Biorisk management Flashcards

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
Q

The study of how we make sense of data

A

Statistics

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26
Q

Two Main Fields of Statistics

A

Mathematical and applied statistics

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27
Q

study and development of statistical theory and methods in the abstract

A

Mathematical stats

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28
Q

the application of statistical methods to solve real problems involving arandomly generated data and the development of new statistical methodology
motivated by real problems

A

Applied stats

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29
Q

Is the branch of applied statistics directed toward applications in the health sciences and biology

A

Biostatics

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30
Q

Other branches of applied statistics

A
Psychometrics
Econometrics
Chemometrics
Astrostatistics
Envirometrics
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31
Q

provide statistical methods that are more heavily used in health application than elsewhere (e.g., survival analysis, longitudinal data analysis.)

A

Biostatics

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32
Q

e starting point of a clinical study.

A

Hypothesis

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33
Q

a statement that describes the relationship between two or more variables and can be proven or disproven by supporting data.

A

Hypothesis

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34
Q

Characteristics of a good hypothesis include

A
Simplicity
Clarity
Impartiality
Specificity
Objectively
Relevance
Verifiability
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35
Q

Types of Hypothesis

A

Null and Alternative hypothesis

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36
Q

would predict the direction of the effect

A

one-tailed alternative hypothesis

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37
Q

there is an association without specifying the direction

A

A two-tailed alternative hypothesis

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38
Q

observations of random variables made on the elements of a population or sample

A

Data

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39
Q
the quantities (numbers) or qualities (attributes) 
measured or observed that are to be collected and/or analyzed
A

Data

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40
Q

The word data is plural - __ is singular

A

datum

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41
Q

⇒ A collection of data is often called a

A

data set

42
Q

CAN NOT BE ORDERED

A

Nominal scale

43
Q

Uses names, labels, or symbols to assign each measurement to one of a limited number of categories that cannot be ordered

A

Nominal scale

44
Q

Example of nominal scale

A

Blood type (A/B/AB/O) sex (Male/female) race (Somali/ Oromo) marital status (married/not married/ divorced).

45
Q

categories can be PLACED IN ORDER

A

Ordinal scale

46
Q

Assigns each measurement to one of a limited number of categories that are ranked in terms of a graded order

A

Ordinal scale

47
Q

Example of ordinal scale

A

Questionnaire, degree of malnutrition, socio-economic status

48
Q

Quantitative data

A

Interval scale, Ratio scale, discrete data

49
Q

assigns each measurement to one of an unlimited number of categories that are equally spaced.

A

Interval scale

50
Q

Interval scale example

A
  • 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
Q

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

A

Ratio scale

52
Q

All values are clearly separated from each

other, although numbers are used.

A

Discrete data

53
Q

Example of discrete data

A

number of surgery operations performed in one month.

Number of newly diagnosed psychiatric patients last year

54
Q

have values that are intrinsically non-numeric

categorical

A

Qualitative variables

55
Q

Qualitative variables example

A

Cause of death, nationality, race, gender, severity of pain -mild moderate severe

56
Q

generally have either nominal or ordinal scales

A

Qualitative variables

57
Q

can be reassigned numeric values (eg male/female) but they are still intrinsically qualitative

A

Qualitative variables

58
Q

have values that are intrinsically numeric

A

Quantitative variables

59
Q

Quantitative variables examples

A

survival time, systolic blood pressure, number of children in a family, height, age, body mass index.

60
Q

Quantitative variables can be further into

A

Discrete, continuous variables

61
Q

have a set of possible values that is countably finite

A

Discrete variables

62
Q

Discrete values example

A

E.g. number of pregnancies, shoe size, number of

missing teeth

63
Q

has a set of possible values including all values in an interval of the real line

A

continuous variables

64
Q

Examples of continuous variables

A

duration of a seizure, body mass index height

65
Q

Systems for Collecting Data

A

Regular, Ad hoc system

66
Q

Registration of events as they become available.

A

Regular system

67
Q

A form of survey to collect information that is not available on a regular basis

A

Ad hoc system

68
Q

collected from the items or individual respondents directly for the purpose of certain study.

A

Primary data

69
Q

which had been collected by certain people or agency, and statistically treated and the information contained in it is used for other purpose

A

Secondary data

70
Q

Ways to Present Qualitative Data

A

⇒ Pie charts
⇒ Bar charts (simple and clustered bar charts)
⇒ Relative frequency (percentage) table

71
Q

⇒ 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

A

Pie charts

72
Q

⇒ Place categories on the horizontal axis.

⇒ Place frequency (or relative frequency) on the vertical axis

A

Bar charts (simple and clustered bar charts)

73
Q

Ways to Present Quantitative Data

A
Histogram
Frequency polygons
Stem and leaf plot
Box and whisker plot
Scatter plot
74
Q

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.

A

Histogram

75
Q

essentially a line that connects the middle of each of the bars of the histogram, are also used extensively.

A

Frequency polygons and Ogive

76
Q

⇒ 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

A

Stem and leaf plot

77
Q

It is another way to display information when the objective is to illustrate certain locations in the distribution.

A

Box and whisker plot

78
Q

a good alternative or complement to a histogram and is usually better for showing several simultaneous comparisons

A

Box and whisker Plot

79
Q

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.

A

scatter diagram graphs

80
Q

Basic Biostatistics

A

♦ Measures of Central Tendency

♦ Measures of Dispersion

81
Q

To avoid biased reporting central tendency must be addressed collectively, based on all the three measures

A

mean, median, mode

82
Q

To avoid biased reporting central tendency must be addressed collectively, based on all the three measures

A

mean, median, mode

83
Q

middle value

♦ It is the second measure, is the middle number of a set of numbers arranged in numerical order

A

Median

84
Q

__ sensitive to outliers

__ is not sensitive to outliers

A

Mean, median

85
Q

When the data are highly skewed, the __ is usually preferred

A

median

86
Q

most frequently observed value(s).

A

Mode

87
Q

Measures of Dispersion

A
 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
Q

difference between the maximum and the minimum data values.

A

Range

89
Q

the sum of squared deviation from the mean

A

Variation

90
Q

average of the squares of the deviations

taken from the mean.

A

Variance

91
Q

square root of variance

A

standard deviation

92
Q

measure of precision of the population distribution

A

standard deviation

93
Q

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

A

Standard error

94
Q

quantifies scatter — how much the values vary from

one another

A

standard deviation

95
Q

quantifies how accurately the true mean of the

population

A

Standard error

96
Q

Quartiles are divided by the __

A

25th percentile, 50th percentile, and 75th percentile

97
Q

gives the cut-point for the lower 25% of the data set

A

Q1

98
Q

is the median.

A

Q2

99
Q

gives the cut-point for the upper 25% of the data set

A

Q3

100
Q

Also known as relative variability.

A

Coefficient of variation (CV)

101
Q

♦ 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.

A

Coefficient of variation (CV)

102
Q

Biostatistics Application in various fields of Clinical Research:

A
  • Epidemiology
  • Clinical trials
  • Population genetics
  • Systems biology