Public Health Flashcards
Study design where the frequency of disease and frequency of risk related factors are assessed in the present.
Cross sectional study - “What is happening”
Disease prevalence but does not assess causality
Study design that compares a group of people with disease to a group of people without disease. Looks to see if odds of prior exposure or risk factor differs by disease state
Case-control study - “what happened”
ODDs ratio
ex) people with COPD had higher odds of a smoking history than those without COPD
Study design that compares a group with a given exposure or risk factor to a group without such exposure. Looks to see if exposure or risk factor is associated with later development of disease.
Cohort study - “Who will develop the disease” or “who developed the disease”
Can be prospective or retrospective
Relative risk
ex) smokers have a higher risk of developing COPD than nonsmokers
Clinical trial phases: phase I
think “SWIM”
Phase I -“is it SAFE”. Small number of healthy volunteers or pt with dz of interest. Assess safety,toxicity, pharmkinetics, pharmdynamics
clinical trial : phase II
think “SWIM”
Phase II- “Does it WORK”. Moderate numbers of patients with disease of interest. Assess treatment efficacy, optimal dosing, and adverse effects
Clinical trial: phase III
think “SWIM”
Phase III- “IMPROVEMENT?” . Large number of pt with placebo and then with treatment. Compares new treatment to current standard of care
Clinical trial: Phase IV
think “SWIM”
Phase IV: “Can it stay in MARKET”. This is after being approved. Detects rare or long term effects
Sensitivity
True positive rate. Probability that when a dz is present then the rest is positive. SCREENING test
If negative then rules OUT a disease. Higher sensitivity has a lower false negative rate.
TP/(TP + FN) = 1- FN rate
Specificity
True negative rate. Probability that when the disease is absent the test is negative. CONFIRMATION test
When positive it rules IN a disease. Higher specificity means lower false positive rate
=TN/(TN+FP) = 1-FP rate
positive predictive value
probability that a person who has a positive test result actually has the disease
PPV= TP/(TP + FP)
varies directly with pretest probability. High pretest probability results in a high PPV
negative predictive value
probability that a negative rest actually does not have the disease
NPV=TN/(TN+FN)
varies indirectly with pretest probability. high pretest probability results in low NPV
Likelihood ratio
Likelihood that a given result would be expected in a patient with the target disorder compared to the likelihood that the same result would be expected in a patient without the target disorder
LR+ >10 and or LR- 0.1 indicates a useful diagnostic test
LR+=sensitivity/(1-specificity)=TP rate/FP rate
LR-=1-sensitivity/specificity= FN rate/TN rate
Odds ratio
Odds of a certain exposure given an event vs the odds of exposure in the absence of that event. Used in case control studies
OR=ad/bc
Relative risk (RR)
the # times risk of cancer in the exposed vs the unexposed. Used in cohort studies
RR=risk in exposure/risk in unexposed
RR=((a/a+b)/(c/c+d))
RR=1 there is not association between exposure and disease
RR>1 there is an association that causes increase in dz
RR<1 there is an association that causes decrease in dz
Attributable risk (AR)
The difference in risk between exposed and unexposed groups
Risk in exposed - risk in unexposed
AR=(a/a+b)-(c/c+d)
Relative risk reduction (RRR)
The proportion in risk reduction attributed to the intervention as compared to a control
RRR=1-RR
Absolute risk reduction (ARR)
The difference in risk (not the proportion) attributable to the intervention as compared to a control
ARR=((c/c+d) - (a/a+b))
Number needed to treat (NNT)
Number needed to treat for 1 patient to benefit
low number is better
NNT=1/ARR
Number needed to harm (NNH)
number needed to be exposed for 1 patient to be harmed
high number is better
NNH=1/AR
Incidence
new cases/#at risk
prevelaence
existing cases/total#of people in a population
increase prevalence causes increase in PPV and decreases in NPV
Precision
reliability
The consistency and reproduciblity of a test. The absence of random variation in a test
Increase precision causes decrease in standard deviation
increase in precision causes increase in statistical power
Accuracy
validity
systemic error decreased accuracy in a test
Berkson bias
study population selected from hospital is less healthy than general population
type of selection bias
Non response bias
participating subjects differ from non respondents in meaningful ways
type of selection bias
Hawthorne effect
participants change behavior upon awareness of being observed
measurement bias
use placebo group to avoid
Procedure bias
avoid with blinding and use of placebo
Observer expectancy bias
avoid with blinding and use of placebo
Lead time bias
early detection is confused with increased survival
avoid by measuring back end survival (Adjust survival according to the severity of disease at the time of diagnosis)
Length time bias
Screening test detects diseases with long latency period while those with shorter latency period become symptomatic earlier
i.e. a slowly progressive cancer is more likely detected by a screening test than a rapidly progressive cancer
prevent via a randomized controlled trial
Standard error
an estimate of how much variability exists in a theoretical set of sample means around the true population mean
SD/sq root(sample size)
variance formula
SD^2
Bimodal distribution
suggests two different populations
Positive skew
mean>median>mode
long tail on right
Negative skew
mean
Normal distribution
mean=median=mode
Null hypothesis (H0)
Hypothesis of no difference or relationship
Alternative (H1)
Hypothesis of some difference or relationship
null hypothesis rejected in favor or alt hypothesis means
there is an effect or difference when one exists
Type 1 error
false positive arror
stating that there is an effect when there isnt
α is the probability of making a type I error
Alpha=An innocent man
Type II error
False negative error
stating there isnt an effect when there is
β is the probability of making a type II error and is related to statistical power (1- β)
Beta=blindy let the guilty go
Confidence interval
range of values within which the true mean of the population is expected to fall, with a specified probability
CI for sample mean = mean +/- Z(SE)
95% CI , α=.05, Z=1.96
if CI for a mean difference bteween 2 variables includes 0=null not rejected
if CI for odds ratio or relative risk includes 1 then null not rejected
if the CI between 2 groups do not overlap then there is a statistically significant difference
if the CI between 2 groups overlap then there is no significant difference
t test
checks difference between means of 2 groups
ex) mean bp between men and women
ANOVA
checks difference between means of 3 or more gorups
comparing the mean bp between 3 different ethnic groups
Chi sq
checks the difference between 2 or more percentages or proportions of categorical outcomes (not mean values)
ex) comparing the percentage of members of 3 different ethnic groups who have essential hypertension
Pearson correlation coefficient
r is between -1 and +1. the closer the absolute value of r is to 1 the stronger the linear correlation between the 2 variables
r^2=coefficient of determination. This is the amount of variance in one variable that can be explained by variance in another variable
core principle of ethics : justice
treat persons fairly and quitably
all principles are autonomy, beneficence, non maleficence, justice
car seats
2 yrs old for rear facing
4 yrs old for with harness
booster seat till 8
children <12 should not ride in front
primary dz prevention
prevent dz before it occurs
hpv vaccine
secondary dx prevention
screen early for and manage existing but asymptomatic dz
pap smear
tertiary dz prevention
treatment to reduce complications from dz that is ongoing or has long term effects
quaternary dz prevention
identifying patients at risk of unnecessary treatment, preventing from the harm of new interventions
Exclusive Provider Organization
restricted to limited panel except in emergences
no referrel needed for specialist
Health maintenance organization (HMO)
restricted to limited panel except in emergencies, denies for any service that does not meet establish evidence based quidelines, requires referel from primary care provider
Point of service
Patient can see providers outside the network, higher copays and deductibles for out of network services, requires referral form primary care provider
Preferred provider organization
patient can see providers outside network, higher copays and deductibles for all services, no referral required
bundled payment
healthcare org receives a set amount per service regardless of how much the patient uses the healthcare system
capitation
physician received a set amount per patient assigned to them per period of time, regardless of how much he patient uses the healthcare system
used by some hmo
discounted fee for service
patient pays for each individual service at a discounted rate predetermined by providers and payers
fee for service
patient pays for each individual service
global payment
patient pays for all expenses associated with a single incident of care with single payment
elective surgeries
Medicare
elderly
medical
destitute and poor
A-hospital
B-outpatient
C-Both
D-drugs
hospice care
medicare, medical, and most private insurance companies pay for hospice care if <6months to live
principle of double effect- prioritize positive effects over negative effects of drugs
readmission
considered when <30 days from discharge
Human factors design
forcing functions (those that prevent undesirable actions) are the most effective
standardization and simplification are beneficial
deficient designs hinder workflow and lead to staff workarounds that bypass safety features
PDSA cycle
P-Plan, define problem and situation
D-Do, test new process
S-study, measure and analyze data
A- Act, integrate new process into regular workflow
Quality measures
Structural - physical equipment, resources, facilities (number of diabetes educators)
Process- performance of system as planned (percentage of diabetic patients whose HbA1c was measures int he past 6 months)
Outcome- Impact on patients (average HbA1C of patients with diabetes)
Balancing-impact of other systems/outcomes (incidence of hypoglycemia among patients who tried an intervention to lower HbA1C)
Swiss cheese model
focuses on systems and conditions rather than an individuals error
patient harm can occur despite multiple safeguards when “the holes int he cheese line up”
potential failures in defense strategies
Active error
occurs at level of frontline operator
immediate impact
Latent error
occurs in processes indirect from operator but impacts patient care (different types of IV pumps used within the same hospital)
Root cause analysis
retrospective approach. applied after failure event to prevent recurrence
Failure mode and effects analysis
forward looking approach. Applied before process implementation to prevent failure occurrences
uses inductive reasoning to identify all the ways a process might fail and prioritizes them by their probability of occurrence and impact on
