Flashcards in Behavioral science Deck (82)

1

## Cross-sectional study

### It collects data from a group of people to assess frequency of disease (and related risk factors) at a particular point in time. It asks "what is happening?" It can measure disease prevalence and can show risk factor association with disease, but does not establish causality.

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## Case control study

### It is retrospective. It compares a group of people with a disease to a group without the disease. It looks for prior exposure or risk factor. It asks "what happened?" It can measure odds ration. Eg patients with COPD had higher odds of a history of smoking than those without COPD.

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## Cohort study

### It can either be prospective or retrospective. It compares a group with a given exposure or risk factors to a group without such exposure. It looks to see if exposure increases the likelihood of disease. The prospective study asks "who will develop disease?" and the retrospective study asks "who developed the disease (exposed vs non-exposed)?

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## Twin concordance study

### It compares the frequency with which both monozygotic twins or both dizygotic twins develop the same disease. It measures heritability and influence of environmental factors (nature vs nurture).

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## Adoption study

### It compares siblings raised by biological vs adoptive parents. It measures heritability and influence of environmental factors.

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## Clinical trial

### Experimental study involving humans. It compares therapeutic benefits of 2 or more treatments, or of treatment and placebo. Study quality improves when the study is randomized, controlled and double-blinded. Triple blind refers to the additional blinding of the researchers analyzing the data.

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## Phase I clinical trial

### It has a small number of healthy volunteers. The purpose is to see if it is safe, to asses safety, toxicity, and pharmacokinetics, and pharmacodynamics.

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## Phase II clinical trial

### It has a small number of patients with disease of interest. The purpose is to see does it work, to asses treatment efficacy, optimal dosing, and adverse effects.

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## Phase III clinical trial

### It has a large number of patients randomly assigned either to the treatment under investigation or to the best available treatment (or placebo). The purpose is to see is it as good or better, compares the new treatment to the current standard of care.

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## Phase IV clinical trial

### It is postmarketing surveillance of patients after treatment is approved. The purpose is to see can it stay, detects rare or long-term adverse effects. It can result in treatment being withdrawn from market.

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## Evaluation of diagnostic tests (2x2 table)

### A 2x2 table compares test results with actual presence of disease. Row is disease (+/-) column is test (+/-). Sensitivity and specificity are fixed properties of a test, while PPV and NPV vary depends on disease prevalence.

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## Sensitivity

### It measures the true positive rate, the proportion of all people with disease who test positive, or the probability that a test detects a disease when the disease is present. A value approaching 100% is desirable for ruling out a disease and indicates a low false-negative rate. A high sensitivity is used for screening disease with a low prevalence. Sensitivity=TP/(TP+FN)= 1-false negative rate. SN-N-OUT=highly SeNsitive test, when Negative, rules OUT disease. If sensitivity is 100%, TP/(TP+FP)=1, FP=0, and all negatives must be TNs.

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## Specificity

### It measures the true-negative rate, proportion of all people without disease who test negative, or the probability that a test indicates no disease when disease is absent. A value approaching 100% is desirable for ruling in a disease and indicates a low false-positive rate. A high specificity test is used for confirmation after a positive screening test. Specificity= TN/(TN+FP)=1-false positive rate. SP-P-IN= highly SPecific test, when Positive, rules IN disease. If specificity is 100%, TN/(TN+FP)=1, FP=0, and all positives must be TPs.

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## Positive predictive value (PPV)

### It measures the proportion of positive test results that are true positive, the probability that a person actually has the disease given a positive test result. Positive predictive value=TP(TP+FP). PPV varies directly with prevalence or pretest probability leads to a high PPV.

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## Negative predictive value (NPV)

### It measures a proportion of negative test results that are true negative, the probability that a person actually is disease free given a negative test result. NPV= TN/(TN+FN). NPV varies inversely with prevalence or pretest probability: a high pretest probability leads to a low NPV.

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## Incidence rate

### Incidence rate= # of new cases/ # of people at risk. During a time period. Incidence looks at new cases (incidents).

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## Prevalence

### Prevalence= # of existing cases/ # of people at risk. At point in time. Prevalence looks at all current cases. Prevalence approximates incidence for a short duration disease (eg common cold). Prevalence also approximates the pretest probability.

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## Odds ratio (OR)

### In case control studies, subjects are chosen by outcome, not exposure. The odds ratio gives the likelihood of the subject developing the adverse outcome as compared to the placebo. The odds ratio approximates relative risk only when prevalence is low because disease with exposure+ no disease with exposure is approximately the same as no disease without exposure. OR=(disease with exposure/ disease without exposure)/ (no disease with exposure/ no disease without exposure)= (disease with exposure x no disease without exposure)/ (no disease with exposure x disease without exposure).

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## Relative risk (RR)

### It is typically used in cohort studies. It measure the risk of developing disease in the exposed group divided by risk in the unexposed group (eg if 20% of smokers develop lung cancer vs 1% of nonsmokers, RR= .2/.01=20). If prevalence is low, OR approximates RR. RR= (disease with exposure/(disease with exposure + no disease with exposure))/(disease without exposure/(disease without exposure + no disease without exposure)).

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## Attributable risk (AR)

### Attributable risk is the risk of an outcome attributable to a given exposure and is expressed mathematically as: AR = Incidence in exposed group (%) – Incidence in unexposed group (%). Example: A study examines the association of strokes with smoking cigarettes. In the exposed group (i.e. smokers), there is a 40% rate of strokes. In the non-exposed group (i.e. non-smokers), there is a 20% risk of strokes. The attributable risk is 20%.

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## Relative risk reduction (RRR)

### It measures the proportion of risk reduction attributable to the intervention as compared to a control (eg if 2% of patients who recieve a flu shot develop the flue, while 8% of unvaccinated patients develop the flu, then RR= 2/8=0.25, and RRR=0.75). RRR= 1-relative risk

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## Absolute risk reduction

### It measures the difference in risk (not the proportion) attributable to the intervention as compared to a control (eg if 8% of people who receive a placebo vaccine develop the flu vs 2% of people who receive a flu vaccine, then ARR=8%-2%=6%=.06) ARR=(disease without risk factor/(disease without risk factor + no disease with risk factor))/(disease with risk factor/(disease with risk factor/(disease with risk factor + no disease with risk factor))

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## Number needed to treat (NNT)

### The "number needed to treat" is the number of patients who need to be treated for therapeutic benefit to be observed in one member of the study population. The measurement of number needed to treat allows comparison of efficacy between different treatments. It is often compared to placebo or no treatment groups. NNT = 1/(absolute risk reduction). Example: If the risk of developing lung cancer in Cincinatti is 1.5% and the risk in St. Louis is 0.5%, the number of people who would have to move from Cinncinatti to St. Louis for one person to avoid contracting lung cancer due to the move would be 100 (NNT = 1/ARR = 1/(0.015-0.005)).

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## Number needed to harm (NNH)

### The "number needed to harm" is the number of subjects who must be exposed to a given risk factor for one person to be harmed. NNH = 1/(attributable risk). Example: Say that exposure to a certain level of benzene is shown to increase the risk of leukemia by 0.5%. The number of people who would have to be exposed to benzene for one additional case of leukemia to be demonstrated relative to controls would be 200 (NNH = 1/(attributable risk) = 1/(0.005)).

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## Precision

### The consistency and reproducibility of a test (reliability). The absence of random variation in a test. Random error decreases the precision in a test. An increase in precision leads to a decrease in standard deviation and an increase in statistical power (1-beta, where beta is the probability of making a Type II error)

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## Accuracy

### The trueness of test measurements (validity). The absence of systemic error or bias in a test. Systemic errors lead to decreased accuracy in a test.

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## Selection bias

### An error in assigning subjects to a study group resulting in an unrepresentative sample. Most commonly a sampling bias. Examples include berkson bias, healthy worker effect, non-response bias. Strategy to reduce bias includes randomization and to ensure the choice of the right comparison/reference group.

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## Berkson bias

### A type of selection bias. It occurs when a study population selected from a hospital is less healthy than the general population.

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## Healthy worker effect

### A type of selection bias. It occurs when a study population is healthier than the general population.

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## Non-response bias

### A type of selection bias. It occurs when participating subjects differ from nonrespondents in meaningful ways.

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## Recall bias

### It occurs when an awareness of a disorder alters recall by subjects. It commonly occurs in retrospective studies. eg patients with a disease recall an exposure after learning of similar cases. A strategy to reduce bias includes a decrease in time from exposure to follow up.

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## Measurement bias

### It occurs when information is gathered in a way that distorts it. For example, a miscalibrated scale consistently overstates weights of subjects. It can be reduced with the use of standardized method of data collection.

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## Procedure bias

### It occurs when subjects in different groups are not treated the same. For example, patients in a treatment group spend more time in highly specialized hospital units. It can be reduced with blinding and using placebo to reduce influence of participants and researchers on procedures and interpretation of outcomes as neither are aware of group allocation.

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## Observer-expectancy bias

### It occurs when a researcher's belief in the efficacy of a treatment changes the outcome of that treatment (aka Pygmalion effect; self-fulfilling prophecy). For example, if the observer expects treatment group to show signs of recovery, then he is more likely to document positive outcomes. It can be reduced with blinding and using placebo to reduce influence of participants and researchers on procedures and interpretation of outcomes as neither are aware of group allocation.

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## Confounding bias

### It occurs when a factor is related to both the exposure and outcome, but not on the causal pathway, which distorts or confuses the effect of exposure on outcome. For example, pulmonary disease is more common in coal workers than the general populations; however people who work in coal mines also smoke more frequently than the general population. It can be reduced by having multiple/repeated studies. Crossover studies (subjects act as their own controls) can also reduce the bias. It can also be reduced by matching patients with similar characteristics in both treatment and control groups.

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## Lead-time bias

### It occurs when early detection is confused with an increase in survival. For example, early detection makes it seem as though survival has increased, but the natural history of the disease has not changed. It can be reduced by measuring the back end survival, thereby adujusting survival according to the severity of disease at the at the time of diagnosis.

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## Mean

### sum of values/total number of values. Most affected by outliers (extreme values).

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## Median

### The middle value of a list of data sorted from least to greatest. If there is an even number of values, the median will be the average of the middle two values.

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## Mode

### Most common value. It is least affected by outliers.

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## Standard deviation

### How much variability exists from the median in a set of values. omega=SD; n= sample size

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## +/- 1 standard deviation

### 68%

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## +/- 2 standard deviation

### 95%

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## +/- 3 standard deviation

### 99.7%

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## Standard error of the mean

### An estimate of how much variability exists between the sample mean and the true population mean. SEM=SD/square root of n. SEM decreases as n increases.

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## Normal distribution

### Gaussian, also called bell-shaped. Mean=Mode=Median.

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## Bimodal distribution

### Suggests two different populations (eg metabolic polymorphism such as fast vs slow acetylators; age at onset of hodgkin lymphoma; suicide rate by age).

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## Positive skew

### A shift to the left with a longer tail on the right. Typically, mean is greater than median is greater than mode.

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## Negative skew

### A shift to the right with a longer tail on the left . Typically, mean is less than median is less than mode.

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## Null (H0) hypotheses

### A hypotheses of no difference or relationship (eg there is no association between the disease and the risk factor in the population).

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## Alternative (H1) hypotheses

### A hypotheses of some difference or relationship (eg there is some association between the disease and the risk factor in the population).

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## Type I error (alpha)

### A type I error occurs when the null hypothesis is incorrectly rejected. In other words, the null hypothesis is rejected in favor of an alternative hypothesis that is incorrect. It is also known as a false-positive error. α (alpha) is the probability of making a type I error. Mnemonic: You sαw a difference that didn't actually exist.

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## Type II error (beta)

### A type II error occurs when the null hypothesis is not rejected even though it is false. In other words, the null hypothesis is not rejected when a difference actually does exist. Example: declaring a guilty man innocent. β is the probability of making a type II error. Mnemonic: β measures when you were βlind to the truth

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## Confidence interval

### It is a range of values in which a specified probability of the means of repeated samples would be expected to fall. CI=mean+/-Z(standard error of mean). The 95% CI (corresponding to p=0.05) is often used. For 95% CI, Z=1.96. For 99% CI, Z=2.58. If the 95% CI for a mean difference between 2 variables includes 0, then there is no significant difference and H0 is not rejected. If the 95% CI for odds ratio or relative risk includes 1, H0 is not rejected. If the CIs between 2 groups do not overlap than a statistical significant difference exists. If the CIs between 2 groups overlap then there is usually no significant difference.

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## t-test

### It checks the difference between the means of 2 groups. Tea is MEANt for 2. For example, comparing the mean blood pressure between men and women.

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## ANOVA

### It checks the difference between the means of 3 or more groups. 3 words: ANalysis Of VAriance. For example, comparing the mean blood pressure between members of 3 different ethnic groups.

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## Chi-square

### It checks the difference between 2 or more percentages or proportions of categorical (Chi-tegorical) outcomes (not mean values). For example, comparing the percentage of members of 3 different ethnic groups who have essential hypertension.

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## Pearon correlation coefficient (r)

### r is always between -1 and +1. The closer the absolute value of r is to 1, the stronger the linear correlation between the 2 variables. Positive r value, leads to a positive correlation (as one variable increases, the other variable increases). A negative value of r, leads to a negative correlation (as one variable increases, the other decreases). The coefficient of determination=r squared.

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## Disease prevention

### Primary is Prevention of a disease occurrence (eg HPV vaccination). Secondary is Screening early for a disease (eg pap smear). Tertiary is Treatment to reduce disability from disease (eg chemotherapy). Quaternary is identifying patients at risk of unnecessary treatment, protecting from the harm of new interventions.

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## The four parts of medicare

### Part A is hospital insurance. Part B is basic medical bills. Part C is parts A and B delivered by approved private companies. Part D is prescription drugs

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## Autonomy

### The obligation to respect patients as individuals (truth telling or confidentiality), to create conditions necessary for autonomous choice (informed consent), and to honor their preference in accepting or not accepting medical care.

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## Beneficence

### Physicians have a special ethical (fiduciary) duty to act in the patient's best interest. It may conflict with autonomy (an informed patients has the right to decide) or what is best for society (traditionally patient interest supersedes).

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## Non-maleficence

### No do harm. Must be balanced against beneficence; if the benefits outweigh the risk, a patient may make an informed decision to proceed (most surgeries and medications fall into this category)

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## Justice

### To treat persons fairly and equitably. This does not always imply equally (eg triage).

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## Informed consent

### A process (not just a document/signature) that requires: disclosure (a discussion of pertinent information), understanding (ability to comprehend), capacity (ability to reason and make one's own decisions, which is distinct from competence, a legal determination), voluntariness (freedom from coercion and manipulation). Patients must have an intelligent understanding of their diagnosis and the risks/benefits of a proposed treatment and alternative options, including no treatment. Patient must be informed that he or she can revoke written consent at ay time, even orally.

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## Exceptions to informed consent

### Patients lack decision making capacity or is legally incompetent. Implied consent in an emergency. Therapeutic privilege-withholding information when disclosure would severely harm the patient or undermine informed decision making capacity. Waiver- patient explicitly waives the right of informed consent.

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## Consent for minors

### A minor is generally any person less than 18 years old. Parental consent laws in relation to health care vary by state. In general, parental consent should be obtained unless emergent treatment is required (eg blood transfusion) even if it opposes parental religious/cultural beliefs, or if a minor is legally emancipated (eg is married, is self supporting, or is in the military). Situations in which parental consent is usually not requires include: Sex (contraceptions, STIs, pregnancy), Drugs (addiction), Rock and roll (emergency/trauma). Physicians should always encourage healthy minor guardian communication.

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## Decision-making capacity

### Physician must determine whether the patient is psychologically and legally capable of making a particular health care decision. Components include: Patient is 18 or older or emancipated, makes and communicates a choice, is informed (knows and understands); decision remain stable over time, is consistent with patients values and goals and not clouded by a mood disorder, is not a result of altered mental status (delusion, delirium, hallucinations).

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## Exceptions to patient confidentiality

### Reportable disease (eg STIs, TB, hepatitis, food poisoning), tarasoff decision (informing potential victims from harm), child or elder abuse, impaired automobile drivers (epileptics), suicidal/ homicidal patients.

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## Apgar score

### An assessment of newborn vital signs following labor via a 10 point scale evaluated at 1 minute and 5 minutes. Apgar score is based on Appearance, Pulse, Grimace, Activity, and Respiration (over 7=good; 4-6=assist and stimulate; less then 4=resuscitate). If Apgar score remains less than 4 at later time points, there is an increase risk that the child will develop long-term neurologic damage.

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## Low birth weight

### Defined as less than 2500 g. It is caused by prematurity or intrauterine growth restriction (IGUR). Associated with an increase risk of sudden infant death syndrome (SIDS) and with an increase in overall mortality Other problems include impaired thermoregulation and immune function, hypoglycemia, polycythemia, and impaired neurocognitive/emotional development. Complications include infections, respiratory distress syndrome, necrotizing enterocolitis, intraventricular hemorrhage, and persistent fetal circulation.

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## 0-12 moths milestones

### Primitive reflexes disappear- Moro (by 3 months), rooting (by 4 months), palmer (by 6 months), Babinski (by 12 months). Posture- lifts head up prone (by 1 month), rolls and sits (by 6 months), crawls (by 8th months), stands (by 10 months), walks (by 12-18 months). Passes toys hand to hand by 6 months, pincer grasp by 10 months. Points to objects by 12 months. Social smile by 2 months, stranger anxiety by 6 months, separation anxiety by 9 months. First voice by 4 months, object permanence by 9 months, and says mama and dada by 10 months.

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## Toddler (12-36 months) milestones

### Takes first steps by 12 months, climbs stairs by 18 months, stacks cubes (number=year*3), feeds self with fork and spoon by 20 months, kicks ball by 24 months. Recreational parallel play by 24-36 months, moves away from and returns to mother by 24 months, realization of core gender identity by 36 months, 200 words by age 2, 2-word sentences.

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## Preschool (3-5 years)

### Tricycle by 3 years, copies lines or circle or stick figure by 4 year, hops on one foot by 4 years, uses buttons or zippers or grooms self by 5 years. Comfortably spends part of day away from mother by 3 years. they play cooperative and have imaginary friends by 4 year. Has 1000 words by age 3, uses complete sentences and prepositions by 4 years. They can tell detailed stories by 4 years.

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## Changes in the elderly

### Sexual changes include slower erection/ ejaculation, longer refractory period in men and vaginal shortening, thinning, and dryness in women. Sleep patterns changes include a decrease in REM and slow wave sleep; an increase in sleep onset latency and an increase in early awakenings. An increase in suicide rate. A decrease in vision, hearing, immune response, and bladder control. A decrease in renal, pulmonary, GI function. A decrease in muscle mass and an increase in fat. Sexual interest does not decrease. Intelligence does not decrease.

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## Presbycusis

### A sensorineural hearing loss (often of higher frequencies) due to destruction of hair cells at the cochlear base (preserved low-frequency hearing at apex).

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## Most common causes of death in the US in those under 1 year

### 1. congenital malformations. 2. preterm birth. 3. SIDS

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## Most common causes of death in the US in those between 1-14 years

### 1. Unintentional injury. 2. Cancer. 3. Congenital malformations.

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## Most common causes of death in the US in those between 15-34 years

### 1. Unintentional injury. 2. Suicide. 3. Homicide

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## Most common causes of death in the US in those between 35-44 years

### 1. Unintentional injury. 2. Cancer. 3. Heart disease.

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## Most common causes of death in the US in those between 44-64 years

### 1. Cancer. 2. Heart disease. 3. Unintentional injury.

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## Most common causes of death in the US in those over the age of 65 years

### 1. Heart disease. 2. Cancer. 3. Chronic respiratory disease.

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