Biorisk management Flashcards

(102 cards)

1
Q

most important factor at the origin of accidents

A

human error

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2
Q

have been used, even in the recent past, to threaten and harm people, to disrupt society, economies and the political status quo

A

Toxins and pathogens

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3
Q

Situations that urged the need to respond to the international community and articulate biosecurity in the laboratory:

A

Smallpox
Poliomyelitis
Anthrax

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4
Q

Current trend for biorisk management is:

A

Rather than providing a prescriptive approach to addressing biosafety and
related issues, and requesting compliance with a set of strict rules, the move to a goal-setting approach describing performance expectations for facilities and placing the responsibility on single facilities to demonstrate that appropriate and valid biorisk minimization measures have been established, is
proving very successful.

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5
Q

Agents that pose a risk to the well-being of a person, medically speaking, by directly causing an infection to the man’s systems or by disrupting the environment he/she is functioning in.

A

Biohazard

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6
Q

Commonalities and conflicts of laboratory biosafety and biosecurity

A

Keeping VBM safely and securely inside the areas where they are used and stored;

Controls that reduce unauthorized
access might also hinder an emergency response by fire or rescue personnel.
Biohazard signs placed on laboratory doors

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7
Q

• Possibility that something bad or unpleasant (such as an injury or loss) will happen
• Likelihood that an adverse event involving a specific hazard or threat will occur and
the consequences of that occurrence.
• Is always dependent on a situation

A

Risk

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8
Q

something that has the potential to cause harm

A

Hazard

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9
Q

Risk assessment goal

A

Understand the risk
Determine the risk
Define strategic mitigation of risk

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10
Q

All of these risks involving biological agents

A

Biorisk

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11
Q

Can affect humans, animals, or the environment after an accidental exposure or release of a biological agent.

A

Biosafety risk

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12
Q

Factors contributing to the severity of risk in biosafety risk

A

o Properties of the VBM
o Properties of the potential host.
o Work practices and procedures.

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13
Q

• Results from a person who has malicious intent and has potential access to a
hazardous material or facility.
• Dependent upon intent of the individuals and their level of determination to obtain
or use the asset

A

Biosecurity risk

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14
Q

substantive exercises that evaluate all of a facility’s
risks, and are based on the unique operations of the facility, not on generic
agent risk statements or agent risk groups.

A

Risk assessment

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15
Q

Risk assessment should be based on 3 general question

A

Define the situation
Define risk in the situation
Characterize the risk

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16
Q

Responsibilities of RA

A
  • biorisk management advisors/ biosafety professionals
  • principal investigators, scientist, researchers
  • security and response personnel
  • Legal department
  • Laboratory contractors
  • Executive management
  • Administration
  • Community stakeholders
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17
Q

⇒ implemented according to management’s risk-based decisions, not based on a predetermined description of a biosafety level.
⇒ Most common management approach to achieve safety and security.

A

Mitigation

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18
Q

5 Areas of Controls in mitigatio

A
Elimination/substitution
Engineering controls
Administrative controls
Practices and procedures
PPE
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19
Q

highest degree of risk reduction

A

Elimination

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20
Q

when elimination isn’t possible

A

Substitution

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21
Q

Physical changes to work stations, equipment, production facilities, or any other relevant aspect of the work environment that reduces or prevents exposure to hazards

provide example

A

Engineering controls

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22
Q

Policies, standards, and guidelines used to control risks.

A

Administrative control

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23
Q

devices worn by workers to protect them against chemicals, toxins, and pathogenic hazards in the laboratory

A

PPE

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24
Q

study and use of theory and methods for the analysis of data arising from random processes or phenomena

A

Statistics

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25
The study of how we make sense of data
Statistics
26
Two Main Fields of Statistics
Mathematical and applied statistics
27
study and development of statistical theory and methods in the abstract
Mathematical stats
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the application of statistical methods to solve real problems involving arandomly generated data and the development of new statistical methodology motivated by real problems
Applied stats
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Is the branch of applied statistics directed toward applications in the health sciences and biology
Biostatics
30
Other branches of applied statistics
``` Psychometrics Econometrics Chemometrics Astrostatistics Envirometrics ```
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provide statistical methods that are more heavily used in health application than elsewhere (e.g., survival analysis, longitudinal data analysis.)
Biostatics
32
e starting point of a clinical study.
Hypothesis
33
a statement that describes the relationship between two or more variables and can be proven or disproven by supporting data.
Hypothesis
34
Characteristics of a good hypothesis include
``` Simplicity Clarity Impartiality Specificity Objectively Relevance Verifiability ```
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Types of Hypothesis
Null and Alternative hypothesis
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would predict the direction of the effect
one-tailed alternative hypothesis
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there is an association without specifying the direction
A two-tailed alternative hypothesis
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observations of random variables made on the elements of a population or sample
Data
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``` the quantities (numbers) or qualities (attributes) measured or observed that are to be collected and/or analyzed ```
Data
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The word data is plural - __ is singular
datum
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⇒ A collection of data is often called a
data set
42
CAN NOT BE ORDERED
Nominal scale
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Uses names, labels, or symbols to assign each measurement to one of a limited number of categories that cannot be ordered
Nominal scale
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Example of nominal scale
Blood type (A/B/AB/O) sex (Male/female) race (Somali/ Oromo) marital status (married/not married/ divorced).
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categories can be PLACED IN ORDER
Ordinal scale
46
Assigns each measurement to one of a limited number of categories that are ranked in terms of a graded order
Ordinal scale
47
Example of ordinal scale
Questionnaire, degree of malnutrition, socio-economic status
48
Quantitative data
Interval scale, Ratio scale, discrete data
49
assigns each measurement to one of an unlimited number of categories that are equally spaced.
Interval scale
50
Interval scale example
- body temperature measured on Celsius or Fahrenheit - heart rate measured per second. These kind of measurement can be converted into dichotomous nominal scale e.g. afebrile (oral temp < 37) febrile (>37) also can be ordered (ordinal scale).
51
measurement begins at a true zero point and the scale | has equal space. __ Similar to interval scales but it is the ratio of two measurements and also have a true zero
Ratio scale
52
All values are clearly separated from each | other, although numbers are used.
Discrete data
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Example of discrete data
number of surgery operations performed in one month. | Number of newly diagnosed psychiatric patients last year
54
have values that are intrinsically non-numeric | categorical
Qualitative variables
55
Qualitative variables example
Cause of death, nationality, race, gender, severity of pain -mild moderate severe
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generally have either nominal or ordinal scales
Qualitative variables
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can be reassigned numeric values (eg male/female) but they are still intrinsically qualitative
Qualitative variables
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have values that are intrinsically numeric
Quantitative variables
59
Quantitative variables examples
survival time, systolic blood pressure, number of children in a family, height, age, body mass index.
60
Quantitative variables can be further into
Discrete, continuous variables
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have a set of possible values that is countably finite
Discrete variables
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Discrete values example
E.g. number of pregnancies, shoe size, number of | missing teeth
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has a set of possible values including all values in an interval of the real line
continuous variables
64
Examples of continuous variables
duration of a seizure, body mass index height
65
Systems for Collecting Data
Regular, Ad hoc system
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Registration of events as they become available.
Regular system
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A form of survey to collect information that is not available on a regular basis
Ad hoc system
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collected from the items or individual respondents directly for the purpose of certain study.
Primary data
69
which had been collected by certain people or agency, and statistically treated and the information contained in it is used for other purpose
Secondary data
70
Ways to Present Qualitative Data
⇒ Pie charts ⇒ Bar charts (simple and clustered bar charts) ⇒ Relative frequency (percentage) table
71
⇒ are typically used to present the relative frequency of qualitative data. ⇒ In most cases the data are nominal (not in order), but ordinal data (place in order) can also be displayed on this
Pie charts
72
⇒ Place categories on the horizontal axis. | ⇒ Place frequency (or relative frequency) on the vertical axis
Bar charts (simple and clustered bar charts)
73
Ways to Present Quantitative Data
``` Histogram Frequency polygons Stem and leaf plot Box and whisker plot Scatter plot ```
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which looks like a bar chart but there is no space between bars. The heights of the bars represent either the number or percent of observations within each interval.
Histogram
75
essentially a line that connects the middle of each of the bars of the histogram, are also used extensively.
Frequency polygons and Ogive
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⇒ Orders the data, so that the maximum and minimum are evident ⇒ Gaps in the data become evident ⇒ All the data is displayed ⇒ The shape of the data becomes clearer
Stem and leaf plot
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It is another way to display information when the objective is to illustrate certain locations in the distribution.
Box and whisker plot
78
a good alternative or complement to a histogram and is usually better for showing several simultaneous comparisons
Box and whisker Plot
79
pairs of numerical data, with one variable on each axis, to look for a relationship between them. If the variables are correlated, the points will fall along a line or curve. The better the correlation, the tighter the points will hug the line. This cause analysis tool is considered one of the seven basic quality tools.
scatter diagram graphs
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Basic Biostatistics
♦ Measures of Central Tendency | ♦ Measures of Dispersion
81
To avoid biased reporting central tendency must be addressed collectively, based on all the three measures
mean, median, mode
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To avoid biased reporting central tendency must be addressed collectively, based on all the three measures
mean, median, mode
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middle value | ♦ It is the second measure, is the middle number of a set of numbers arranged in numerical order
Median
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__ sensitive to outliers | __ is not sensitive to outliers
Mean, median
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When the data are highly skewed, the __ is usually preferred
median
86
most frequently observed value(s).
Mode
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Measures of Dispersion
```  Range  Variation (SS) → the sum of squared deviation from the mean.  Variance (S2)  Standard deviation (S)  Standard error (SE)  Quartiles and inter-quartile range (QR)  Coefficient of variation (CV) ```
88
difference between the maximum and the minimum data values.
Range
89
the sum of squared deviation from the mean
Variation
90
average of the squares of the deviations | taken from the mean.
Variance
91
square root of variance
standard deviation
92
measure of precision of the population distribution
standard deviation
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quantifies the precision of the mean. It is a measure of precision of a sample statistic. Tells us how precise our estimate of the parameter is. It is a measure of how far your sample mean is likely to be from the true population mean
Standard error
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quantifies scatter — how much the values vary from | one another
standard deviation
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quantifies how accurately the true mean of the | population
Standard error
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Quartiles are divided by the __
25th percentile, 50th percentile, and 75th percentile
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gives the cut-point for the lower 25% of the data set
Q1
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is the median.
Q2
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gives the cut-point for the upper 25% of the data set
Q3
100
Also known as relative variability.
Coefficient of variation (CV)
101
♦ It is the measure of normalised dispersion. ♦ It is the ratio between measure of spread and measure of location. ♦ It is expressed in percentage (%) form.
Coefficient of variation (CV)
102
Biostatistics Application in various fields of Clinical Research:
* Epidemiology * Clinical trials * Population genetics * Systems biology