NCCMT - URE - Effectiveness of Interventions - Understanding the Number Needed to Treat
Summary
TLDRThe video script discusses the complexities of decision-making in public health, especially when selecting interventions with limited resources. It introduces the model of evidence-informed decision-making and emphasizes the importance of considering the magnitude of the problem, available resources, and societal preferences. The script also explains how to calculate and interpret the Number Needed to Treat (NNT), a measure that helps public health decision-makers understand the impact of interventions on a population level. It provides examples of calculating NNT for interventions like social media-based pregnancy prevention and car seat installation clinics, highlighting the balance between benefits and potential harms.
Takeaways
- đĄ Public health decision-making can be complex due to finite budgets and multiple effective interventions.
- đ The National collaborating Center for methods and tools suggests using evidence-informed decision-making models in public health.
- đ Decision-makers should consider the magnitude of the problem, available resources, and societal preferences when choosing interventions.
- 𧟠Number Needed to Treat (NNT) is a useful measure to calculate how many people need an intervention for one to benefit, aiding in decision-making.
- đ NNT is calculated by dividing 1 by the Absolute Risk Reduction (ARR), which is the difference in outcome rates between control and intervention groups.
- đ€ Calculating NNT helps interpret research results, showing how likely and impactful an intervention is on a population level.
- đ« NNTs are not always reported in studies, so public health professionals may need to calculate them using provided data.
- đ¶ The script provides an example of calculating NNT for a social media-based teenage pregnancy prevention campaign.
- đ Another example is given for a car seat installation clinic, demonstrating how NNT can be calculated and interpreted for different interventions.
- â ïž Number Needed to Harm (NNH) is also introduced to consider potential negative outcomes of interventions, balancing benefits and risks.
- đ Number Needed to Immunize (NNI) is mentioned as a related concept, important for immunization programs to prevent diseases.
Q & A
What is the primary challenge faced by health departments when choosing interventions?
-The primary challenge is deciding which intervention to implement among many effective ones due to a finite budget.
How does the model of evidence-informed decision-making assist in public health decisions?
-It assists by considering not only research evidence but also the magnitude of the problem, resources needed, and societal and political preferences.
What additional factors do decision-makers need to consider when making decisions across the spectrum of public health services?
-They need to compare the benefits and resources required for different interventions to determine which delivers the greatest impact for the investment.
What are the three valuable concepts that can help in decision-making but do not tell you everything about an intervention's effectiveness?
-Relative risks, odds ratios, and confidence intervals help estimate the likelihood and size of an effect but do not indicate who the intervention worked for or how many people experienced the desired outcome.
What is the Number Needed to Treat (NNT) and how is it calculated?
-NNT is the number of people who need to receive an intervention for one person to experience a positive outcome. It is calculated as 1 divided by the absolute risk reduction (ARR).
Why is it important to calculate the NNT for public health interventions?
-Calculating NNT provides a clear understanding of how many people need to be exposed to an intervention to achieve a single positive outcome, aiding in resource allocation and program decision-making.
In the example of teenage pregnancy prevention using social media, what was the calculated NNT?
-The calculated NNT was 67, meaning 67 teenagers need to receive the intervention to prevent one pregnancy over a one-year period.
What is the significance of a smaller NNT in public health interventions?
-A smaller NNT indicates that fewer people need to be exposed to the intervention for one positive outcome, suggesting a greater potential impact at a population level.
What is the Number Needed to Harm (NNH) and how does it differ from NNT?
-NNH is the number of people who need to be exposed to an intervention for one person to experience an unintended negative outcome. It differs from NNT by focusing on harm rather than benefit.
How can the concept of NNT be applied to immunization programs?
-The Number Needed to Immunize (NNI) is calculated similarly to NNT and tells us how many people need to be immunized to avoid one negative outcome, such as death from an infection.
Why are NNTs, NNHs, and NNIs considered powerful tools for public health decision-makers?
-These measures provide clear, quantifiable information on the impact and potential harm of interventions, aiding decision-makers in making informed choices about public health programs.
Outlines
đ Decision-Making in Public Health: Evidence-Informed Approach
This paragraph discusses the challenges faced by health departments in choosing which interventions to implement, given limited budgets. It introduces the model of evidence-informed decision-making in public health, which considers not only research evidence but also the magnitude of the problem, resources needed, and societal and political preferences. The paragraph emphasizes the complexity of decision-making, especially when comparing interventions across different public health services. It also introduces the concept of Number Needed to Treat (NNT), a statistical measure that helps determine the number of people who need to receive an intervention for one person to experience a positive outcome. The paragraph provides an example of how to calculate NNT using hypothetical data on teenage pregnancy rates and the effectiveness of social media-based pregnancy prevention messages.
đ Calculating and Interpreting NNT in Public Health Interventions
This paragraph delves into the process of calculating the Number Needed to Treat (NNT) and its interpretation in the context of public health interventions. It explains the formula for calculating NNT, which is 1 divided by the Absolute Risk Reduction (ARR). The ARR is the difference in outcome rates between the intervention and control groups. Using the example of a social media campaign to reduce teenage pregnancy, the paragraph demonstrates how to calculate NNT and interpret its meaning. It also discusses the implications of a low NNT value, indicating a relatively effective intervention. The paragraph further explores other related measures such as Number Needed to Harm (NNH) and Number Needed to Immunize (NNI), which provide additional insights for decision-makers when considering the potential negative outcomes or benefits of interventions.
đ Utilizing NNT in Public Health Decision-Making
The final paragraph emphasizes the practical application of NNT in public health decision-making. It highlights that with practice, decision-makers can become proficient in calculating and interpreting NNT, which can inform decisions about public health interventions. The paragraph suggests that NNT, along with other measures like NNH and NNI, can provide valuable information to help balance the benefits and potential harms of interventions. It concludes by stating that these calculations are straightforward and can be performed using data typically available in published studies, thus empowering public health professionals to make informed choices.
Mindmap
Keywords
đĄEvidence-Informed Decision-Making
đĄIntervention
đĄMagnitude of the Problem
đĄResources
đĄSocietal and Political Preferences
đĄRelative Risks, Odds Ratios, and Confidence Intervals
đĄNumber Needed to Treat (NNT)
đĄAbsolute Risk Reduction (ARR)
đĄNumber Needed to Harm (NNH)
đĄNumber Needed to Immunize (NNI)
Highlights
The challenge of selecting the most effective public health intervention given a finite budget.
The model of evidence-informed decision-making in public health.
Considering the magnitude of the problem, resources, and societal preferences in decision-making.
The complexity of choosing interventions across different public health programs.
Using relative risks, odds ratios, and confidence intervals in decision-making.
The importance of understanding who an intervention worked for and the desired outcomes.
Introduction to the Number Needed to Treat (NNT) as a decision-making tool.
Definition and calculation method of NNT in public health interventions.
Example of calculating NNT for a social media-based teenage pregnancy prevention program.
Interpretation of NNT results and their implications for public health interventions.
Calculating NNT for a car seat installation clinic intervention.
The significance of a smaller NNT in indicating a greater potential impact at a population level.
Introduction to the Number Needed to Harm (NNH) for assessing negative outcomes.
Example of calculating NNH for an anti-bullying campaign.
The concept of Number Needed to Immunize (NNI) in the context of vaccination programs.
Practical application of NNT, NNH, and NNI in public health decision-making.
The simplicity of calculating NNTs with data from published studies.
Building confidence in calculating and interpreting NNTs for public health interventions.
Transcripts
have you ever found yourself in a
situation where you have to decide which
intervention among many you want to
implement at your health department you
have looked at the research evidence and
even though a number of interventions
have been shown to be effective in
practice you have a finite budget and
can only choose one how do you decide
which one to implement you may have used
the model of evidence informed
decision-making in public health
suggested by the National collaborating
Center for methods and tools to assist
you in making your decision if you were
to use this model in addition to looking
at the research evidence you would also
look at the magnitude of the problem in
your community the resources needed to
implement and maintain the intervention
as well as the societal and political
preferences choosing the best
combination of interventions can be very
challenging even when the decision is
limited to one problem within one
program such as implementing a vaccine
promotion strategy used in social media
the decision-making process becomes even
more complicated when decisions across
the spectrum of public health services
are being made for example strategic
planning initiatives in these instances
decision makers may have to compare the
benefits and resources required to
maintain an environmental health
intervention such as monitoring swimming
conditions at public beaches to a
chronic disease prevention intervention
such as a healthy eating campaign given
limited resources decision-makers may
need to consider carefully across all
public health programs which
interventions deliver the greatest
impact for the required human and
financial investment please view the
other videos in this series to learn how
to use relative risks odds ratios and
confidence intervals in your
decision-making these valuable concepts
will give you an estimate of how likely
the result is whether it is
statistically significant and how large
the effect is what they don't tell you
is who an intervention worked for or how
many people had the desired outcome they
also don't tell you how many people need
to receive the intervention in order to
see one person with the desired outcome
to answer these questions we can use a
Mystikal measure called number needed to
treat or NNT it is defined as the number
of people who need to receive an
intervention in order for one person to
experience a positive outcome or to
avoid a negative one nnts can only be
calculated when there are only two
possible options for the outcome being
measured for example initiating breast
feeding or not initiating breast feeding
currently an antes are not usually
reported in the public health literature
but they are easy to calculate let's
take a look at some public health
examples to see just how easy it is to
calculate and interpret them remember
the data presented here are hypothetical
and are for demonstration purposes only
let's say data were presented at a
senior management meeting illustrating
that teenage pregnancy rates are higher
in your community than the provincial
average you've been asked to make
recommendations to address this issue
after searching health evidence org you
find a recently published systematic
review reporting that pregnancy
prevention messages disseminated using
social media lead to a statistically
significant reduction in teenage
pregnancy although a reduction in
teenage pregnancy is the outcome you
were hoping to see you wonder how many
teenagers need to receive the messages
in order for one pregnancy to be
prevented given there are only two
options for the outcome pregnant or not
pregnant you know that it is possible to
calculate an N NT unfortunately an N NT
is not reported in the systematic review
so you'll need to calculate it yourself
the formula for calculating and n n T is
1 divided by the absolute risk reduction
also referred to as an AR AR it sounds
complicated but it really isn't an AR R
is simply the difference in rates of the
outcome in the control group compared to
the intervention group let's do the math
say we have 1,000 teenagers in the
intervention group who receive weekly
messages over a one-year period about
pregnancy prevention and another 1,000
teenagers in a control group who do not
after one year we see how many teenagers
became pregnant in each
we find there were 15 pregnancies in the
intervention group and 30 pregnancies in
the control group the rate of pregnancy
in the intervention group is the number
of pregnancies divided by the total
number of teenagers in the intervention
group in this case 15 divided by 1,000
which equals 0.01 5 the rate of
pregnancy in the control group is 30
divided by 1000 or 0.030 we calculate
the absolute risk reduction by
subtracting 0.015 from 0.03 0 which
gives us an AR AR of 0.01 5 as mentioned
earlier to calculate the number needed
to treat we divide one by the 8 RR so 1
divided by 0.01 5 gives us an N NT of
66.7 because we can't have half a person
pregnant or half a pregnancy we round
the n NT up to the nearest whole number
so our n NT is 67 now we need to
interpret what an N NT of 67 means it
means we would need to expose 67
teenagers to the intervention in order
to prevent one pregnancy over a one-year
period this would constitute a
relatively effective intervention as few
teenagers need to receive the
intervention in order for one negative
outcome teenage pregnancy to be avoided
let's look at another example suppose
you are planning a strategy to increase
the number of properly installed car
seats a recently published meta-analysis
reported on the effectiveness of holding
clinics to promote the correct
installation of car seats the
intervention consisted of staff
assessing whether car seats were
installed correctly and teaching people
how to correctly install them the
control group received standard practice
where people could call and request
information on correct installation one
month after the intervention cars were
assessed to identify correctly installed
car seats following data were reported
in the meta-analysis among 500 people
exposed to the intervention 225
correctly installed their car seats
compared to 195 in the control group we
calculate the event rate in the
intervention group by dividing the
number of correctly installed car seats
by the total number of people in the
intervention group so 225 divided by 500
which equals 0.45 using the same formula
we divide 195 by 500 which gives us an
event rate in the control group of 0.39
to calculate the ARR we subtract 0.39
from 0.45 which gives us an AR r of 0.06
the N NT is calculated by dividing 1 by
0.06 which gives us an N NT of sixteen
point six seven we round that up to the
next whole number giving us an N NT of
seventeen which means that 17 people
need to attend the clinic in order for
one car seat to be correctly installed
one month later now that we have
determined how many people need to be
exposed to these two interventions in
order to either obtain one positive
outcome or avoid one negative outcome we
can identify both the financial and
human resources that will be required to
achieve a meaningful outcome for the
whole population while there are other
factors to consider in interpreting NN
teas such as the underlying risk of an
outcome in specific populations a
general understanding of how to
calculate and interpret them will
provide Public Health decision-makers
with powerful information upon which
they can make difficult program
decisions generally the smaller the NNT
the greater potential impact there will
be at a population level sometimes an
intervention can produce unanticipated
negative outcomes or harms which should
be considered when making a decision to
assess the magnitude of negative
outcomes we calculate the number needed
to harm or NNH the nnh tells us how many
people would need to be exposed to an
intervention in order for one unintended
negative outcome to be observed for
example let's say we are considering an
anti-bullying camp
the evidence indicates that the campaign
is effective in reducing the number of
bullying episodes however more children
exposed to the intervention report
feeling isolated compared to those not
exposed you can calculate the N and H to
determine how many students would need
to be exposed to the intervention for
one student to feel more isolated this
will help you weigh the benefits on an
intervention against the potential harms
one final use of this calculation is the
number needed to immunize or nni it is
calculated the same way as both the n n
h and n n t and tells us the number of
people who need to be immunized in order
to avoid one negative outcome such as
death as a result of infection you can
see how n n T's would be helpful in
adding information related to how many
people would need to be exposed to an
intervention in order for one person to
experience the outcome of interest
number needed to harm and number needed
to immunize also provide useful
information to decision makers along
with number needed to treat they are
simple to calculate and most published
studies provide sufficient data on
outcome measures so that you can easily
calculate them yourself with a little
practice you will not only become
confident in calculating n n T's but
also interpreting them and using them to
inform decisions about public health
interventions
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