Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Arguments about face masks and Covid-19 reflect broader methodologic debates within medical science.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • المصدر:
      Publisher: Kluwer Academic Publishers Country of Publication: Netherlands NLM ID: 8508062 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-7284 (Electronic) Linking ISSN: 03932990 NLM ISO Abbreviation: Eur J Epidemiol Subsets: MEDLINE
    • بيانات النشر:
      Publication: Dordrecht : Kluwer Academic Publishers
      Original Publication: [Rome : The Journal, 1985-
    • الموضوع:
    • نبذة مختصرة :
      There has perhaps been no issue as contentious in Covid-19 as face masks. The most contentious scientific debate has been between those who argue that "there is no scientific evidence", by which they mean that there are no randomized controlled trials (RCTs), versus those who argue that when the evidence is considered together, "the science supports that face coverings save lives". It used to be a 'given' that to decide whether a particular factor, either exogenous or endogenous, can cause a particular disease, and in what order of magnitude, one should consider all reasonably cogent evidence. This approach is being increasingly challenged, both scientifically and politically. The scientific challenge has come from methodologic views that focus on the randomized controlled trial (RCT) as the scientific gold standard, with priority being given, either to evidence from RCTs or to observational studies which closely mimic RCTs. The political challenge has come from various interests calling for the exclusion of epidemiological evidence from consideration by regulatory and advisory committees.
    • References:
      Peeples L. What the data say about wearing face masks. Nature. 2020;586:186–9. (PMID: 10.1038/d41586-020-02801-8)
      Smith GC, Pell JP. Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials. Bmj. 2003;327(7429):1459–61. (PMID: 10.1136/bmj.327.7429.1459)
      Higgins JP, et al. The Cochrane collaboration’s tool for assessing risk of bias in randomised trials. Bmj. 2011;343:d5928. (PMID: 10.1136/bmj.d5928)
      Djulbegovic B, Guyatt GH. Progress in evidence-based medicine: a quarter century on. Lancet. 2017;390(10092):415–23. (PMID: 10.1016/S0140-6736(16)31592-6)
      Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183(8):758–64. (PMID: 10.1093/aje/kwv254)
      Steenland K, et al. Risk of bias assessments and evidence syntheses for observational epidemiologic studies of environmental and occupational exposures: strengths and limitations. Environ Health Perspect. 2020;128(9):95002. (PMID: 10.1289/EHP6980)
      New York Times. EPA to limit science used to write public health rules. 2019. https://www.nytimes.com/2019/11/11/climate/epa-science-trump.html .
      Michaels D. Doubt is their product: how industry’s assault on science threatens your health. New York: Oxford University Press; 2008.
      Michaels D. The triumph of doubt. New York: Oxford University Press; 2020.
      Oreskes N, Conway EM. Merchants of doubt: how a handful of scientists obscured the truth on issues from tobacco smoking to global warming. New York: Bloomsbury Press; 2021.
      Balmes JR. Do we really need another time-series study of the PM2.5-mortality association? N Engl J Med. 2019;381(8):774–6. (PMID: 10.1056/NEJMe1909053)
      Cummings, D. 2019. https://dominiccummings.com/2020/01/02/two-hands-are-a-lot-were-hiring-data-scientists-project-managers-policy-experts-assorted-weirdos/ .
      Hill AB. The environment and disease: association or causation? Proc R Soc Med. 1965;58:295–300. (PMID: 142838791898525)
      Lawlor DA, Tilling K, Davey-Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45:1866–86. (PMID: 10.1093/ije/dyw127)
      Pearce N, Vandenbroucke J, Lawlor D. Causal inference in environmental epidemiology: old and new approaches. Epidemiology. 2019;30:311–6. (PMID: 10.1097/EDE.0000000000000987)
      Pearce N, et al. IARC monographs: 40 years of evaluating carcinogenic hazards to humans. Environ Health Perspect. 2015;123(6):507–14. (PMID: 10.1289/ehp.1409149)
      Guyatt GH, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. Bmj. 2008;336(7650):924–6. (PMID: 10.1136/bmj.39489.470347.AD)
      Rotham KJ. Six persistent research misconceptions. J Gen Intern Med. 2015;29:1060–4.
      Sørensen H. Case-control studies and the hierarchy of study design. Curr Epidemiol Rep. 2016;3(4):262–4. (PMID: 10.1007/s40471-016-0091-7)
      Vandenbroucke JP. Observational research, randomised trials, and two views of medical science. PLoS Med. 2008;5(3):e67. (PMID: 10.1371/journal.pmed.0050067)
      Dwyer O. Food fight: controversy over red meat guidelines rumbles on. Bmj. 2020;368:m397. (PMID: 10.1136/bmj.m397)
      Han MA, et al. Reduction of red and processed meat intake and cancer mortality and incidence a systematic review and meta-analysis of cohort studies. Ann Internal Med. 2019;171(10):711–20. (PMID: 10.7326/M19-0699)
      Vernooij RWM, et al. Patterns of red and processed meat consumption and risk for cardiometabolic and cancer outcomes a systematic review and meta-analysis of cohort studies. Ann Internal Med. 2019;171(10):732–41. (PMID: 10.7326/M19-1583)
      Zeraatkar D, et al. Effect of lower versus higher red meat intake on cardiometabolic and cancer outcomes a systematic review of randomized trials. Ann Intern Med. 2019;171(10):721. (PMID: 10.7326/M19-0622)
      International Agency for Research on Cancer, Red meat and processed meat. Lyon: IARC; 2018.
      Hernan MA. Invited commentary: hypothetical interventions to define causal effects—afterthought or prerequisite? Am J Epidemiol. 2005;162(7):618–20. (PMID: 10.1093/aje/kwi255)
      Guyatt GH, et al. GRADE guidelines: 4 rating the quality of evidence-study limitations (risk of bias). J Clin Epidemiol. 2011;64(4):407–15. (PMID: 10.1016/j.jclinepi.2010.07.017)
      Sterne JAC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. Bmj-Br Med J. 2016;355.
      Vandenbroucke J, Broadbent A, Pearce N. Causality and causal inference in epidemiology: the need for a pluralistic approach. Int J Epidemiol. 2016;45:1776–86. (PMID: 10.1093/ije/dyv341)
      Vandenbroucke J, Pearce N. Point: incident exposures, prevalent exposures, and causal inference: does limiting studies to persons who are followed from first exposure onward damage epidemiology? Am J Epidemiol. 2015;182(10):826–33. (PMID: 10.1093/aje/kwv225)
      Schwartz S, et al. Is the “well-defined intervention assumption” politically conservative? Soc Sci Med. 2016;166:254–7. (PMID: 10.1016/j.socscimed.2015.10.054)
      Rose G. The stategy of preventative medicine. Oxford: Oxford University Press; 1992.
      Vandenbroucke JP, de Craen AJM. Alternative medicine: A “mirror image” for scientific reasoning in conventional medicine. Ann Intern Med. 2001;135(7):507–13. (PMID: 10.7326/0003-4819-135-7-200110020-00010)
      Steenland K, et al. Risk of bias assessments for evidence syntheses of observational epidemiologic studies of environmental and occupational exposures: strengths and limitations. Environ Health Perspect. 2020;128:095002. (PMID: 10.1289/EHP6980)
      Savitz DA, Wellenius GA, Trikalinos TE. The problem with mechanistic risk of bias assessments in evidence synthesis of observational studies and a practical alternative: assess the impact of specific sources of potential bias. Am J Epidemiol. 2019;188:1581–5. (PMID: 10.1093/aje/kwz131)
    • Grant Information:
      MR/P02386X/1 United Kingdom MRC_ Medical Research Council
    • Contributed Indexing:
      Keywords: Causal inference; Causality; Epidemiology; Evidence synthesis; Methods
    • الموضوع:
      Date Created: 20210316 Date Completed: 20210405 Latest Revision: 20240306
    • الموضوع:
      20250114
    • الرقم المعرف:
      PMC7961168
    • الرقم المعرف:
      10.1007/s10654-021-00735-7
    • الرقم المعرف:
      33725291