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Population Health > Test 1 > Flashcards

Test 1 Flashcards

1
Q

define practice guidelines

A

statements that have recommendations to optimize patient care and informed by a systematic review of evidence and an assessment of the benefits and harm of alternative care options

directs or principles presenting current or future rules of policies for assisting practitioners in patient decisions for diagnosis, therapy, or other circumstances
(can be developed by gov. agencies, institutions, professional societies, managed care orgs, or expert panels)

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2
Q

explain why practice guidelines are needed - CPG4

A

improve quality of health care (encourage/discourage use of therapies), direction for disease state treatment through evidence, reduce liability, identify alternative treatments, provide consistent treatment, decrease cost

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3
Q

discuss how practice guidelines are developed

A
  1. find topic
  2. define clinical question
  3. determine the criteria for evidence
  4. systematic liturature analysis
  5. syntheis of evidence prepared
  6. agree on procedures
  7. formulate grad recommendations
  8. draft and review panels evaluate draft
  9. approval of practice guidelines
  10. tool for implementation of guideline
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4
Q

level of evidence 1

A

systematic review or meta-analysis of al randomized control trials or evidence-based CPG based systematic reviews of RCT (original research- clinical trials)

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5
Q

describe how to locate practice guidelines

A 
PubMed
National_Guideline_Clearinghouse
agency for healthcare research and quality (AHRQ)
Cochrane Database of systematic reviews
Association Websites
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6
Q

selecting a topic for guideline development

A

high prevalence and/or severity of associated morbidity or mortality, availability of high-quality evidence for the efficacy of treatments that reduce morbidity and mortality, feasible implementation of treatment, potential cost-effectiveness, evidence that practice not optimal, evidence of practice variation, availability of personnel/expertise/resources

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7
Q

level of evidence 2

A

evidence from at least one well-designed RCT (indexing/abstracting services via PubMed, IPA)

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8
Q

level of evidence 3

A

evidence from a well-designed controlled trial without randomization (textbooks, review articles, monographs, practice guidelines - lexicomp/pharmacotherapy)

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9
Q

level of evidence 4

A

evidence from a well-designed case-control and cohort studies

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10
Q

level of evidence 5

A

evidence from systematic reviews of descriptive and qualitative studies

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11
Q

formulate and grade reccomendations

A

all recs should stand alone, be action oriented, and assigned a grade. recs referring to drugs should use generic name and avoid stating dosages, indicate where the rec refers to off-label use, tables used to present recommendations when it improves clarity, recommendations should take the pt into consideration and avoid the use of words such as subjects

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12
Q

peer review and pilot test

A

all guidelines undergo peer review and only members of the org may be included

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13
Q

current healthcare payment models in the US

A

heath care in the US is paid by: person’s out of pocket funds, private health insurance plans, government programs (such as medicare and medicaid)

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14
Q

Centers for Medicare and Medicaid Services (CMS)

A

value-based programs reward health care providers with incentive payments for the quality of care in medicare. triple aim strategy to reform how health care is delivered and paid for

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15
Q

why are we moving from volume to value-based healthcare system

A
  1. better care for individuals
  2. better health for populations
  3. lower costs
    “if you cannot measure it, you cannot improve it”
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16
Q

what are the seven CMS value-based programs

A
  1. end stage renal disease quality incentive program
  2. hospital value-based purchasing program
  3. hospital readmission reduction program
  4. physician value-based modifier
  5. hospital acquired conditions reduction program
  6. skilled nursing facility value-based program
  7. home health value based program
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17
Q

how can pharmacists engage in value-based programs

A
  • CMS value-based programs are not directly linked to pharm because they are not providers
  • CMS quality measures do focus on medication use
  • medication use measure demand engagement by pharmacists and pharmacies, health plans and PBM
  • PBMs health insurers can require metrics from pharmacy and reward outcomes with financial incentives
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18
Q

described PQA’s role in defining pharmacy’s engagement in value-based healthcare

A

optimizes health by advancing the quality of medication use, established in 2006 as public private-partnership with CMS shortly after adding Medicare part D prescription

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19
Q

financial impacts of CMS

A

pharmacists not universally established providers - only in some states can pharmacists get reimbursement for patient care under Medicaid

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20
Q

described electronic quality management systems

A
  • way to track product, patients, and outcomes in a single non-paper environment
  • there are many vendors and room to develop your own system based on systems by common databases like access and Sharepoint
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21
Q

single payer health insurance

A
  • one institution purchases all of the care
  • institution (government) does not pay the providers, own the hospitals or technology
  • France and US medicare
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22
Q

socialized medicine

A

institution (gov) owns the means of providing healthcare

  • gov does pay the providers, own the hospitals or the tech
  • the United Kingdom National Health Service (NHS) and the US veterans Health Admin
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23
Q

5 steps in PQA measure development process

A
  1. measure concept ideas
  2. measure concept development
  3. draft measure testing
  4. measure endorsement
  5. measure update
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24
Q

statistics

A

science concerned with developing and studying methods for collecting, organizing, summarizing, and interpreting empirical data

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25
Q

biostatistics

A

application of statistical principles to questions and problems in biology or health sciences

  • study characteristics of populations
  • handles uncertainty and variability
  • methods used for data reduction and inference
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26
Q

sample

A

simply a subset of the population or universe of interest and conveys information that is of administrative usefulness

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27
Q

population

A

a universe or population is defined as all observations (patients) or all theoretically conceivable observations concerning a phenomenon of interest

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28
Q

descriptive statistics

A

purpose: summarize the information in a collection of data
stats: frequency, graphs, central tendency, dispersion, distribution

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29
Q

inferential statistics

A

provide predictions about a population, based on data from a sample of that population
stats: parametric and non-parametric tests

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30
Q

categorical (qual)

A

data in which the classification of objects is based on attributes and properties EX: gender, ethnicity, race

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31
Q

numerical (quant)

A

type of data which can be measured and expressed numerically EX: age, weight, BMI

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32
Q

nominal

A

do not represent an amount or quant (ex: single vs married)

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33
Q

ordinal

A

represents and ordered series of relationship (ex: disease severity)

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34
Q

interval

A

measured on an interval scale having equal units but an arbitrary zero (temp)

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35
Q

ratio

A

variable units such as weight for which we can compare meaningful one weight vs another

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36
Q

parameter

A

value that describes population

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37
Q

variable

A

attribute or characteristic, or measure

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38
Q

tables

A

summarize data, absolute numbers/%/frequencies, goal of descriptive statistical techniques, construct frequency distribution

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39
Q

absolute frequency:

A

number of times a value appears, all of them for a set of data add up to the total number of population

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40
Q

relative frequency

A

dividing the absolute frequency of a value by the total number of data, all adds up to 1 or 100 if a percent

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41
Q

cumulative frequency

A

adding each frequency from a frequency distribution table to the sum of its predecessors. last number should be 100

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42
Q

pie charts

A

presents frequency distributions of nominal data, are of each category is proportional to the corresponding frequency

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43
Q

bar charts

A

present frequency distributions of ordinal or nominal data. horizontal axis: categories
vertical bars: height represents the frequency of observations within that class. bars equal width

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44
Q

histograms

A

present frequency of discrete or continuous data. variable of interest on horizontal axis. no natural separation between rectangles of adjacent classes

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45
Q

scatter plot

A

relationship between two continuous measurements.

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46
Q

box and whisker

A

summarizes data using median, upper and lower quartiles, extreme values. box shoes the quartiles of dataset. whiskers extend to show the rest of the distribution

47
Q

Measures of central tendency

A

statistical measure that determines a single value that accurately describes the center of the distribution and represents the entire distribution of scores. the goal of central tendency is to identify the single value that is the best representative for the entire set of data - 3 common measures are mean, median, mode

48
Q

sample mean

A

average of all the data values - add up all observations to obtain the sum and divide the number of observations

49
Q

weighted mean

A

mean computed by giving each observation a weight that reflects its relative importance
choice of weight depends on the application
ex: GPA, dollars
num: sum of weighted values / denom: sum of weights

50
Q

bimodal

A

two modes

51
Q

multimodal

A

more than two modes

52
Q

useful graphs to determine the mode

A

frequency table, bar chart, histogram, frequency polygons

53
Q

skewed data to the left

A

negative skewed data
skewness pulls mean in the direction of the tail
mean < median
median describes the center

54
Q

skewed data to the right

A

positive skewed data
skewness pulls mean in direction of the tail
mean > mode
median should be used to describe the center

55
Q

data symmetric

A

mean and median are about the same.

56
Q

outliers pull mean in their direction

A

large outlier mean > median

small outlier mean < median

57
Q

quartile measures 1, 2, 3

A

first quartile: Q = (n+1) / 4
second quartile: Q = (n+1) / 2
third quartile: Q = 3(n+1) / 4

58
Q

Interquartile Range (IQR)

A

Q3 - Q1

measure of variability not influenced by outliers or extreme values

59
Q

resistant measures

A

Q1, Q3, IOR that are not influenced by outliers

60
Q

simple variance

A

S^2, average of squared deviations of values from the mean

61
Q

standard deviation: for a sample

A

S, square root of the variance

62
Q

standard deviation for a population

A

square root of the population variance, denoted by σ

63
Q

the more the data are spread out the great the ___

A

range, variance, standard deviation

64
Q

if all of the values are the same, ____ will be zero

A

range, variance, standard deviation

65
Q

coefficient of variation

A 
measure of relative variation
always in a percentage
shows variation relative to mean
used to compare variability of 2 or more groups
CV= (standard dev / mean)
66
Q

formula for confidence interval

A

Point estimate +/- critical value x standard error

67
Q

formula for confidence interval

A

Point estimate +/- (critical value x standard error)

68
Q

critical value

A

table value based on the sampling distribution of the point estimate and the desired confidence interval. controlled by the choice of z or t score

69
Q

standard error

A

standard deviation of the point estimate (standard deviation / squr root sample size

70
Q

confidence interval when standard deviation is unknown

A

use t distribution instead of z. introduces extra uncertainty since S is variable from sample to sample

71
Q

hypothesis

A

used to determine whether a statement about the value of a population parameter should or should not be rejected

72
Q

null hypothesis

A

the claim you are trying to test

hypothesize that the population parameter is equal to some value Ho

73
Q

alternative hypothesis

A

the claim we are gathering evidence for
contradicts the null hypothesis
one of the two statements of null or alternative hypothesis must be true!

74
Q

summary forms of null and alternative hypothesis

A

null: pop mean > hypothesized pop mean
alternative: pop mean < hypothesized value
one tail - lower - tail (visa versa for the upper tail)

null: pop mean = hypothesized pop mean
alternative: pop mean does not = hypothesized value
two tailed

75
Q

t-test

A

used to hypotheses about mean when the population variance is unknown
(sample mean- hypothesized pop mean) / sample standard dev/ squr root sample size)

76
Q

z-test

A

used to determine whether two samples means are different when variance is known and sample is large (n>30)

77
Q

z-test

A

used to determine whether two samples means are different when variance is known and sample is large (n>30)

78
Q

type 1 error

A

type 1 error occurs when the null hypothesis is rejected when in fact it is true! probability denotes by alpha

79
Q

type 2 error

A

when null hypothesis is not rejected when it is, in fact false, denoted by beta

80
Q

measures of assosiation

A

used in clinical research to quantify the strength of association between variables, often an outcome and treatment or outcome and exposure
- relative risk, odds ratio, hazard ratio

81
Q

relative risk/risk ratio

A

ratio of risk of an event occurring in the exposed group vs the unexposed group
RR= risk exposed / risk unexposed
RR = (a / (a+b)) / (c/ (c+d))
a and b are positive for the disease, c and d are not

82
Q

RR < 1

A

exposure being considered is associated with a reduction in risk in the exposed group compared to the unexposed group (protective effect)

83
Q

RR = 1

A

(or close to 1) suggests no or little difference in the risk between exposed or unexposed (no effect)

84
Q

RR > 1

A

suggests an increased risk of the outcome in the exposed group compared to the unexposed group (“harmful” effect)

85
Q

odds ratio

A 
ratio of the odds of the disease in a group
OR - odds exposed / odds unexposed
OR = (a / b) / (c / d) = ad / bc
a and c are the positive for cancer
a and b are the exposed
86
Q

OR = 1

A

no association

87
Q

OR > 1

A

odds of disease in exposed are greater than in unexposed, possibly causal

88
Q

OR < 1

A

odds of disease in exposed are lower than in unexposed, possibly protective

89
Q

hazard ratio

A

measure of the effect of an exposure on an outcome of interest in two population or samples over time
- reported ini time-to-event or suvival analysis
- the outcome could be positive or negative
HR = hazard exposed / hazard unexposed

90
Q

HR = 0.5

A

at any particular time, half as many patients in the exposed group are experiencing an even proportionally to the unexposed group

91
Q

HR = 1

A

at any particular time, even rates are the same in both groups

92
Q

HR = 2

A

at an particular time, twice as many patients in the exposed group are experiencing an even proportionally to the unexposed group

93
Q

kaplan meir curves

A

product-limit estimate, makes a picture of survival often used to measure the fraction of patients living for a certain amount of time after treatment.

94
Q

P-value

A

probability computed using the test statistic that measures the support (or lack of support) provided by the sample for the null hypothesis

95
Q

P > 0.10

A

non-significant evidence against H

96
Q

0.05 < P < 0.10

A

marginally significant evidence for H

97
Q

0.01 < P < 0.05

A

significant evidence against H

98
Q

P < 0.01

A

highly significant evidence against H

99
Q

p-value < alpha

A

there is enough evidence against the null hypothesis to reject it

100
Q

p-value > alpha

A

we can say there is not enough evidence against the null hypothesis to reject it

101
Q

alpha

A

standard for how extreme the data is before we reject it

102
Q

epidemiology

A

science of public health and examines the distribution and frequency of disease in populations, as opposed to studying diseases at the individual level

103
Q

uses of epidemiology

A 
describe the health and risks of groups
identify causes (proximal and distal)
inform interventions and policies
evaluate programs and interventions
104
Q

counts

A

tells us how many events/ cases of the disease

pros;

105
Q

proportions

A 
tells us what fraction of the population is affected (ex: risk, prevalence, cumulative incidence) 
# of people impacted / total # of people
106
Q

rates

A

tells us how fast the disease is occurring in a population (incidence density/rate)

107
Q

counts pros/cons

A

pros: easy to understand, communicates public health importance
cons: hard to compare counts unless the denominator is the same across the comparison, doesn’t lend itself to many epidemiological or statistical analyses

108
Q

prevalence

A
  • number of existing cases (old and new) in the population (sick, healthy, at risk, not at risk)
  • focuses on the disease state and measures the proportion of the population who has the disease of interest
  • point prevalence provides a single snapshot of the population at one point in time
  • period prevalence provides a series of snapshots of a population within a specified period of time
109
Q

incidence proportion

A
  • measures the proportion of the population at risk that develops the disease of interest over a period of time
  • usually 1 year time period
  • measure of risk in a group of people
  • used in fixed populations
  • cumulative incidence or risk
    incidence = # of new cases of disease / total # at risk for developing disease in population
110
Q

incidence rate

A

incidence rate = # new cases / total person-time of observation

used when different individuals are followed for different lengths of time

  • used when there are losses to follow-up
  • numerator is the same as that of cumulative incidence
111
Q

person-time

A
  • amount of time at risk that people contribute, summed over every person in the population
  • contributed only by people at risk for the outcomes
    units can be person-years, person-months, person-days
112
Q

when does a person stop contributiong person time?

A

death, moving out of study population, when they develop the disease under study, loss to follow-up

113
Q

prevalence

A

= incidence rate X average duration

Population Health (3 decks)

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