diseases Cluster. 2017 [cited 2022 Sep 22]. Available from: https://apps.who.int/iris/bitstream/handle/10665/259634/9789290233930-eng.pdf?sequence=1&isAllowed=y

[39]

Wang RY, Strong DM. Beyond Accuracy: What Data Quality Means to Data Consumers. J Manag Info Syst. 1996; 12(4): 5-33. https://doi.org/10.1080/07421222.1996.11518099

[40]

Bonney W, Scobbie D, Nind T, et al. Profiling Clinical Datasets for Data Quality Assessment and Improvement [Internet]. In: BCS Health Informatics Scotland 2014 Conference. 2014 [cited 2022 Oct 13]. 1-8 p. Available from: https://scienceopen.com/document?vid=89e51c55-2e78-480b-93ca-4ccca7e91099

[41]

Karr AF, Sanil AP, Banks DL. Data quality: A statistical perspective. Stat Methodol. 2006; 3(2): 137-73. https://doi.org/10.1016/j.stamet.2005.08.005

[42]

Liaw ST, Rahimi A, Ray P, et al. Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature. Int J Med Inform. 2013; 82(1): 10-24. PMid: 23122633. https://doi.org/10.1016/j.ijmedinf.2012.10.001

[43]

Wang RY. A product perspective on total data quality management. Commun ACM. 1998; 41(2): 58-65. https://doi.org/10.1145/269012.269022

[44]

Redman TC. Data quality:the field guide. Boston, MA: Digital Press; 2001.

[45]

Goldsmith J, Sun Y, Fried LP, et al. The Emergence and Future of Public Health Data Science. Public Health Rev. 2021; 42: 1604023. PMid: 34692178. https://doi.org/10.3389/phrs.2021.1604023

[46]

U.S. Centers for Disease Control and Prevention (CDC). Case definitions for infectious conditions under public health surveillance. 1997 [cited 2022 Sep 22]. Available from: https://wonder.cdc.gov/wonder/Prevguid/m0047449/m0047449.asp

[47]

Asaro PV, Land GH, Hales JW. Making Public Health Data Available to Community-Level Decision Makers-Goals, Issues, and a Case Report: J Public Health Manag Pract. 2001; 7(5): 58-63. PMid:11680032. https://doi.org/10.1097/00124784-200107050-00009

[48]

Cheung S.sam Dibiguating the benefits and risks from public health data in the digital economy. Big Data Soc. 2020; 7(1): 1-15. https://doi.org/10.1177/2053951720933924

[49]

Kostkova P. Disease surveillance data sharing for public health: the next ethical frontiers. Life Sci Soc Policy. 2018; 14(1): 16. PMid: 29971516. https://doi.org/10.1186/s40504-018-0078-x

AI Summary AI Mindmap
PDF (397KB)

111

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/