Public Health Flashcards
1
Q
Systematic errorcan only be decreased by ______.
A
Eliminating the source ofbias
Whilerandom error (random errorsin data) can be decreased by repeating measurements and/or increasing the sample size.
2
Q
Random error (random errorsin data) can be decreased by ___________.
A
Repeating measurements and/or increasing the sample size.
3
Q
A test with a highprecisionwill have________.
A
Minimalrandom error
4
Q
Precisionimproves with _______.
A
Decreasedstandard deviationand increasedpowerof a statistical test.
5
Q
Interrater Reliability
A
The test yields the same results when performed by different researchers.
6
Q
Parallel-test Reliability
A
The reliability of a new test is compared with an established test.
The new test determines the reliability of a test in comparison to another test, the reliability of which has already been established.
Similar statistical results imply a similar degree of reliability.
7
Q
Test-retest Reliability
A
The test yields the same results when repeated on the same subjects.
8
Q
A test with high validity will have _________.
A
Minimal systematic error and bias.
9
Q
Internal Validity
A
Extent to which a study is free of error (most often in the form ofbias) and the results are therefore true for the study sample.
Highinternal validitycan be achieved by matching study groups according to age, sex, and other characteristics, and observing measures to reduce systemic errors (bias) to a minimum
10
Q
Highinternal validitycan be achieved by ________.
A
Matching study groups according to age, sex, and other characteristics, and observing measures to reduce systemic errors (bias) to a minimum
11
Q
External Validity
A
Refers to whether study results can be extrapolated from a sample population to the general population (generalizability).
A test has high external validity if results in the sample group reflect the actual figures (eg, prevalence) in the general population.
A study with highexternal validityresults can be reproduced in different sample groups and has highinternal validity
12
Q
A test has high external validity if __________.
A
Results in the sample group reflect the actual figures (eg, prevalence) in the general population.
13
Q
A study with _____external validityresults can be reproduced in different sample groups and has ____internal validity
A
high; high
14
Q
Berkson Bias
A
Individuals in sample groups drawn from ahospitalpopulation are more likely to be ill than individuals in the general population.
15
Q
Attrition Bias
A
Participants lost to follow up have a different prognosis than those who complete the study.
Most commonly seen in prospective studies.
Risk that the remaining participants differ significantly from those lost to follow up.
16
Q
Susceptibility Bias
A
One disease predisposes affected individuals to another disease, and the treatment for the first disease is mistakenly interpreted as a predisposing factor for thesecond disease.
17
Q
Survival biasalso known as__________.
A
Prevalence-incidencebiasandNeyman bias
18
Q
Survival bias (also known asprevalence-incidencebiasandNeyman bias)
A
When observed subjects have more or less severe manifestations than the standard exposed individual.
If individuals with severe disease die before the moment of observation, those with less severe disease are more likely to be observed.
If individuals with less severe disease have a resolution of their disease before the moment of observation, those with more severe disease are more likely to be observed.
Most commonly occurs incase-controlandcross-sectionalstudies.
19
Q
Sampling bias also known as __________.
A
Ascertainment bias
20
Q
Sampling bias (ascertainment bias)
A
Occurs when certain individuals are more likely to be selected for a study group, resulting in a nonrandomized sample.
This can lead to incorrect conclusionsbeing drawn about the relationship between exposures and outcomes.
Limitsgeneralizability.
Types ofsampling bias:
- Nonresponse bias → Nonresponder characteristics differ significantly from responder characteristics because nonrespondersdo not returninformation during a study (e.g., subjects do not return a call).
- Healthy worker effect → The working population is healthier on average than the general population. Severely ill individuals do not usually work, so any sample consisting of only subjects that work is not representative of the general population.
- Volunteer bias → Individuals who volunteer to participate in a study have different characteristics than the general population.
21
Q
Types ofSampling Bias (ascertainment bias)
A
- Nonresponse bias → Nonresponder characteristics differ significantly from responder characteristics because nonrespondersdo not returninformation during a study (e.g., subjects do not return a call).
- Healthy worker effect → The working population is healthier on average than the general population. Severely ill individuals do not usually work, so any sample consisting of only subjects that work is not representative of the general population.
- Volunteer bias → Individuals who volunteer to participate in a study have different characteristics than the general population.
22
Q
Nonresponse Bias
A
Nonresponder characteristics differ significantly from responder characteristics because nonrespondersdo not returninformation during a study (e.g., subjects do not return a call).
23
Q
Healthy Worker Effect
A
The working population is healthier on average than the general population.
Severely ill individuals do not usually work, so any sample consisting of only subjects that work is not representative of the general population.
24
Q
Volunteer Bias
A
Individuals who volunteer to participate in a study have different characteristics than the general population.
25
Solutions to Selection Bias (Ascertainment Bias)
1. Randomization
- Subjects are randomly assigned to the exposure and control groups to ensure that both groups are roughly equal in baseline characteristics (often displayed in a table, e.g., in randomized controlled trials).
- Controls for both known and unknown confounders
- Successful if possible confounding characteristics (e.g., socioeconomic demographics, family history) are approximately equally distributed between the exposure and control groups
2. Ensure the sample is representative of the population of interest (e.g., in case-control studies).
3. Ensure the correct reference group is chosen for comparison.
4. Collect as much data on the characteristics of the participants as possible.
5. Nonresponder characteristics should not be assumed. Instead, undisclosed characteristics of nonresponders should be recorded as unknown.
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Referral Bias
Results when patients are sampled from specialized medical centers and therefore they do not represent the general population.
27
Recall Bias
Awareness of a condition by subjects changes their recall of related risk factors (recall a certain exposure).
Common in retrospective studies.
Solution → reducing time to follow up in retrospective studies (e.g., retrospective cohort studies or case-control studies)
28
Recall Bias Solutions
Reducing time to follow up in retrospective studies (e.g., retrospective cohort studies or case-control studies)
29
Allocation Bias
Systematic difference in the way that participants are assigned to treatment and control groups.
Solution → randomization
30
Allocation Bias Solutions
Randomization
31
Late-look Bias
Sending a survey out to people diagnosed with a fatal illness five years after diagnosis will preferentially sample those with a low grade disease (or few comorbidities).
32
Types of Selection Bias
1. Berkson bias
2. Attrition bias
3. Susceptibility bias
4. Survival bias (also known as prevalence-incidence bias and Neyman bias)
5. Sampling bias (ascertainment bias)
33
Types of Information Bias
1. Measurement bias
2. Reporting bias
3. Interviewer bias
34
Information Bias
Incorrect data collection, measurement, or interpretation that leads to misclassification of groups or exposure.
Information is gathered differently between the treatment and control groups.
Insufficient information about exposure and disease frequency among subjects.
Solution → standardize data collection.
Types of information bias:
1. Measurement bias → any systematic error that occurs when measuring the outcome
2. Reporting bias: a distortion of the information from research due to the selective disclosure or suppression of information by the individuals involved in the study. Can involve the study, design, analysis, and/or findings. Results in underreporting or overreporting of exposure or outcome
3. Interviewer bias: Different interviewing approaches prompt different responses by interviewees, which results in researchers finding differences between groups when there are none.
35
Information Bias Solutions
Standardize data collection
36
Measurement Bias
Any systematic error that occurs when measuring the outcome
37
Reporting Bias
A distortion of the information from research due to the selective disclosure or suppression of information by the individuals involved in the study.
Can involve the study, design, analysis, and/or findings. Results in underreporting or overreporting of exposure or outcome
38
Interviewer Bias
Different interviewing approaches prompt different responses by interviewees, which results in researchers finding differences between groups when there are none.
39
Misclassification Bias
Results from an incorrect categorization of subjects regarding their exposure status, outcome status, or both.
40
Cognitive Bias
The personal beliefs of the subjects and/or investigators.
Solution → Use placebo, blinding and prolong the time of the observation to monitor long term effects.
Types of cognitive bias:
1. Response bias → Study participants do not respond truthfully or accurately because of the manner in which questions are phrased (e.g., leading questions) and/or because subjects interpret certain answer options to be more socially acceptable than others.
2. Observer bias (experimenter-expectancy effect or Pygmalion effect) → The measurement of a variable or classification of subjects is influenced by the researcher's knowledge or expectations.
3. Confirmation bias → The researcher includes only those results that support their hypothesis and ignores other results.
4. Placebo and nocebo effects → A placebo or nocebo affects subjects' preconceptions/beliefs about the outcome.
41
Cognitive Bias Solutions
Use placebo, blinding and prolong the time of the observation to monitor long term effects.
42
Types of Cognitive Bias
1. Response bias
2. Observer bias (experimenter-expectancy effect or Pygmalion effect)
3. Confirmation bias
4. Placebo and nocebo effects
43
Response Bias
Study participants do not respond truthfully or accurately because of the manner in which questions are phrased (e.g., leading questions) and/or because subjects interpret certain answer options to be more socially acceptable than others.
44
Observer Bias is also known as ___________.
Experimenter-expectancy effect or Pygmalion effect
45
Observer Bias (experimenter-expectancy effect or Pygmalion effect)
The measurement of a variable or classification of subjects is influenced by the researcher's knowledge or expectations.
46
Confirmation Bias
The researcher includes only those results that support their hypothesis and ignores other results.
47
Placebo and Nocebo Effects
A placebo or nocebo affects subjects' preconceptions/beliefs about the outcome.
48
Performance Bias
Researchers provide different levels of attention/care to different groups, or subjects change their responses when they become aware of which group they are allocated to.
For example:
1. Procedure bias → When patients or investigators decide on the assignment of treatment and this affects the findings. The investigator may consciously or subconsciously assign particular treatments to specific types of patients (e.g., one group receives a higher quality of treatment; patients in treatment group spend more time in highly specialized hospital units ). Solution → blinding
2. Hawthorne effect → subjects change their behavior once they are aware that they are being observed. Especially relevant in psychiatric research. This type of bias is difficult to eliminate. Solution → blinding
49
Procedure Bias
When patients or investigators decide on the assignment of treatment and this affects the findings.
The investigator may consciously or subconsciously assign particular treatments to specific types of patients (e.g., one group receives a higher quality of treatment; patients in treatment group spend more time in highly specialized hospital units).
Solution → blinding
50
Procedure Bias Solution
Blinding
51
Hawthorne Effect
Subjects change their behavior once they are aware that they are being observed.
Especially relevant in psychiatric research.
This type of bias is difficult to eliminate.
Solution → blinding
52
Hawthorne Effect Solution
Blinding
53
Cofounding Bias
Any third variable that has not been considered in the study but that correlates with the exposure and the outcome. A confounder can be responsible for the observed relationship between the dependent and independent variables.
Solutions to cofounding:
1. Perform multiple studies with different populations.
2. Randomization (randomized controlled trials)
3. Crossover study design
4. Restriction (epidemiology) → a study design in which only individuals who meet certain criteria are included in the study sample (e.g., only male individuals with a particular disease are included in a study to avoid the influence of gender on the exposure and outcome). Disadvantages → limit generalizability and makes obtaining a large sample group difficult
5. Matching (epidemiology) → A study design in which study participants are grouped into pairs with similar attributes . Commonly used in case-control studies to minimize confounding. Disadvantages → Does not completely eliminate confounding, can introduce confounding if the effect of the matching factor on disease occurrence cannot be studied anymore, can introduce bias
6. Stratified analysis
54
Solutions to Cofounding
1. Perform multiple studies with different populations.
2. Randomization (randomized controlled trials)
3. Crossover study design
4. Restriction (epidemiology) → a study design in which only individuals who meet certain criteria are included in the study sample (e.g., only male individuals with a particular disease are included in a study to avoid the influence of gender on the exposure and outcome). Disadvantages → limit generalizability and makes obtaining a large sample group difficult
5. Matching (epidemiology) → A study design in which study participants are grouped into pairs with similar attributes . Commonly used in case-control studies to minimize confounding. Disadvantages → Does not completely eliminate confounding, can introduce confounding if the effect of the matching factor on disease occurrence cannot be studied anymore, can introduce bias
6. Stratified analysis
55
Lead Time Bias
Early detection of disease is misinterpreted as increased survival (Lead time is the average length of time between the detection of a disease and the expected outcome).
Often discussed in the context of cancer screening.
Lead-time bias occurs when survival times are chosen as an endpoint of screening tests.
Solution → Measure the back-end survival by adjusting for the severity of the disease at the time of diagnosis. The gold standard for screening test effectiveness is to use mortality rates instead of survival times.
56
Lead Time Bias Solutions
Measure the back-end survival by adjusting for the severity of the disease at the time of diagnosis.
The gold standard for screening test effectiveness is to use mortality rates instead of survival times.
57
Length Time Bias
Apparent improvement in the duration of survival for a terminal disease with a long clinical course (e.g., slow-growing tumor).
Often discussed in the context of cancer screening.
Solution → Arrange patients according to the severity of the disease. Use a randomized controlled trial to allocate subjects into control and treatment groups.
58
Length Time Bias Solutions
Arrange patients according to the severity of the disease.
| Use a randomized controlled trial to allocate subjects into control and treatment groups.
59
Surveillance Bias
Outcome is diagnosed more frequently in a sample group than in the general population because of increased testing and monitoring.
Results in misleadingly high incidence and prevalence rates.
Solution → Compare the treatment group to an unexposed control group with a similar likelihood of screening. Select an outcome that is possible in both the exposed and unexposed groups.
60
Surveillance Bias Solutions
Compare the treatment group to an unexposed control group with a similar likelihood of screening.
Select an outcome that is possible in both the exposed and unexposed groups.
61
Effect Modification
A third variable influences the effect of an exposure on an outcome.
Occurs when the exposure has a different effect on the control and treatment groups.
Not considered a type of bias in itself, but rather a biological phenomenon (i.e., the exposure has a different impact in different circumstances).
Example: A certain drug works in children, but does not have any effect on adults.
Solution → stratified analysis. Stratifying participants into subgroups according to the third variable results in a stronger relationship in one subgroup. Stratified analysis helps to differentiate effect modification from confounding. When the population is stratified according to a factor, different results will be seen depending on whether it is a confounder or an effect modifier.
- Well-known examples of effect modification include:
1. Effect of estrogens on the risk of venous thrombosis (modified by smoking)
2. Risk of lung cancer in people exposed to asbestos (modified by smoking)
62
Effect Modification Solutions
- Stratified analysis.
- Stratifying participants into subgroups according to the third variable results in a stronger relationship in one subgroup.
- Stratified analysis helps to differentiate effect modification from confounding.
- When the population is stratified according to a factor, different results will be seen depending on whether it is a confounder or an effect modifier.
63
Cognitive biases can lead to diagnostic errors for example:
1. Confirmation bias → tendency of an investigator to favor results that support his or her hypothesis or preconceived notions, ignoring other results.
2. Anchoring bias → tendency to inappropriately rely on initial perception or information, which hinders later judgment when new information becomes available (e.g., favoring a diagnosis proposed earlier despite new evidence)
3. Availability bias → tendency to make judgments based on the availability of information in an individual's memory (e.g., when a physician makes a premature diagnosis that comes to mind easily and quickly due to having seen several patients with a similar clinical presentation)
4. Framing bias → the tendency to be influenced by how information is presented (e.g., the order of symptoms and/or emphasis placed on specific findings).
5. Visceral bias → a tendency for clinical decisions to be influenced by positive or negative feelings towards the patient (e.g., doubts regarding symptoms when described by a patient with substance use disorder)
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Confirmation Bias
Tendency of an investigator to favor results that support his or her hypothesis or preconceived notions, ignoring other results.
65
Anchoring Bias
Tendency to inappropriately rely on initial perception or information, which hinders later judgment when new information becomes available (e.g., favoring a diagnosis proposed earlier despite new evidence)
66
Availability Bias
Tendency to make judgments based on the availability of information in an individual's memory (e.g., when a physician makes a premature diagnosis that comes to mind easily and quickly due to having seen several patients with a similar clinical presentation)
67
Framing Bias
Tendency to be influenced by how information is presented (e.g., the order of symptoms and/or emphasis placed on specific findings).
68
Visceral Bias
Tendency for clinical decisions to be influenced by positive or negative feelings towards the patient (e.g., doubts regarding symptoms when described by a patient with substance use disorder)
69
Intention-to-Teat analysis
All patients who initially enrolled in the study, including drop-outs, are included in the analysis of study data.
Advantage:
- Allows the investigator or reader of the study to draw accurate conclusions regarding the effectiveness of an intervention
- Helps to reduce selection bias → Participants who are randomized are included in the analysis and analyzed according to the group they were originally assigned to.
- Preserves randomization (a large dropout rate may be influenced by the treatment itself. Intention-to-treat analysis preserves randomization by limiting the confounding effects of dropout due to treatment)
Disadvantages
- Noncompliance can lead to a conservative estimate of the treatment effect.
- Heterogeneity might be introduced (e.g., noncompliant, drop-out, and compliant subjects are analyzed together).
- Susceptible to type II error
70
Per-Protocol Analysis
Treatment and control groups are compared using data from only those study participants who adhered to the study protocol.
Advantages:
- Improves the estimate of the real effect of treatment under optimal conditions
- Can be used in noninferiority trials, phase 1 trials, and phase 2 trials
Disadvantages:
- Loss of randomization (increased selection bias)
- Overestimates the effects of the tested treatments (because it does not take drop-outs into account)
- Possibly a significant reduction in sample size
- Increases risk of bias
- Excludes participants that did not adhere to the protocol or were lost to follow up
71
Stratified Analysis
Stratifying participants into subgroups according to a third variable to eliminate confounding (e.g., by age, gender, race).
Evaluation of the association between exposure and disease is performed within each stratum (e.g., crude odds ratio).
Crude odds ratio or relative risk, as well as their confidence intervals, are used to measures the strength of the association (how different they are).
Eliminates confounding.
Example: evaluating the association between obesity and cardiovascular disease after stratifying by age.
72
Evidence Based Medicine
Practice of medicine in which the physician uses clinical decision-making methods based on the best available current research from peer-reviewed clinical and epidemiological studies with the aim of producing the most favorable outcome for the patient.
73
Levels of Evidence
Method used in evidence-based medicine to determine the reliability of findings from a clinical and/or epidemiological study.
Level I --> findings from at least one high-quality randomized controlled study.
Level III --> Expert opinions
(Level I --> most reliable; Level III --> least reliable).
74
Level I of Evidence
Findings from at least one high-quality randomized controlled study.
(Level I --> most reliable; Level III --> least reliable).
75
Level III of Evidence
Expert opinions
(Level I --> most reliable; Level III --> least reliable).
76
Grades of Clinical Recommendation (according to Evidence Based Medicine Guidelines)
System developed by medical societies, healthcare regulatory entities, and governments to rate clinical evidence and create guidelines for clinical practice based on medical evidence.
From A-D (A highly recommended; D not recommended)
I --> evidence is insufficience
77
Prevalence / (1 - Prevalence) =
Incidence rate (IR) × Average duration of the disease (T)
78
Incidence Rate (IR) =
[Prevalence / (1 - Prevalence)] / average duration of the disease (T)
79
Birth rate
Number of live births within a specific time interval
80
Fertility Rate
Rate of live births among women of childbearing age (15–44 years) in a population within a specific time interval
81
Morbidity
Number of individuals in a population with a disease at a specific point in time (i.e., the disease burden in a population)
82
Mortality
Number of deaths in a population within a specific time interval
83
Standard Deviation
How much variability exists in a set of values, around the mean of these values.
84
Standard Error
An estimate of how much variability exists in a (theoretical) set of sample means around the true population mean.
85
Affected by extreme outliers
Mean
86
Most resistant to outliers. Can be used to describe a qualitative data.
Mode
87
Positively skewed distribution (right-skewed distribution)
Data set has a peak on the left side and a long tail on the right (positive direction).
Mean > median > mode.
88
Negatively skewed distribution (left-skewed distribution)
Data set has a peak on the right side and a long tail on the left (negative direction).
Mean < median < mode.
89
In normal distribution (bell curve, Gaussian distribution) recommended measures of central tendency are _________; recommended measure of spread is __________.
Mean, median and mode; standard deviation.
90
In skewed (asymmetrical) distribution recommended measure of central tendency is the __________; recommended measure of spread is _________.
Median; range or interquartile range
(Standard deviation should be avoided because it requires the mean. The mean should be avoided as a measure of central tendency in skewed data sets)
91
Null hypothesis (H0)
The assumption that there is no relationship between two measured variables (e.g., the exposure and the outcome) or no significant difference between two studied populations.
92
Alternative hypothesis (H1)
The assumption that there is a relationship between two measured variables (e.g., the exposure and the outcome) or a significant difference between two studied populations. This hypothesis is formulated as a counterpart to the null hypothesis.
- Directional alternative hypothesis (one-tailed) → specifies the direction of a tested relationship. It states that one variable is predicted to be larger or smaller than null value.
- Non-directional alternative hypothesis (two-tailed) → only states that a difference exists in a tested relationship (does not specify the direction)
93
Directional alternative hypothesis (one-tailed)
Specifies the direction of a tested relationship. It states that one variable is predicted to be larger or smaller than null value.
94
Non-directional alternative hypothesis (two-tailed)
Only states that a difference exists in a tested relationship (does not specify the direction)
95
P-value
Probability that a statistical test leads to the false conclusion that there is a relationship between two measured variables (e.g., the exposure and the outcome) or that there is a significant difference between two studied populations.
Probability of obtaining test results at least as extreme as those observed during the test, assuming that H0 is correct.
Probability that the study results occurred by chance alone, given that the null hypothesis is true.
- If the p-value is equal to or less than a predetermined significance level (usually set at 0.05), the association is considered statistically significant (i.e., the probability that the result was obtained by chance is < 5%). The null hypothesis is rejected and the alternative hypothesis is accepted. If p value > 0.05, null hypothesis is not rejected and the results are not significant.
- It is not possible to prove H1 is true, but having a p-value that is lower than the significance level indicates that it is very unlikely that the H0 is correct.
96
If the p-value is equal to or less than a predetermined significance level (usually set at 0.05), the association is considered ________.
statistically significant (i.e., the probability that the result was obtained by chance is < 5%).
The null hypothesis is rejected and the alternative hypothesis is accepted.
97
If p value > 0.05, null hypothesis is ________.
not rejected and the results are not significant
98
It is not possible to prove H1 is true, but having a p-value that is _______ than the significance level indicates that it is very unlikely that the H0 is correct.
lower
99
Type 1 error (α)
The null hypothesis is rejected when it is actually true and, consequently, the alternative hypothesis is accepted, although the observed effect is actually due to chance (false positive error).
Stating that there is an effect or difference when none exists (H0 incorrectly rejected in favor of H1).
Significance level (type 1 error rate) → the probability of a type 1 error (denoted with “α”)
100
Significance level (type 1 error rate)
Probability of a type 1 error (denoted with “α”)
- The significance level is determined by the principal investigator before the study is conducted. The higher the significance level, the higher the likelihood that a difference between two groups is the result of chance.
- For medical/epidemiological studies, the significance level α is usually set to 0.05
- If p < α, then assuming H0 is true, the probability of obtaining the test results would be less than the probability of making a type I error. H0 is therefore rejected as false.
101
The higher the significance level, the _____ the likelihood that a difference between two groups is the result of chance.
higher
102
If p < α, then assuming H0 is true, the probability of obtaining the test results would be _____ than the probability of making a type I error. H0 is therefore ________.
less; rejected as false.
103
Type 2 error (β)
The null hypothesis is accepted when it is actually false and, consequently, the alternative hypothesis is rejected even though an observed effect did not occur due to chance (false negative error).
An existing effect is overlooked.
A small sample size increases the likelihood of a type 2 error.
Stating that there is not an effect or difference when one exists (H0 is not rejected when it is in fact false).
- Type 2 error rate → the probability of a type 2 error (denoted by “β”)
104
A small sample size _______ the likelihood of a type 2 error.
increases
Stating that there is not an effect or difference when one exists (H0 is not rejected when it is in fact false).
105
Statistical power (1-β)
The probability of correctly rejecting the null hypothesis (of making type II error), i.e., the ability to detect a difference between two groups when there truly is a difference
Complementary to the type 2 error rate
Positively correlates with the sample size and the magnitude of the association of interest (e.g., increasing the sample size of a study would increase its statistical power)
Positively correlates with measurement accuracy
By convention, most studies aim to achieve 80% statistical power.
Increase power and decrease β by increasing sample size, expected effect size and precision of measurement
106
Statistical power (1-β) positively correlates with ________.
Sample size
Magnitude of the association of interest (e.g., increasing the sample size of a study would increase its statistical power)
Measurement accuracy
107
Increase power and decrease β by _________.
increasing sample size, expected effect size and precision of measurement
108
Standard deviation (σ)
Describes the variability or dispersion of data in relation to its mean.
It is calculated by first calculating the mean. The mean is subtracted from each population data set value. Each difference is squared and added together. The total sum is divided by the total number of data set values - 1. The square root of this value is the standard deviation (σ).
The standard deviation is the square root of the variance.
σ = √variance
109
Variance
SDˆ2
110
Standard Error of the Mean
Estimate of how much variability exists in a (theoretical) set of sample means around the true population mean.
Demonstrates how accurate the estimate of the mean is likely to be.
Influenced by the standard deviation (e.g., a greater SD increases the chance of error) and the sample size (a smaller sample size will increase the chance of error)
SEM = standard deviation/√(sample size)
111
Standard error of the mean is influenced by ______.
the standard deviation (e.g., a greater SD increases the chance of error) and the sample size (a smaller sample size will increase the chance of error)
SEM = standard deviation/√(sample size)
112
Standard normal value (Z-score, Z-value, standard normalized score)
Enables the comparison of populations with different means and standard deviations
Standard normal value = (value - population mean) divided by standard deviation
Determines how many standard deviations an observation is above or below the mean
Z-score for a 90% confidence interval = 1.64
Z-score for a 95% confidence interval = 1.96
Z-score for a 97.5% confidence interval = 2.24
Z-score for a 99% confidence interval = 2.58
113
Confidence Interval
Provide a way to determine a population measurement or a value that is subject to change from a sample measurement.
Is the range of values that are highly likely to contain the true sample measurement
Confidence interval for sample mean = mean +/- Z (standard error of the mean) = mean +/- Z (standard deviation/√sample size)
A lower confidence level means that a greater degree of error is possible and the measurement is less reliable.
Small standard deviation → narrow confidence intervals
114
A lower confidence level means that a ______ degree of error is possible and the measurement is _____ reliable.
greater; less
115
Small standard deviation → ____ confidence intervals
narrow
116
_____ standard deviation → narrow confidence intervals
small
117
_________ confidence intervals between two groups may indicate that there is no statistically significant difference.
Overlapping
118
__________ confidence intervals between two groups signify that there is a statistically significant difference.
Nonoverlapping
119
If the confidence interval includes the null hypothesis, the result is ________ and the null hypothesis _______ rejected.
not significant; cannot be
120
If the 95% confidence interval of relative risk or odds ratio includes ____, the result is not significant and the null hypothesis cannot be rejected.
1.0
121
If the 95% confidence interval of relative risk or odds ratio includes 1.0, the result is _________ and the null hypothesis ________ rejected.
not significant; cannot be
122
If the 95% confidence interval of a difference between the means of two variables includes ___, the result is not significant and the null hypothesis cannot be rejected.
0
123
If the 95% confidence interval of a difference between the means of two variables includes 0, the result is ______ and the null hypothesis ______ rejected.
not significant; cannot be
124
A 95% confidence interval that does not include the null hypothesis corresponds to a p-value of ____.
0.05
125
A 95% confidence interval that __________ corresponds to a p-value of 0.05.
does not include the null hypothesis
126
A 99% confidence interval that does not include the null hypothesis corresponds to a p-value of ______.
0.01
127
A 99% confidence interval that __________ corresponds to a p-value of 0.01
does not include the null hypothesis
128
The null hypothesis (H0) is rejected (and results are significant) when...
95% CI for mean difference excludes 0
95% CI OR or RR excludes 1
CIs between two groups do not overlap
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The null hypothesis (H0) is accepted (and results are significant) when...
95% CI for mean difference includes 0
95% CI OR or RR includes 1
CIs between two groups do overlap
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Clinical Significance
Describes an important change in a patient's clinical condition, which may or may not be due to an intervention introduced during a clinical study
Vs. statistical significance → describes a true statistical outcome (i.e., that is determined by statistical tests) that has not occurred by chance
Statistical and clinical significance do not necessarily correlate (Ex. A certain drug may cause a small drop in blood pressure that is statistically significant. While statistically significant, this small change in blood pressure may not benefit the patient and be considered clinically insignificant)
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Pearson correlation coefficient (r coefficient)
Statistical measure of the strength and direction of a linear relationship between two variables expressed on a scatter plot.
It ranges anywhere from -1 to +1, where -1 represents a perfectly negative linear relationship and +1 represents a perfectly positive linear relationship.
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Spearman correlation coefficient
Nonparametric method to measure the correlation between two ranked variables (i.e., compares ordinal scale variables, not nominal scale variables). Due to this method, the Spearman correlation coefficient is less susceptible to the effect of outliers but is relatively imprecise, as it does not use all information from a data set.
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Coefficient of determination
rˆ2
Amount of variance in one variable that can be explained by variance in another variable
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Parametric Tests
Used to evaluate statistically significant differences between groups when the study sample has a normal distribution and the sample size is large (eg, ANOVA, t-test, Pearson correlation coefficient)
135
T-test
Calculates the difference between the means of two groups or between a sample and population or a value subject to change; especially when samples are small and/or the population or a value subject to change distribution is not known.
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One-sample t-test
The t-value can be classified according a table that lists t-values and their corresponding quantiles based on the number of degrees of freedom (df) and the significance level (α value).
Calculates whether a sample mean differs from the population mean (μ0)
Prerequisite → normal distribution
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Two-sample t-test
Calculates whether the means of two groups differ from one another
Involves converting two sets of values from two different groups into a single set of values by calculating the difference between the two sets of observations. This single set of differences can then be analyzed in a fashion similar to one sample t-tests.
Prerequisites
- Both sample groups are drawn from the same population and have the same (but unknown) variance.
- The difference between the observations in the two groups approximately follows a normal distribution.
Unpaired t-test (independent samples t-test)
- Two different groups are sampled at the same time
Paired t-test (dependent samples t-test)
- The same group is sampled at two different times
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Unpaired t-test (independent samples t-test)
Two different groups are sampled at the same time
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Paired t-test (dependent samples t-test)
The same group is sampled at two different times
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Analysis of variance (ANOVA)
Calculates the statistically significant difference between ≥ 3 independent groups by comparing their means (an extension of the t-test)
- One-way analysis of variance assesses 1 variable (e.g., the mean height of women in clinics A, B, and C at a given point in time, the variable is height).
- Two-way analysis of variance assesses 2 variables (e.g., the mean height of women and the mean height of men in clinics A, B, and C at a point in time, the variables are gender and height)
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Nonparametric Tests
Used to evaluate the statistically significant difference between groups when the sample has nonnormal distribution and the sample size is small (eg, Spearman correlation coefficient, Mann-Whitney U test, Wilcoxon test, Binomial test)
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Binomial Test
Examines whether the observed frequency of an event with binary outcomes (e.g., heads/tails, dead/alive) is statistically probable or not
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Categorical Tests
Used to evaluate the statistically significant difference between groups with categorical variables (no mean values) (eg, chi-square test, fisher exact test)
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Chi-square Test (Xˆ2 test)
Calculates the difference between 2 or more percentages or proportions of categorical outcomes.
Aims to determine how likely outcomes are to occur due to chance (used in cross-sectional studies)
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Fisher Exact Test
Calculates the difference between 2 or more percentages or proportions of categorical outcomes but, unlike a Chi-square test, is used when the study sample is small. Also aims to determine how likely it was the outcomes occurred due to chance.
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Histogram
Similar to a bar graph but displays data on a metric scale. The data is grouped into intervals that are plotted on the x-axis.
Useful for continuous data
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Box Plot
Quartiles and median are used to display numerical data in the form of a box.
Useful for continuous data
Shows the following important characteristics of data:
- Minimum and maximum values
- First and third quartiles
- Interquartile range
- Median
- Easily shows measures of central tendency, range, symmetry, and outliers at a glance
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Scatter Plot
Graph used to display values for (typically) two variables of data, plotted on the horizontal (x-axis) and vertical (y-axis) axes using cartesian coordinates, which represent individual data values
Helps to establish correlations between dependent and independent variables
Helps to determine whether a relationship between data sets is linear or nonlinear
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Informed Consent
A process (not just a document/signature) that requires:
Disclosure: discussion of pertinent
information (using medical interpreter, if
needed)
- Understanding: ability to comprehend (avoid jargon, individualize and relate to the patient)
- Capacity: ability to reason and make one’s
own decisions (distinct from competence, a
legal determination)
- Voluntariness: freedom from coercion and
manipulation
Patients must have a comprehensive understanding of their diagnosis and the risks/benefits of proposed treatment and alternative options, including no treatment, complications, morbidity, risk of death, types and risk of anesthesia.
Patient must be informed of their right to revoke written consent at any time, even orally.
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Competence
Legal assessment of a patient's ability to make decisions
Assessed by a court of law (physicians do not have the legal power to pronounce patients legally incompetent)
Questions of legal competence arise in the presence of reduced mental capacity (e.g., severe mental illness, intoxication, impulsive/constantly changing decisions, or decisions that are inconsistent with the patient's values)
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Exceptions to Informed Consent
Waiver—patient explicitly relinquishes the right of informed consent
Legally Incompetent—patient lacks decision-making capacity (obtain consent from legal surrogate) or no surrogate available
Therapeutic Privilege—withholding information when disclosure would severely harm the patient or undermine informed decision-making capacity
Emergency situation—implied consent may apply
Examination, treatment or quarantine to prevent epidemics
If patient decision to refuse treatment posses a safety risk to their own wellbeing and/or welfare of others (eg, in the event of severe psychosis, patients with active tuberculosis)
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Consent for Minors
A minor is generally any person < 18 years old (except Mississippi < 21 years; Nebraska and Alabama < 19 years). Parental consent laws in relation to healthcare vary by state. In general, parental consent should be obtained, but exceptions exist for emergency treatment (eg, blood transfusions, trauma, suicidal ideation) or if minor is legally emancipated (eg, married, self-supporting, or in the military).
A parent cannot refuse an emergency life saving intervention for a minor, not even for religious reasons.
If a parent continue to refuse treatment for a non emergent but fatal medical condition seek a court order
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Situations in which parental consent is usually not required
Sex (contraception, STIs, prenatal care— usually not abortion)
Drugs (substance use disorder treatment)
Emergency/trauma
Minor serving a sentence of confinement
If the parents of the patients are themselves minors the grandparents give consent
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General principles for exceptions to confidentiality
- Potential physical harm to self or others is serious and imminent
- Alternative means to warn or protect those at risk is not possible
- Steps can be taken to prevent harm
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Examples of exceptions to patient confidentiality (many are state specific)
-Patients with Suicidal/homicidal ideation
-Abuse (children, elderly, and/or prisoners)
-Duty to protect—state-specific laws that sometimes allow physician to inform or somehow protect potential victim from harm
-Patients with Epilepsy and other impaired automobile drivers
- Reportable Diseases (eg, STIs, hepatitis, food poisoning); physicians may have a duty to warn
public officials, who will then notify people at risk. Dangerous communicable diseases, such as TB or Ebola, may require involuntary treatment.
-Patients request physician to share information with another party
-Patient has suffered penetrating trauma from assault (gunshot wound, stab wound). In this case, law enforcement must be notified
-Patient is a minor and care does not involve sexual or addiction medicine
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Decision Making Capacity
Note that decisions made with capacity cannot be revoked simply if the patient later loses capacity.
Intellectual disabilities and mental illnesses are not exclusion criteria for informed decision-making unless their condition presently impairs their ability to make healthcare decisions.
Capacity is determined by a physician for a specific healthcare-related decision (eg, to refuse medical care).
Components (assessing capacity is of MASSIVE importance):
- Decision is not a result of Mental illness exacerbation
- Patient is ≥ 18 years of Age or otherwise legally emancipated
- Decision is not a result of altered mental Status (eg, delirium, intoxication)
- Decision remains Stable over time
- Patient is Informed and understands
- Decision is consistent with patient’s Values and goals
- Patient Expresses preferences
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Shared Decision Making
A model in which patients and physicians decide on the best treatment option together
Empowers patient, as it is based on the patient's personal values, cultural beliefs, and preferences
Results in better health outcomes and increases patient satisfaction
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Surrogate Decision Maker
May be appointed by patients (e.g., medical power of attorney), legally appointed (e.g., court-ordered guardian), or next of kin (if no advance directive exists).
The exact hierarchy of decision-making varies from state to state. However, it generally follows the following order:
1. A mentally competent patient capable of expressing his/her own decision
2. Advance healthcare directive
- Durable medical power of attorney: a document through which an individual designates a surrogate health care decision-maker in the event that he/she lacks decision-making capacity or competence
- Living will: should the durable medical POA and the living will be in conflict, the POA can override the living will only if such a decision is in line with the patient's most recently expressed wishes.
3. Next of kin--> Spouse, Adult child, Parent, Adult sibling, in about half of states: “close friend”
4. Ethics committee or legal consult
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Ethics committee or legal consult for a decision making when
- When no surrogate is available or there is ongoing dispute (i.e., between equal priority surrogates) regarding who takes precedence as surrogate.
- Legal action may be necessary to appoint a legal guardian if there is no surrogate available or there is ongoing dispute between equal priority surrogates.
- When there is ongoing disagreement (i.e., between equal priority surrogates) about treatment decisions that cannot be resolved
- When a physician determines that a surrogate's decision may go against the patient's best interests or preferred decision
- When a physician determines that a surrogate's decision is made to benefit a third party, rather than in the patient's best interests
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Directive Counseling
Ethically appropriate when only one treatment option is medically reasonable and has clearly superior evidence based support
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Assisted Decision Making
Occurs when a family member or a care giver helps the patient in making a medical decision (but does not make the decision for the patient)
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Substituted Judgement
Occurs when a surrogate decision maker makes a health care decision for an incapacitated patient based on the surrogate's knowledge of the patient wishes and values.
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Therapeutic Privilege
A physician determines that full disclosure would cause severe harm to the patient's severe psychological harm (e.g., following an unfavorable prognosis)
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Elements of an optimal error disclosure
- Clearly admit an error has occured
- State the course of events leading to and during the error, avoiding jargon
- Explain the consequences of the error, both immediate and long term (if necessary)
- Describe corrective steps and future preventative steps
- Express personal regret and apology
- Allow ample time for questions and continued dialogue
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Physician-Assisted Suicide
When a physician supplies a patient with the means to end their own life (e.g., a physician provides a patient with a lethal dose of morphine that the patient then self-injects).
Illegal in most state.
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Euthanasia
Active termination of a terminally ill patient's life by a physician to end suffering. (e.g., a physician injects a lethal dose of morphine).
Euthansia is illegal in the United States.
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Terminal Sedation
It is legal to adjust medical therapy accordingly to provide relief from pain and suffering in a patient with terminal illness, despite hastening the patient's dying process (e.g., increasing doses of morphine in a patient with metastatic cancer).
Legal and distinct from euthanasia in so far as the intent must be to relieve pain rather than bring about death, even though it may hasten the dying process.
Not an appropriate means of addressing primarily existential suffering, e.g., death anxiety.
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Principle of Double Effect
Ethical principle that legitimizes an act of good intent despite causing serious harm (e.g. self defense homicide or terminal sedation).
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Withdrawal of Care
Patients with capacity (or their surrogate decision-makers) have the right to refuse any form of treatment at any time, even if that would result in that patient's death.
Physicians should make an effort to understand the reasons behind the patient's decision for refusing treatment.
There is no ethical difference between withholding care and withdrawing care at a later time. The patient may refuse treatment before or request to stop treatment after its commencement.
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Treatment can be considered futile if:
- There is no evidence for the effectiveness of treatment
- If the intervention has previously failed
- If last-line therapy is failing
- If treatment will not fulfill the goals of care
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Hospitals must decline organs that are considered unsuitable:
- Sepsis
- HIV
- Poor organ function (e.g., patient died of acute renal failure, so kidneys would be refused)
- Hypothermia
- Patient > 80 years old
- Prolonged organ ischemia (e.g., patient found deceased at home)
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Death Criteria
Death can be diagnosed if a patient meets criteria for brain death or cardiopulmonary death. Only one of these conditions is required, although they may coexist.
- Brain death is is an irreversible, complete loss of function of the entire brain (including the brainstem), even if cardiopulmonary functions can be upheld by artificial life support
- Cardiopulmonary death is the absence of a spontaneous heartbeat in an asystolic patient.
A hypothermic patient must be warmed to normal body temperature before death can be diagnosed.
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Notifiable Diseases
HIV
Sexually transmitted infections (e.g., gonorrhea, chlamydia)
Hepatitis
Tuberculosis
Rabies
Meningococcal meningitis
Some forms of food poisoning (e.g., salmonellosis)
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Primordial Prevention
Actions that address environmental, socioeconomic, and behavioral risk factors that affect a population as a whole to prevent potential disease or injury.
Target → entire population.
Health promotion beginning in childhood to encourage positive and discourage negative lifestyle habits, mass education, legislation.
Examples → programs on food safety and nutrition guidelines, campaigns discouraging tobacco and drug use (e.g., smoke-free air laws in public buildings), building bicycle and sidewalks to promote physical activity.
Primordial prevention aims to prevent risk factors from developing in the first place, whereas primary prevention targets existing risk factors to prevent the onset of a disease.
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Strategies to encourage adherence
- Identify potential barriers (e.g., disease-related knowledge)
- A patient-centered approach
- Communication about treatment options
- Provide patient education (written instructions regarding medication, discuss medication side effects)
- Reminder system (regular follow-up visits. email reminder programs, electronic alerts, phone call reminders)
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Accountable Care Organization
- Coordinated network of doctors, specialists, and hospitals that are voluntarily enrolled
- Encourage health care providers to work together to achieve a higher quality of care at a lower cost.
- Patient and provider participation in an accountable care organization is completely voluntary.
- Cover medicare patients
- Enrollment of specialists on a voluntary basis
- Member costs varies (expected to reduce overall costs)
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Exclusive Provider Organization
- Network of doctors, specialists, and hospitals
- No primary care physician needed
- No coverage for out-of-network providers
- Specialists can be seen without a referral from a primary care physician.
- Low member costs
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Health Maintenance Organization (HMO)
- Least flexible but also lowest costs for members
- Network of doctors, specialists, and hospitals
- Primary care physician is the first contact person.
- No coverage for out-of-network providers, except emergency visits that are covered at in-network rates
- Referral needed from primary care physician to see a specialist
- Women have direct access to obstetric and gynecological care.
- Low member costs
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Preferred Provider Organization
- Network of doctors, specialists, and hospitals
- No primary care physician needed
- Coverage for out-of-network providers
- High out-of-pocket payments
- Specialists can be seen without a referral from a primary care physician.
- High member costs
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Point of Service (POS)
- Combination of HMO and PPO. Patients still have to go through a primary care physician (point-of-service)
- Network of doctors, specialists, and hospitals
- Primary care physician is the first contact person.
- Coverage for out-of-network providers
- High out-of-pocket payments
- Referral needed from primary care physician to see a specialist
- Moderate member costs
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Medicare Eligibility
- Individuals ≥ 65 years old who have worked (or their spouse) and paid Medicare taxes for at least 10 years and who are US citizens or have a permanent legal residence
- Patients with end-stage renal failure or amyotrophic lateral sclerosis
- Individuals with permanent disabilities irrespective of age
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Medicare Parts
- Part A → hospital care, hospice care for terminal patients, skilled nursing facility care (if services are needed daily after a minimum 3-day stay in a hospital)
- Part B → doctor’s fees, emergency department visits, diagnostic tests, rehabilitation
- Part C (Medicare Advantage Plan) → all services covered by parts A and B, plus a private insurance plan. “All in one” plan that allows people to enroll in a private health insurance plan approved by Medicare. Medicare pays other organizations, such as insurance companies, hospital systems, or managed care organizations, to provide care.
- Part D → prescription drugs (individuals can add part D to either of the programs)
The two main coverage options are Original Medicare (part A and part B) and Medicare Advantage (part C). Individuals also have the option of adding part D to their main coverage.
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Medicaid
Jointly funded by the state and federal governments
Eligibility → individuals with low income
Coverage → hospital care, laboratory tests, diagnostic tests (such as x-rays), doctors' visits, skilled nursing care, vaccinations, home health care
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Children's Health Insurance Program (CHIP)
Eligibility → uninsured children of families with low income, but not low enough to qualify for Medicaid
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Fee-for-Service
- Health care providers are compensated for each individual service provided (e.g., individual laboratory tests, imaging studies, procedures).
- Incentivizes health care providers to overtreat patients because compensation is based on the number of services provided
- Associated with high overall health care costs
- A fixed payment schedule with discounted prices can be negotiated between health care providers and payers (i.e., discounted fee-for-service).
- A health care organization is compensated with a fixed amount for all services provided for a clinically-defined episode of care (e.g., hip replacement, cholecystectomy). Payment is then distributed to the health care providers.
- Incentivizes health care providers to deliver efficient care (e.g., health care providers avoid unnecessary procedures)
- Carries the risk that patients will be undertreated because compensation does not rely on the quantity or quality of services provided for each clinically-defined episode of care (e.g., physicians may be pressured to prematurely discharge patients from the hospital because the compensation received for each admission is unaffected by an early discharge.
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Global Payment
- Health care providers are compensated with a single payment for all the services included in a single episode of care.
- Often used for nonurgent surgery, with the coverage extended to all the pre- and postoperative visits
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Capitation
- Health care providers are compensated a fixed amount per patient during each payment period, regardless of the actual amount of health care utilized by the patient.
- Incentivizes health care providers to deliver efficient care (e.g., cost-effective preventive health care to avoid larger downstream costs)
- Carries the risk that patients will be undertreated because compensation is not based on the quantity or quality of services provided
- Often used by health maintenance organizations (HMOs)
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Per Diem Payment
- A health care organization is compensated a fixed amount per patient per day for a specific care service provided, regardless of the actual costs involved in providing services for any particular patient.
- Often used for reimbursement of inpatient service → A hospital and payer can negotiate a fixed per diem rate for most routine inpatient services (e.g., common medical and surgical conditions) based on the average daily cost of admission.
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Eligibility for Hospice Care
- Estimated life expectancy < 6 months
- Patients are usually on Medicare, Medicaid, or private insurance plans.
- The patient (and family) has made the decision to stop curative or life-preserving treatment in order to maximize quality of life.
Not all treatment should be withdrawn. Antibiotics, for example, can still be given if the patient develops an infection.
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Adverse Reaction
Unintended, unexpected, or undesirable consequence of a correct and justified action
191
Adverse Event
Any harm resulting from medical management rather than an underlying disease.
An adverse event may or may not be the result of a medical error.
Can be preventable or nonpreventable
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Near Miss (Close Call)
Medical error that could have resulted in an adverse event but did not, either incidentally or due to a timely intervention (e.g., a nurse identifies that a doctor's order is incorrect)
193
Medical Error
Accidental failure to perform an action as intended (e.g., misdosing the appropriate drug) or accidentally performing the incorrect action (e.g., prescribing the wrong drug), which could potentially result in harm to the patient
- Individual error → medical error resulting from the failure of a single healthcare professional due to negligence
- Systems error → medical error resulting from a series of actions and/or factors in treatment or diagnosis, from flaws in technical and organizational design and/or decision-making, and from failure to recognize and mitigate hazards and risks in the healthcare setting
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Medical Malpractice
Negligent conduct on the part of a healthcare provider or performance of a medical task with an unreasonable lack of skill
- Negligence → failure to perform an action with the skill, care, and knowledge expected of a healthcare provider under reasonable circumstances. This includes medical ignorance (i.e., failing to have the necessary current medical knowledge to diagnose and treat a specific condition).
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Safety Culture
Culture that promotes safety awareness, develops and implements measures for the maintenance of a safe work environment, and ensures that individuals can openly express safety concerns
Improves the identification of errors
Event reporting systems facilitate internal and external monitoring through the collection of data on errors.
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Sentinel Event
Wholly unexpected or unacceptable adverse event that results in serious injury or death of a patient
The term “sentinel event” was developed by The Joint Commission, while the term “never event” was developed by the Agency for Healthcare Research and Quality. There is some overlap in the definitions of sentinel events and never events.
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Never Event
Serious adverse event that is clearly identifiable, causes serious injury or death, and is considered sufficiently preventable that it should never occur
Examples include injury, disability, or death due to the following:
- Wrong-site surgery
- Wrong-patient surgery
- Post-procedure retention of a foreign object in a patient
- Suicide, suicide attempt, or self-harm within a health care facility
- Using contaminated devices or medications
- Using medical devices for purposes other than the intended function
- Administering the wrong medication
The term “sentinel event” was developed by The Joint Commission, while the term “never event” was developed by the Agency for Healthcare Research and Quality. There is some overlap in the definitions of sentinel events and never events.
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Ameliorable Adverse Event
Unintended consequence of medical management that is unavoidable but could have been less severe if a different protocol were followed.
199
Latent Error
Typically caused by a failure of design and/or of the larger system in question (e.g., the hospital).
200
Domains of health care quality (STEEEP)
- Safe → Avoid or minimize risks and hazards that may lead to harm.
- Timely → Reduce delays in administering healthcare that may lead to harm.
- Effective → Provide evidenced-based healthcare and avoid services or treatments of doubtful benefit.
- Efficient → Minimize or avoid wasting medical equipment, time, and energy.
- Equitable → Provide equal care to all irrespective of gender, ethnicity, sexuality, and socioeconomic status.
- Patient-centered → Individualize treatment with respect for patient preferences, values, and needs.
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Structural Quality Measurement
Measures of the resources available to a healthcare facility (e.g., equipment, facilities).
For example, physician-patient ratio, number of beds, number of nutritionists available for diabetic patients
202
Process Quality Measurement
Measures of the performance of a health care system as it was planned.
For example, percentage of individuals who receive a particular preventive service (e.g., immunizations, cancer screening, HbA1c measurement) over a period of time
203
Outcome Quality Measurement
Measures of the final impact of service provided by a healthcare facility, including mortality and morbidity.
For example, average HbA1c of patients over a period of time, maternal mortality rates, rates of nosocomial infections
204
Balancing Quality Measurement
Measures of the impact of one system on another.
Given that resources are limited, it is important to consider whether the resources allocated to one system might be better utilized in another system.
For example, cost-benefit analysis (e.g., using number needed to treat) of hiring more nutritionists to educate diabetic patients
205
Pareto Chart
Bar graph with bars representing the frequency or cost of problems.
Used to identify the significance of individual problems.
Helps analyze how certain factors contribute to a variety of issues
206
Shewhart Chart (Control Chart)
Graphic representation in which data is plotted over time to determine if a perceived improvement in quality over time is statistically significant
207
Run Chart (Time Plot)
Line graph that plots data in a time sequence to analyze trends.
Used in healthcare quality improvement, for example, to analyze the impact of an intervention over time and to help determine whether the improvement is a random or a true trend.
Use minimal mathematical complexity (Unlike a Shewhart chart).
208
Convenience Sampling
Study population that is drawn from a population that is easy to reach.
Since the study population is not chosen at random, this type of study can lead to bias and may not be as effective as a randomized control trial.
209
SMART Criteria
Used to improve patient safety.
| Goals should be Specific, Measurable, Achievable, Realistic, and Timely.
210
Plan, Do, Study, Act Cycle (PDSA)
- Plan → Define an area that needs improvement and plan potential interventions or innovations to bring about a corrective change.
- Do → Test the new intervention.
- Study → Assess the impact of the intervention on quality of healthcare.
- Act → Implement the new intervention if the previous test showed a positive impact on healthcare quality or restart the cycle if the results were negative or had no significant impact.
211
Lean Process Improvement
Process of continually evaluating the methods used to identify and eliminate factors that use time, energy, and resources without improving patient outcome (e.g., avoiding unnecessary investigations)
212
Burnout
Overall exhaustion due to an excess of stress over an extended period of time
- Lack of motivation and interest
- Feelings of failure and helplessness
- Cynical and detached work attitude
- Impaired immune function
- Increased risk of medical errors due to decreased concern, reduced professional efficacy
213
Fatigue
- Chronic sleep deprivation
- Decreased energy and motivation
- Impaired cognitive function
- Increased risk of medical errors due to impairment in intellectual function
214
Active Error
Error at the direct level of contact between healthcare personnel and patients. Has an immediate impact on the patient.
For example, surgery on the incorrect site, wrong route of drug administration
215
Latent Error
```
Error that could contribute to patient harm.
Latent error(s) in conjunction with active error(s) can lead to an adverse event.
```
For example, flaws in hospital organization, implementation of new equipment without adequate staff training
216
Communication Error
Error in communication between the healthcare personnel and the patient as well as among healthcare personnel.
For example, errors in → history taking; explaining planned medical procedures to the patient; written communication (e.g., poor handwriting); verbal communication (e.g., using nonstandard terminology or jargon)
217
Diagnostic Error
Errors or delays in diagnosis.
For example, not ordering the required investigations, use of outdated tests, failure to adequately monitor clinical signs or laboratory studies
218
Treatment Error
Errors or delays in treatment.
For example, inappropriate medical procedures, incorrect administration of treatment, incorrect drug dosage or method of use, failure to provide treatment or respond to diagnoses in a timely manner
219
Preventive Error
Errors in prophylaxis.
For example, failure to implement appropriate prophylaxis, failure to provide adequate monitoring or follow-up treatment, failure in equipment and system maintenance
220
Medication Error
Errors in prescribing; errors in transcribing; errors in dispensing; errors in administration.
For example, failure to correctly transcribe drug names, dosages, routes of administration (e.g, misinterpretation of a trailing zero), incorrect drug dispensing due to an error related to medications that look or sound alike
221
Domains of Health Care Quality (STEEEP)
- Safe → Avoid or minimize risks and hazards that may lead to harm.
- Timely → Reduce delays in administering healthcare that may lead to harm.
- Effective → Provide evidenced-based healthcare and avoid services or treatments of doubtful benefit.
- Efficient → Minimize or avoid wasting medical equipment, time, and energy.
- Equitable → Provide equal care to all irrespective of gender, ethnicity, sexuality, and socioeconomic status.
- Patient-centered → Individualize treatment with respect for patient preferences, values, and needs.
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Structural Quality
Measures of the resources available to a healthcare facility (e.g., equipment, facilities).
For example, physician-patient ratio, number of beds, number of nutritionists available for diabetic patients
223
Process Quality
Measures of the performance of a health care system as it was planned.
For example, percentage of individuals who receive a particular preventive service (e.g., immunizations, cancer screening, HbA1c measurement) over a period of time
224
Outcome Quality
Measures of the final impact of service provided by a healthcare facility, including mortality and morbidity.
For example, average HbA1c of patients over a period of time, maternal mortality rates, rates of nosocomial infections
225
Balancing Quality
Measures of the impact of one system on another. Given that resources are limited, it is important to consider whether the resources allocated to one system might be better utilized in another system.
For example, cost-benefit analysis (e.g., using number needed to treat) of hiring more nutritionists to educate diabetic patients
226
Pareto Chart
A bar graph with bars representing the frequency or cost of problems.
Used to identify the significance of individual problems.
Helps analyze how certain factors contribute to a variety of issues
227
Shewhart Chart (Control Chart)
Graphic representation in which data is plotted over time to determine if a perceived improvement in quality over time is statistically significant
228
Run Chart (Time Plot)
A line graph that plots data in a time sequence to analyze trends.
Used in healthcare quality improvement, for example, to analyze the impact of an intervention over time and to help determine whether the improvement is a random or a true trend.
Use minimal mathematical complexity (Unlike a Shewhart chart).
229
Convenience Sampling
A study population that is drawn from a population that is easy to reach.
Since the study population is not chosen at random, this type of study can lead to bias and may not be as effective as a randomized control trial.
230
SMART Criteria
Used to improve patient safety.
| Goals should be Specific, Measurable, Achievable, Realistic, and Timely.
231
Plan, Do, Study, Act Cycle (PDSA)
- Plan → Define an area that needs improvement and plan potential interventions or innovations to bring about a corrective change.
- Do → Test the new intervention.
- Study → Assess the impact of the intervention on quality of healthcare.
- Act → Implement the new intervention if the previous test showed a positive impact on healthcare quality or restart the cycle if the results were negative or had no significant impact.
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Lean Process Improvement
Process of continually evaluating the methods used to identify and eliminate factors that use time, energy, and resources without improving patient outcome (e.g., avoiding unnecessary investigations)
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Burnout
Overall exhaustion due to an excess of stress over an extended period of time
- Lack of motivation and interest
- Feelings of failure and helplessness
- Cynical and detached work attitude
- Impaired immune function
- Increased risk of medical errors due to decreased concern
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Fatigue
- Chronic sleep deprivation
- Decreased energy and motivation
- Impaired cognitive function
- Increased risk of medical errors due to impairment in intellectual function
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Alert Fatigue
Tendency of health care providers to become desensitized to and subsequently ignore alerts prompted by clinical decision support systems due to the excessive number or limited clinical relevance of the alerts
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Active Error
Error at the direct level of contact between healthcare personnel and patients.
Has an immediate impact on the patient.
For example, surgery on the incorrect site, wrong route of drug administration
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Latent Error
Error that could contribute to patient harm. Latent error(s) in conjunction with active error(s) can lead to an adverse event. For example, flaws in hospital organization, implementation of new equipment without adequate staff training
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Communication Error
Error in communication between the healthcare personnel and the patient as well as among healthcare personnel.
For example, errors in → History taking; explaining planned medical procedures to the patient; written communication (e.g., poor handwriting); verbal communication (e.g., using nonstandard terminology or jargon)
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Diagnostic Error
Errors or delays in diagnosis.
For example, not ordering the required investigations, use of outdated tests, failure to adequately monitor clinical signs or laboratory studies
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Treatment Error
Errors or delays in treatment.
For example, inappropriate medical procedures, incorrect administration of treatment, incorrect drug dosage or method of use, failure to provide treatment or respond to diagnoses in a timely manner
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Preventive Error
Errors in prophylaxis.
For example, failure to implement appropriate prophylaxis, failure to provide adequate monitoring or follow-up treatment, failure in equipment and system maintenance
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Medication Error
Errors in prescribing; errors in transcribing; errors in dispensing; errors in administration.
For example, failure to correctly transcribe drug names, dosages, routes of administration (e.g, misinterpretation of a trailing zero), incorrect drug dispensing due to an error related to medications that look or sound alike
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Several elements of an optimal error disclosure
- Clearly admit an error has occurred
- State the course of events leading to and during the error, avoiding jargon
- Explain the consequences of the error, both immediate and long term (if necessary)
- Express personal regret and apology
- Describe corrective steps and future preventative steps
- Allow ample time for questions and continued dialogue
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Human Factors Design
Forcing functions (those that prevent undesirable actions [eg, connecting feeding syringe to IV tubing]) are the most effective. Equipment, process, method, or system design features that prevent error by forcing the best option by default. Most effective technique for minimizing adverse events because they inhibit a chain of action that causes or perpetuates error
Standardization improves process reliability (eg, clinical pathways, guidelines, checklists), efficiency, communication, and safety
- Standardized terminology →helps prevent communication errors (e.g., use of the chemical name of a drug rather than its generic name or brand name)
- Standardized abbreviations and writing styles → help prevent communication and transcribing errors. Tall man lettering → a strategy used to prevent dispensing errors related to medications that look or sound alike (e.g., buPROPion vs. busPIRone)
- Standardized medication reconciliation → performed during transitions of care (e.g., admission, transfer, discharge) in which new medication orders are compared to past medication orders. Used to avoid medication errors such as duplication, omission, dosing errors, and drug interactions
- Standardized handoff → exchange of predefined items of information between health care providers when passing on responsibility for the care of patients (e.g., at the end of a shift) to reduce the risk of medical errors. Should include the patient's diagnoses, code status, a summary of the hospital course, current medications, recent changes to management, explicit suggestions for anticipated concerns, tasks to complete
- Time out → a perioperative pause conducted by the surgical team to confirm the patient's identity, surgical site, and planned procedure. Intended to prevent harm from a wrong procedure at the wrong site or patient.
- Simplification reduces wasteful activities (eg, consolidating electronic medical records). Reduction of complexity of equipment, systems, and processes to increase efficiency and reduce the risk of error
Effective communication
- Closed-loop communication → a communication method in which the receiver of information repeats that information back to the sender, upon which the sender confirms that the information has been received as intended (used to avoid communication errors)
- Patient involvement → prior to administering drugs or inducing anesthesia, the patient is asked for verification of identity and any further relevant information (e.g., site of surgery, planned procedure, consent)
- Computerized order entry (CPOE) → a system in which electronically placed orders for medications, tests, procedures, and consults are directly transferred to the recipient (used to prevent errors due to poor handwriting or ambiguous abbreviations)
Morbidity and mortality reviews (M&M) → a standardized process wherein departmental meetings are held to review complications and deaths that occur in hospitals to improve safe clinical practice and avoid preventable errors.
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Closed-Loop Communication
Communication method in which the receiver of information repeats that information back to the sender, upon which the sender confirms that the information has been received as intended (used to avoid communication errors)
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Root Cause Analysis
Retrospective analysis performed after a medical error has occurred in order to identify the (root) causes of the error and implement measures to prevent recurrence.
Procedure:
1. Identify the medical error. (“What happened?”)
- Retrospective analysis of all possible factors that could have led to the error with the support of patient documentation, the equipment/drug used, and the environment the patient was in. If feasible, the patient should also be interviewed.
- Examples include → slippery floor, infectious ward, nonquarantined patient, management protocol used
2. Identify what could have prevented the error from occurring. (“Why did it happen?”)
- Tool used → Fishbone diagram, also called Ishikawa diagram or cause and effect diagram. Used to break down and display all potential events that led to the error
3. Implement preventive measures. (“What can be done to prevent recurrence?”). Examples include → updating technology, employing double checks, using checklists, staff education on new policies
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Failure Mode and Effects Analysis (FMEA)
Prospective analysis to anticipate potential risks and hazards and proactively implement measures to prevent a medical error from occurring.
Procedure:
- Identify what could go wrong and what impact that could have (failure mode).
- Identify why it could go wrong (failure causes).
- Identify the outcomes of potential failures (failure effects).
- Prioritize the hypothetical failures by their chance of occurrence and severity of outcome.
- Proactive implementation of corrective measures
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Causal criteria (Bradford-Hill criteria)
List of criteria that helps to establish causality in epidemiological studies
- Strength of association (effect size) → a quantitative measure of the degree of relationship between two variables. The stronger association, the more evidence for causation.
- Dose-response relationship (biological gradient) → tests whether greater exposure usually leads to a higher occurrence of the outcome (e.g., the greater the exposure to ionizing radiation, the higher the risk of malignancy)
- Temporality → tests whether the outcome occurs after the effect (e.g., surgical site infection occurs after incision of the skin)
- Reproducibility (consistency) → The strength of association found between two factors increases when similar findings are observed in different studies (e.g., in different places, with different sample sizes). Repeated observations of the findings in multiple distinct samples.
- Specificity → It is more likely that a causal relationship exists between a factor and an effect when a specific disease occurs in a specific population at a specific time (e.g., there is an increase in the number of leukemia cases in a small town after a chemical factory is built nearby).
- Biologic plausibility → The relationship between an exposure and an outcome is usually consistent with current biological and medical knowledge (e.g., carcinogens in cigarettes cause lung cancer, and water molecules do not).
- Coherence (epidemiology) → tests whether new evidence is in agreement with previously established evidence. If there is a contradiction with previously established results, this reflects negatively on the likelihood of a causal relationship between a given set of factors (e.g., an epidemiological study that finds a correlation between higher rates of lung cancer in men while there is no available biological data to support it).
- Experimental evidence → data drawn from provisional experimentation (e.g., a distance of at least 2 meters between people is correlated with a decrease in the number of COVID-19 cases). Empirical evidence supporting the presumed cause and effect
- Analogy (epidemiology) → When there is strong evidence of a causal relationship between an exposure and an outcome, there is a greater likelihood of a causal relationship between another similar exposure and outcome (e.g., when one class of medication is known to produce an effect, it is likely that another agent of that class produces a similar effect).
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Reverse Causality
Association between exposure and outcome that is different than common presumption (e.g., people assume that low socioeconomic status causes schizophrenia, but in fact, schizophrenia causes a decline in socioeconomic status over time)
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Patient-Centered Interviewing Techniques
- Introduction
- Agenda setting
- Reflection (actively listen and synthesize information offered by the patient, particularly with respect to primary concern(s))
- Validation
- Recapitulation
- Facilitation (encourage the patient to speak freely without guiding responses or leading questions. Allow the patient to ask questions throughout the encounter)
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Expressing Empathy
- Partnership → reassure the patient that you will work together through difficult times, and offer appropriate resources.
- Empathy → acknowledge the emotions displayed and demonstrate understanding of why the patient is feeling that way.
- Apology → take personal responsibility when appropriate, or offer condolences for the patient’s situation
- Respect → commend the patient for coming in to discuss a problem, pushing through challenging circumstances, keeping a positive attitude, or other constructive behaviors.
- Legitimization → assure the patient that emotional responses are understandable or common.
- Support → offer to help the patient through difficult times.
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Delivering Bad News
- Setting → Offer in advance for the patient to bring support. Eliminate distractions, ensure privacy, and sit down with the patient to talk.
- Perception → Determine the patient’s understanding and expectations of the situation
- Invitation → Obtain the patient’s permission to disclose the news and what level of detail is desired.
- Knowledge → Share the information in small pieces without medical jargon, allowing time to process. Assess the patient’s understanding.
- Emotions → Acknowledge the patient’s emotions, and provide opportunity to express them. Listen and offer empathetic responses.
- Strategy → If the patient feels ready, discuss treatment options and goals of care. Offer an agenda for the next appointment.
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Hand Disinfection
Decreases transient skin flora more effectively and dehydrates the skin to a lesser extent compared to handwashing.
Although the lipids on the stratum corneum are emulsified by alcoholic preparations and are thereby released from their structure, they remain on the skin as long as the hands are not subsequently rinsed.
Disinfection → minimum contact time: 30–60 seconds
Indication:
Before and after contact with each patient
Before work, before and after breaks, as well as before (self-protection) and after going to the bathroom.
Before handling medication, syringes, and infusions
After removing contaminated gloves
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Contact Precautions
Used for the care of patients with drug-resistant pathogens (e.g., MRSA, VRE), enteric infections (e.g., Clostridium difficile, Escherichia coli O157:H7), scabies, impetigo, and draining abscesses
Includes performing hand hygiene and wearing gloves and gowns when getting into the patient's room (even when direct contact with the patient or infected material is not expected).
Patients should be kept in isolation or in cohort (keeping those with similar illness in one room)
Medical equipment should be dedicated to a single patient. If not possible, disinfect before reuse.
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Droplet Precautions
Used for the care of patients with suspected or confirmed infection with pathogens that spread with droplets, such as Neisseria meningitidis, Bordetella pertussis, Influenza, Parainfluenza, Adenovirus, Haemophilus influenzae type b, Mycoplasma pneumoniae, and Rubella
Patients should be kept in isolation or in cohort.
Includes wearing masks within a distance of 3– 6 feet from the patient and masking patients during transport
Implement hand hygiene after contact with respiratory secretions.
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Airborne precautions
Used for the care of patients with suspected or confirmed tuberculosis, measles, varicella, smallpox, and severe acute respiratory syndrome (SARS) infections
Patients should be kept in a private room with negative air pressure (the door of the isolated room must remain closed).
Individuals must wear a respirator when entering the room.
Minimize transport of patients and mask them if it is mandatory.
Implement hand hygiene after contact with respiratory secretions.
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Prevention of catheter-associated urinary tract infections
Avoiding unnecessary catheterization
Using sterile technique during catheter placement
Cleaning the catheter surrounding area with soap and water suffices for maintenance.
Prompt removal when the catheter is no longer needed
Using clean intermittent catheterization in patients with neurogenic bladder (in this technique, the catheter is immediately removed after bladder drainage and gets either discarded (single-use catheter) or cleaned (reusable catheter))
Use of antibiotic-coated catheters or prophylactic antibiotics should be avoided as it might lead to the development of drug-resistant pathogens.
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Prevention of intravascular catheter-related infections (e.g., central venous line infection)
Implementing hand hygiene and strict aseptic technique during insertion
Using a cap, mask, long-sleeved sterile gown, sterile gloves, and a sterile full-body drape
Preparing skin with chlorhexidine and alcohol before inserting the catheter
Systemic anticoagulation and antibiotics may be considered in oncology patients who require long-term central venous access.
Changing dressings regularly
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Prevention of ventilator-associated infections
Follow ventilator bundle protocol (a set of 4 standardized practices to prevent ventilator-associated infection)
Elevate head of the bed to 30–45°.
Assess readiness of extubation daily.
Interrupt sedation daily (sedation vacation).
Administer peptic ulcer and deep vein thrombosis prophylaxis.
Use noninvasive ventilation where possible.
Place tubes through the mouth rather than nose, if possible.
Maintain proper patient oral hygiene (e.g., use of oral chlorhexidine).
Consider antibiotic prophylaxis in patients that underwent emergency intubation.
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Prevention of surgical site infections
Delay elective procedures until all infections, even those remote from the surgical site, are treated.
In patients with planned surgery
- Ask the patient to cease smoking at least one month before surgery.
- Ask the patient to bathe or shower the night before the operation.
Skin preparation routine in the operating room should include an alcohol-based agent.
Parenteral periprocedural antimicrobial prophylaxis
- Should not be given routinely, except after cesarean deliveries
- If given, antimicrobial prophylaxis should to be timed to ensure appropriate concentrations at the time of incision.
- In clean and clean-contaminated procedures, no additional antimicrobial prophylaxis should be given after the incision has been closed.
No topical antimicrobial agents should be applied to the incision.
Blood glucose should be monitored; target levels during surgery are < 200 mg/dL.
Ensure normothermia.
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Range
The difference between the largest and smallest value in a data set. For example, in the data set “27, 3, 4, 9,” the range is 24 (i.e., 27-3).
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Interquartile range
The range from the second to the third quartile. Calculated by establishing the difference between the 75th and 25th percentile.
Less influenced by extreme data values (outliers).
