How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials
Objective Reports of efficacy, effectiveness and harms of COVID-19 vaccines have not used key indicators from evidence-based medicine (EBM) that can inform policies about vaccine distribution. This study aims to clarify EBM indicators that consider baseline risks when assessing vaccines’ benefits ve...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2022-12-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/12/e063525.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849722105827950592 |
|---|---|
| author | Ella Fassler Andrew Larkin Howard Waitzkin Kesavan Rajasekharan Nayar |
| author_facet | Ella Fassler Andrew Larkin Howard Waitzkin Kesavan Rajasekharan Nayar |
| author_sort | Ella Fassler |
| collection | DOAJ |
| description | Objective Reports of efficacy, effectiveness and harms of COVID-19 vaccines have not used key indicators from evidence-based medicine (EBM) that can inform policies about vaccine distribution. This study aims to clarify EBM indicators that consider baseline risks when assessing vaccines’ benefits versus harms: absolute risk reduction (ARR) and number needed to be vaccinated (NNV), versus absolute risk of the intervention (ARI) and number needed to harm (NNH).Methods We used a multimethod approach, including a scoping review of the literature; calculation of risk reductions and harms from data concerning five major vaccines; analysis of risk reductions in population subgroups with varying baseline risks; and comparisons with prior vaccines.Findings The scoping review showed few reports regarding ARR, NNV, ARI and NNH; comparisons of benefits versus harms using these EBM methods; or analyses of varying baseline risks. Calculated ARRs for symptomatic infection and hospitalisation were approximately 1% and 0.1%, respectively, as compared with relative risk reduction of 50%–95% and 58%–100%. NNV to prevent one symptomatic infection and one hospitalisation was in the range of 80–500 and 500–4000. Based on available data, ARI and NNH as measures of harm were difficult to calculate, and the balance between benefits and harms using EBM measures remained uncertain. The effectiveness of COVID-19 vaccines as measured by ARR and NNV was substantially higher in population subgroups with high versus low baseline risks.Conclusions Priorities for vaccine distribution should target subpopulations with higher baseline risks. Similar analyses using ARR/NNV and ARI/NNH would strengthen evaluations of vaccines’ benefits versus harms. An EBM perspective on vaccine distribution that emphasises baseline risks becomes especially important as the world’s population continues to face major barriers to vaccine access—sometimes termed ‘vaccine apartheid’. |
| format | Article |
| id | doaj-art-3810e0fa0b5c4ff5a7eeca055b161e16 |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-3810e0fa0b5c4ff5a7eeca055b161e162025-08-20T03:11:26ZengBMJ Publishing GroupBMJ Open2044-60552022-12-01121210.1136/bmjopen-2022-063525How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trialsElla Fassler0Andrew Larkin1Howard Waitzkin2Kesavan Rajasekharan Nayar31 Allende Program in Social Medicine, Albuquerque, New Mexico, USA1 Allende Program in Social Medicine, Albuquerque, New Mexico, USA1 Allende Program in Social Medicine, Albuquerque, New Mexico, USA3 Santhigiri Research Foundation, Thiruvananthapuram, Kerala, IndiaObjective Reports of efficacy, effectiveness and harms of COVID-19 vaccines have not used key indicators from evidence-based medicine (EBM) that can inform policies about vaccine distribution. This study aims to clarify EBM indicators that consider baseline risks when assessing vaccines’ benefits versus harms: absolute risk reduction (ARR) and number needed to be vaccinated (NNV), versus absolute risk of the intervention (ARI) and number needed to harm (NNH).Methods We used a multimethod approach, including a scoping review of the literature; calculation of risk reductions and harms from data concerning five major vaccines; analysis of risk reductions in population subgroups with varying baseline risks; and comparisons with prior vaccines.Findings The scoping review showed few reports regarding ARR, NNV, ARI and NNH; comparisons of benefits versus harms using these EBM methods; or analyses of varying baseline risks. Calculated ARRs for symptomatic infection and hospitalisation were approximately 1% and 0.1%, respectively, as compared with relative risk reduction of 50%–95% and 58%–100%. NNV to prevent one symptomatic infection and one hospitalisation was in the range of 80–500 and 500–4000. Based on available data, ARI and NNH as measures of harm were difficult to calculate, and the balance between benefits and harms using EBM measures remained uncertain. The effectiveness of COVID-19 vaccines as measured by ARR and NNV was substantially higher in population subgroups with high versus low baseline risks.Conclusions Priorities for vaccine distribution should target subpopulations with higher baseline risks. Similar analyses using ARR/NNV and ARI/NNH would strengthen evaluations of vaccines’ benefits versus harms. An EBM perspective on vaccine distribution that emphasises baseline risks becomes especially important as the world’s population continues to face major barriers to vaccine access—sometimes termed ‘vaccine apartheid’.https://bmjopen.bmj.com/content/12/12/e063525.full |
| spellingShingle | Ella Fassler Andrew Larkin Howard Waitzkin Kesavan Rajasekharan Nayar How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials BMJ Open |
| title | How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials |
| title_full | How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials |
| title_fullStr | How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials |
| title_full_unstemmed | How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials |
| title_short | How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials |
| title_sort | how missing evidence based medicine indicators can inform covid 19 vaccine distribution policies a scoping review and calculation of indicators from data in randomised controlled trials |
| url | https://bmjopen.bmj.com/content/12/12/e063525.full |
| work_keys_str_mv | AT ellafassler howmissingevidencebasedmedicineindicatorscaninformcovid19vaccinedistributionpoliciesascopingreviewandcalculationofindicatorsfromdatainrandomisedcontrolledtrials AT andrewlarkin howmissingevidencebasedmedicineindicatorscaninformcovid19vaccinedistributionpoliciesascopingreviewandcalculationofindicatorsfromdatainrandomisedcontrolledtrials AT howardwaitzkin howmissingevidencebasedmedicineindicatorscaninformcovid19vaccinedistributionpoliciesascopingreviewandcalculationofindicatorsfromdatainrandomisedcontrolledtrials AT kesavanrajasekharannayar howmissingevidencebasedmedicineindicatorscaninformcovid19vaccinedistributionpoliciesascopingreviewandcalculationofindicatorsfromdatainrandomisedcontrolledtrials |