Surveillance and Epi Investigation Flashcards
1
Q
Basic stat measures for descriptive stats
A
Measures of frequency
Measures of central tendency
Measures central dispersion
Percentiles
2
Q
What are the measures of frequency
A
Ratio
Rates
Proportions
3
Q
Relationship between two groups, offers insights into proportion and connections inherent within the data
A
Ratio
4
Q
Occurrence of particular event within specified population during defined period, facilitate comparisons and trend assessments across different populations and time intervals
A
Rate
5
Q
Relative magnitude of a specific category or event within a larger context. Show distribution and relevance if events within a dataset
A
Proportion
6
Q
Percentage of people in population who have a specific condition at a particular moment
A
Prevalence
7
Q
How many new cases of a particular condition occur within a population during a set period of time
A
Incidence
8
Q
Number of deaths that happen within a population during a specific period of time
A
Mortality rate
9
Q
Percentage of people who were exposed to a risk factor and ended up developing the condition
A
Attack rate
10
Q
Average of the values in a data srt
A
Mean
11
Q
Middle value in a ranked data set
A
Median
12
Q
Mode
A
Value that appears most freq in a dataset
13
Q
Which measure of central tendency is impacted by outliers?
A
Mean
14
Q
Span between the smallest and largest value within a data set
A
Range
15
Q
Average square difference between each data point and the mean. It provides a comprehensive understanding of how individual values vary from the data set Central value
A
Variance
16
Q
Difference between an individual data point and the mean of the data set. It gives us a sense of how much each value deviates from the average
A
Deviation
17
Q
Distribution and values around the mean. It quantifies the extent to which data points deviate from the central value helping us gauge overall variability.
A
Standard deviation
18
Q
Set of methods that can be used for improving systems, processes, and outcomes
A
Statistical process control
19
Q
Variation that is inherent to the system
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Common cause variation
20
Q
Variation that is indicative of exceptional events or changes
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Special cause variation
21
Q
Best process control chart for less than 25 points
A
Run chart
22
Q
Best chart for 25-50 points
A
Control chart
23
Q
Evaluates whether differences observed in data are significant and not due to chance
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Hypothesis testing
24
Q
Provide range within which population parameters are likely to exist
A
Confidence intervals
25
Unveiled relationships between variables and forecast outcomes
Regression analysis
26
Compares means across multiple groups helping us discern more meaningful distinctions
Anova
27
What are the two types of hypotheses used in statistical testing?
H0- null hypothesis- there is no association or difference between groups or relationship between variables
Ha- alternative hypothesis- there is a difference between groups or relationship between two variables
28
Describe the one sample test
Compare a sample mean to a known population parameter
Determines if the sample mean is significantly different from the population mean
Used when analyzing a single groups data
29
Describe the two sample t-tests
Compares means between two independent groups
Independent samples t-test is an example
Determines if it means of the two groups are significantly different
30
What are the two types of t-test
Dependent samples t-test
Independent samples t test
31
Describe the dependent samples t test
It compares means of related pairs of data
32
Describe independent samples t-test
Compares means between two independent groups
33
Describe chi-square test
-Used for categorical data
- Test for association or Independence between categorical variables
- Compare observed and expected frequencies
34
When should you reject the null hypothesis
When the p-value is less than alpha. The alpha is reset by the researcher and is typically 0.05 or 0.01. the p-value is calculated from the data
35
Type 1 error
False positive
-Occurs when the null hypothesis is wrongly rejected
- Concluding an effect exist when it doesn't
- controlled by setting the significance level alpha before the test
- results in a p-value below alpha leading to the rejection of a true null hypothesis
36
Type 2 error or beta
False negative
- occurs when the null hypothesis is wrongly accepted
- failing to detect a true effect that exists
- controlled by sample size, affect size, and variability
- and results in a p-value above alpha leading to the acceptance of a false null hypothesis
37
Confidence interval
Range of values within which the true population parameter is likely it's a lie
38
Offer insight into the precision and reliability of an estimate
Confidence intervals
39
If the confidence interval includes the null value the result is..
Not statistically significant
40
If the confidence interval excludes the null value the result is..
Statistically significant
41
What does a wider confidence interval suggest
Higher uncertainty and less precise estimates
42
What does a narrower confidence interval suggest
Greater precision and higher confidence in the estimate
43
Describe regression analysis
Statistical technique used to model the relationship between one or more independent variables and a dependent variable
44
Linear regression
Models linear relationship between independent and dependent variables
Used when the relationship appears to be a straight line
45
Multiple regression analysis
Incorporates more than one independent variable
Accounts for multiple factors influencing the dependent variable
46
Logistic regression
Used when the dependent variable is binary with two possible outcomes
Models the probability of an event occurring
47
Analysis of variance (ANOVA)
Statistical technique used to compare means across multiple groups
Assesses whether there are statistically significant differences in means among different categories
48
What are the types of anova tests?
One Way anova and two-way anova
49
One Way anova
Compares means among three or more independent groups
Determines if there is a significant difference between at least one pair of groups
50
Two-way anova
Analyzes the effect of two independent variables on a dependent variable
Unveils interactions between the variables and their combined effects
51
Systemic and ongoing collection, analysis, interpretation, and dissemination of data on the occurrence and distribution of health related events in a population
Surveillance
52
Diseases, injuries, behaviors, or health-related indicators to identify patterns, trends, or changes within a population
Health related events (outcomes
53
Healthcare workers, patients, visitors, or specific at risk groups
Population
54
Surveillance can be used to improve..
Performance, patient safety, and infection prevention
55
Types of surveillance methodologies
Total house surveillance and targeted surveillance
56
Comprehensive monitoring of all health Care associated infections across the entire population of a healthcare facility. Provides broad and inclusive perspective enabling the identification and potential risk and patterns on a facility-wide scale.
Total house surveillance
57
Surveillance that narrows its focus to a specific care unit and heis. By concentrating on specific areas where risk may be higher or wear specific interventions are needed, this methodology allows for more precise assessments. This approach is especially valuable when allocating resources efficiently and addressing particular concerns.
Targeted surveillance
58
Measure that accurately captures and represents the specific concept of construct it is intended to measure
Valid
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Measurement produced same results when used repeatedly under consistent conditions
Reliable
60
Unusual aggregation, real or perceived, if health events that are grouped together in time and space
Cluster
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Increase in disease among specific population in geographic area during a specific period of time
Outbreak
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Cluster of positive microbiological results without actual clinical illness
Pseudo-outbreak
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Study of distribution and determinants of health related states among specified populations
Epidemiology
64
Difference between surveillance and epidemiology
Surveillance - continuous monitoring of specific health events and conditions
Epidemiology- studies the causes, distribution, and determinants of health and disease in populations
65
Goals of infectious disease epi
1. Prevention, control, and intervention strategies
2. Hypotheses
3. Associations between risk factors and disease
66
Parts of the epi triangle
Host, agent, env
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Epi models
Triangle model
Wheel model
Web of causation
Chain of infection
68
Statistical relationship between a risk factor and a disease, two variables tend to occur together more often than would be expected by chance
Associaition
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Cause and effect relationship between risk factor and diisease
Causation
70
Reasons for association
1. Artifactual (spurious)
2. Indirect (noncausal)
3. Causal
71
What causes artifactual associations?
Random error
Bias
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Chance influences presence of association between variables, with larger number of variables studies, likelihood of chance association increases
Random error
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Steps from errors in study design, data collection, or analysis
Bias
74
What type of artifactual associations can be controlled?
Bias
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Associations that arise from interplay of multiple factors within an epi study
Indirect associations
76
External fator related to exposure and outcome, creating a false appearance of association between the two
Confounding factor
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Ways to prevent confounders
Study design, statistical technique, and controlled experiment
78
Describe Koch's postulates
1. Organism must always be found with disease
2. Isolated and grown in pure culture
3. Same disease reproduced when inoculated with organism grown in pure culture into healthy host
4. Organism recovered from experimentally infected host
79
What are Hill's criteria of causation
Strength
Temporality
Analogy
Consistency
Plausibility
Coherence
Specificity
Biological gradient
Environmental evidence
80
Incidence of the disease should be higher in those who are exposed to the factor under consideration than those who are not exposed
Strength of association
81
The association should be observed in the numerous studies preferably by different researchers using different research methodologies
Consistency
82
The association between one factor and one disease is more likely to be causal
Specificity
83
Exposure to the hypothesized causal Factor must proceed onset of disease
Temporality
84
A dose response relationship between increased exposure to a factor and increase likelihood of disease
Biological gradient
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Plausible considering current knowledge
Biological plausibility
86
The association must be in accordance with other facts known about the natural history of the disease
Coherence
87
Adds considerable weight to the evidence supporting causal associations. These experiments can be animal studies or clinical trials
Experimental knowledge
88
It's similar associations have been shown to be causal, the association is more likely to be causal
Analogy
89
Chain of infection
Causative agent
Reservoir
Portal of exit
Mode of transmission
Portal of entry
Susceptible host
90
Biological, physical, or chemical agent capable of causing disease
Causative agent
91
Examples of causative agents
Bacteria, viruses, fungi, protozoa, and prions
92
Place where infectious agent can survive but may or may not multiply
Reservoir
93
Examples of reservoirs
Humans, animals, and the environment
94
The path by which an infectious agent leaves the reservoir
Portal of exit
95
Examples of the portal of exit
Respiratory tract, gastrointestinal tract, blood, skin and mucous membranes
96
Method by which the organism reaches acceptable host
Mode of transmission
97
Examples of modes of transmission
Droplet, airborne, vectors, contact
98
The path by which an infectious agent enters the host
Portal of entry
99
Examples of portal of entry
Respiratory tract, gastrointestinal tract, blood, skin / mucus membrane
100
Host has variable that modifies the risk of becoming infected and developing disease
Susceptible host
101
Examples of susceptible hosts
Humans and animals
102
Complete prevention of a disease before any manifestation of that disease occurs
Primary prevention
103
Examples of primary prevention
Vaccination and HIV prep
104
Early diagnosis and treatment
Secondary prevention
105
Examples of secondary prevention
TB skin testing
106
Deals with sequelae of disease, occurs after the disease as well established
Tertiary prevention
107
Example of tertiary prevention
Rehab for polio
108
When to implement primary prevention
Before exposure and preclinical stage
109
When to implement secondary prevention
Clinical stage
110
Went to implement tertiary prevention
Resolution stage which is either a problem resolved, return to health, chronic stage, or death
111
Correlation coefficient:
Interpretation of R
R= -1 perfect negative relationship
R= 0 no relationship
R= 1 perfect positive relationship
112
Risk of an event occurring in an exposed group to the risk of it occurring in an unexposed group
Relative risk
113
Calculation for relative risk
(number of cases in the exposed group/ total number in the exposed group) ÷ (number of cases in the unexposed group / total number in the unexposed group)
114
How to interpret relative risk
RR <1 risk of outcome was higher in the group without exposure. the risk factor and the disease are negatively associated. it appears to be a protective Factor
RR=1 risk of outcome was the same for both groups. There is no association found between the risk factor and the outcome.
RR >1 risk of outcome was higher in the group with exposure to the risk factor. The risk factor and the outcome are positively associated
115
How to interpret OR
OR<1: the odds ratio of exposure are higher among the controls, individuals with the outcome are less likely to have been exposed to the risk factor
OR=1: the odds of exposure are the same for both groups, no association was found between the outcome and having been exposed to the risk factor
OR>1: the odds of exposure are higher among the cases individuals with the outcome are more likely to have been exposed to the risk factor
116
If someone has the outcome what is the likelihood the test will be positive?
Sensitivity
117
Equation for sensitivity
Number of true positive results / by number of individuals with the outcome * 100
118
If someone does not have the outcome what is the likelihood that the test will be negative
Specificity
119
What is the equation for specificity?
Number of true negative results / number of individuals without the outcome * 100
120
If the test result is positive what is the likelihood that the person truly has the outcome?
Positive predictive value
121
If the test result is negative what is the likelihood that the person truly does not have the outcome?
Negative predictive value
122
What is the equation for positive predictive value
Number of true positive results ÷ number of individuals with positive test s * 100
123
What is the equation for negative predictive value
Number of true negative results / number of individuals with negative results * 100
124
What is the relationship between sensitivity and specificity?
Inverse relationship, as one increases, the other decreases
125
What happens to false negatives, NPV and specificity when sensitivity increase
Decreases false negatives
Increases NPV
Decreases specificity
126
What happens to false positives, PPV and sensitivity when specificity increases
Decreases false positives
Increases PPV
Decreases sensitivity
127
When prevalence of the disease is low it results in a ____ NPV
Higher
128
When prevalence of a disease is high, it results in a ____ PPV
Higher
129
Describe qualitative studies
- Explore social, behavioral, and contextual factors impacting health in a population
-enhanced epidemiology by uncovering nuanced health insights
130
Examples of contextual factors that impact health in a population
Susceptibility to illness
Severity of illness
Cost of carrying out behavior
Perceived threat
Cues to action
Health motivation
Perceived control
131
What are the four types of qualitative studies?
Narrative
Phenomenology
Grounded theory
Ethnography
132
Complex account of an individual's experience in the context of their social cultural and even institutional environments
Narrative
133
Biographical, autoethnographical, life history, or oral history methods can be applied to this type of qualitative study
Narrative
134
This type of study gathers experiences to identify shared meanings of a phenomenon
Phenomenology
135
This type of study aims for universal themes rather than individual uniqueness and reduces diverse experiences to a fundamental phenomenon
Phenomenology
136
Example of narrative study
Interviewing nurse and documenting her experiences after contracting Ebola during the Ebola epidemic
137
Examples of phenomenology
Desceibe patient experiences with surgical site infections
discerning patient preferences for bed baths
138
This type of study creates new theories and concepts based on participants experiences of process
Grounded theory
139
Unlike other qualitative approaches it doesn't just describe but aims to explain a process
Grounded theory
140
Process for grounded theory
Interview and then look through interviews to identify common themes
141
Example of grounded theory
Healthcare workers compliance with hand hygiene- complete interviews and observations identify themes to identify contextua factors like social norms and perceived barriers
142
This type of study aims to understand human behavior is and their meanings within specific cultures or subcultures
Ethnography
143
This type of study involves participant observation, interviews, field notes, and archival data to provide detail descriptions of daily experiences in a culture
Ethnography
144
Example of ethnographic study
Researcher immerses themselves within a nursing home to understand the experiences and behaviors of patients placed under isolation precautions due to contagious infections. They are able to observe interactions and engage with patients which helps them to gain insight into the lived realities of those navigating isolation protocols
145
Two types of quantitative studies
Observational and experimental clinical trials
146
In this study the investigator observes the exposure and disease status of the population
Observational study
147
In this study the investigator determines the exposure for the population
Experimental clinical trial
148
What are the two types of observational studies?
Descriptive and analytical
149
This type of observational study describes data and basic quantitative terms
Descriptive study
150
This type of observational study compares individuals with and without an outcome by the presence of one or more hypothesized risk factors
Analytical study
151
What are the types of descriptive studies?
Case reports and case series
152
What are the types of analytical studies?
Cross-sectional
Case control
Cohort
153
Detailed description of an individual patient's medical condition, symptoms, diagnosis, treatment, and outcomes
Case report
154
This type of descriptive study is used for rare or unusual medical cases
Case report
155
This type of descriptive study is typically written by healthcare professionals such as physicians, nurses, are medical researchers
Case report
156
This type of descriptive study is a detailed analysis and description of a group of patients who share similar characteristics or have experienced a similar medical condition or event
Case series
157
In this type of analytical study the exposure and outcome are collected from a population simultaneously. The study is done at a specific point in time providing snapshot of the population.
Cross-sectional study
158
What is the purpose of cross-sectional studies?
To find prevalence and generate hypotheses
159
What are the advantages and disadvantages of the cross-sectional study
Advantages- quick, inexpensive
Disadvantages- can only calculate prevalence not incidence, and temporal sequence cannot be determined
160
This type of analytical study compares individuals with an outcome to individuals without the outcome.
Case control
161
Is a case control study retrospective or prospective
Retrospective
162
What can be calculated from case control studies?
Odds ratio
163
What are the advantages of case control studies?
- data already available
- require relatively small number of subjects
- useful for rare diseases
- less expensive than cohort studies
- require less time
164
What are the disadvantages of case control studies?
- dependent on completeness of records
- difficult to select appropriate control group
- subject to recall bias
165
This type of study follows a group of individuals over a period of time. participants are classified into two groups based on their exposure status. These groups are then followed forward in time to determine if the outcome occurs.
Cohort study
166
What can be calculated from a cohort study?
Relative risk
167
What are the advantages of a cohort study
Typically less biased
Exposure precedes disease
Calculates incidence of disease
Carries more weight than case control
168
What are the disadvantages of a cohort study
More expensive
Lengthy follow up
Lost a follow-up
169
These studies are used to evaluate effectiveness of an intervention or treatment. Participants are randomly assigned to one of two or more groups. The treatment and control group that receives a placebo or standard treatment.
Experimental randomized control trial
170
Advantages of randomized control trials
Design minimizes bias
Best evidence for direct causal relationship between factor and outcome
171
Disadvantage of randomized controlled trial
Demanding
Expensive
Generalization is difficult
172
This type of study is used to identify, collect, analyze, and summarize evidence related to a specific research question
Systematic review
173
This type of study identifies gaps in research
Systematic review
174
What is the hierarchy of epidemiological study designs?
Cross-sectional
Case control
Cohort
Randomized control trial
Minute analysis of randomized control trials
